diff --git a/.agent-plan.md b/.agent-plan.md
index cd9459a..6efb1c9 100644
--- a/.agent-plan.md
+++ b/.agent-plan.md
@@ -6,7 +6,7 @@
 
 ## Current System State
 
-**v0.5.0 in progress — Milestones 7–11 complete, v5 dataset shipped + canonical validation module.** Full simulation engine + render/bundle + exposure filtering + CLI commands + validation harness implemented. v4 engine changes merged (PR #21). v5 dataset regenerated with boosted leakage trap (snapshot day 10, Poisson(1) target-correlated boost) and validated via canonical sklearn pipeline (all checks pass). Canonical validation module added as single source of truth (`leadforge/validation/lead_scoring.py`).
+**v0.5.0 in progress — Milestones 7–11 complete, v6 dataset shipped.** Full simulation engine + render/bundle + exposure filtering + CLI commands + validation harness implemented. v6 dataset adds: latent-aware touch intensity (`LatentDecayIntensity`), causally-grounded leakage trap (post-snapshot touches), student/instructor split, `touches_last_7_days` momentum feature, `acquisition_wave` cohort feature, soft ACV winsorization, GBM improvement validation.
 
 ---
 
@@ -85,6 +85,41 @@ No engine changes required — v5 is a build pipeline + validation improvement.
 - [x] `tests/scripts/test_build_v5_snapshot.py` — imports from `leadforge.pipelines.build_v5` directly (no more `importlib` hack)
 - [x] All 705 tests pass; lint clean
 
+### v6: Causally-grounded dataset with student/instructor split (PR #31)
+
+Engine changes:
+- [x] `leadforge/mechanisms/counts.py` — `LatentDecayIntensity` mechanism: Poisson intensity with recency decay + latent-trait modulation (causal link: latent → touches AND latent → conversion)
+- [x] `leadforge/mechanisms/policies.py` — `_TOUCH_LATENT_WEIGHTS` per motif family; `assign_mechanisms(latent_touch_intensity=True)` flag
+- [x] `leadforge/simulation/engine.py` — `simulate_world(latent_touch_intensity=True)` passthrough
+- [x] `leadforge/api/generator.py` — `generate(latent_touch_intensity=True)` via kwargs
+- [x] `leadforge/render/snapshots.py` — `touches_last_7_days` feature added to snapshot builder
+- [x] `leadforge/schema/features.py` — `touches_last_7_days` FeatureSpec added
+
+Build pipeline:
+- [x] `leadforge/pipelines/build_v6.py` — all pipeline functions: `derive_features`, `softcap_expected_acv`, `assign_acquisition_wave`, `compute_post_snapshot_touches`, `rename_and_select`, `subsample`, `inject_missingness`
+- [x] `scripts/build_v6_snapshot.py` — CLI: generates both student + instructor CSVs
+- [x] `scripts/validate_v6_dataset.py` — validates both exports: basic checks, determinism, baseline AUC, tree improvement, value-aware ranking, trap delta (10 seeds), cohort split
+- [x] `scripts/quick_baseline_eval_v6.py` — LR + RF + GBM baselines, value-aware ranking, feature importance, trap detection
+
+Datasets:
+- [x] `lead_scoring_intro/lead_scoring_intro_v6.csv` — 1000 rows × 20 cols (student-safe, no leakage)
+- [x] `lead_scoring_intro/lead_scoring_intro_v6_instructor.csv` — 1000 rows × 21 cols (+ `__leakage__touches_post_snapshot_15_90`)
+
+Validation results:
+- [x] Baseline AUC: 0.627 (within [0.62, 0.90])
+- [x] GBM improvement: +0.022 over LR (5-seed average)
+- [x] Trap delta: mean 0.046, min 0.034 (both above thresholds)
+- [x] Value-aware uplift: +17.6% at K=25, +41.3% at K=50
+- [x] All mandatory checks pass
+
+Documentation + CI:
+- [x] `lead_scoring_intro/RELEASE_v6.md` — column dictionary, missingness patterns, metrics, teaching guidance (4 lectures)
+- [x] `lead_scoring_intro/BACKGROUND_v6.md` — ProcureFlow business context for students
+- [x] `.github/workflows/ci.yml` — `validate-dataset-v6` job added
+- [x] `tests/scripts/test_build_v6_snapshot.py` — 26 tests for pipeline functions
+- [x] `tests/mechanisms/test_mechanisms.py` — 6 tests for `LatentDecayIntensity`
+- [x] All 737 tests pass; lint + format clean
+
 ---
 
 ## Deferred Items
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 5f89201..56f363e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -68,7 +68,7 @@ jobs:
           python-version: "3.12"
       - run: pip install -e ".[dev,scripts]"
       - name: Check for v5 dataset
-        id: check
+        id: check-v5
         run: |
           if [ -f "lead_scoring_intro_v5.csv" ]; then
             echo "found=true" >> "$GITHUB_OUTPUT"
@@ -76,9 +76,37 @@ jobs:
           else
             echo "found=false" >> "$GITHUB_OUTPUT"
           fi
-      - name: Run validator
-        if: steps.check.outputs.found == 'true'
-        run: python scripts/validate_lead_scoring_dataset.py --csv "${{ steps.check.outputs.csv }}" --enforce-1000
-      - name: Skip (no dataset)
-        if: steps.check.outputs.found != 'true'
-        run: echo "No lead_scoring_intro_v5.csv found in repo root — skipping validation"
+      - name: Run v5 validator
+        if: steps.check-v5.outputs.found == 'true'
+        run: python scripts/validate_lead_scoring_dataset.py --csv "${{ steps.check-v5.outputs.csv }}" --enforce-1000
+      - name: Skip v5 (no dataset)
+        if: steps.check-v5.outputs.found != 'true'
+        run: echo "No lead_scoring_intro_v5.csv found in repo root — skipping v5 validation"
+
+  validate-dataset-v6:
+    name: Validate v6 lead scoring dataset
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+      - run: pip install -e ".[dev,scripts]"
+      - name: Check for v6 datasets
+        id: check-v6
+        run: |
+          STUDENT="lead_scoring_intro/lead_scoring_intro_v6.csv"
+          INSTRUCTOR="lead_scoring_intro/lead_scoring_intro_v6_instructor.csv"
+          if [ -f "$STUDENT" ] && [ -f "$INSTRUCTOR" ]; then
+            echo "found=true" >> "$GITHUB_OUTPUT"
+            echo "student=$STUDENT" >> "$GITHUB_OUTPUT"
+            echo "instructor=$INSTRUCTOR" >> "$GITHUB_OUTPUT"
+          else
+            echo "found=false" >> "$GITHUB_OUTPUT"
+          fi
+      - name: Run v6 validator
+        if: steps.check-v6.outputs.found == 'true'
+        run: python scripts/validate_v6_dataset.py "${{ steps.check-v6.outputs.student }}" "${{ steps.check-v6.outputs.instructor }}"
+      - name: Skip v6 (no dataset)
+        if: steps.check-v6.outputs.found != 'true'
+        run: echo "No v6 datasets found — skipping v6 validation"
diff --git a/lead_scoring_intro/BACKGROUND_v6.md b/lead_scoring_intro/BACKGROUND_v6.md
new file mode 100644
index 0000000..4de8830
--- /dev/null
+++ b/lead_scoring_intro/BACKGROUND_v6.md
@@ -0,0 +1,62 @@
+# BACKGROUND v6 — Lead Scoring Intro Dataset
+
+## Business context
+
+You are a data scientist at **ProcureFlow**, a mid-market B2B SaaS company selling AP automation and procurement workflow software. ProcureFlow targets companies with 200–2,000+ employees in manufacturing, logistics, healthcare, financial services, and professional services.
+
+The sales team generates leads through three channels:
+- **Inbound marketing**: content downloads, webinars, website forms
+- **SDR outbound**: cold outreach by sales development representatives
+- **Partner referral**: introductions through consulting and technology partners
+
+### The lead scoring problem
+
+The sales team can only actively work a limited number of leads per quarter. Your job is to build a **lead scoring model** that predicts which leads are most likely to convert to paying customers within 90 days of entering the pipeline.
+
+A good lead score helps sales prioritize their time — contacting high-probability leads first and deprioritizing unlikely conversions.
+
+## Dataset description
+
+The dataset contains **1,000 leads** observed at **day 14** of their lifecycle (the "snapshot day"). All features are computed from activity that occurred during the first 14 days. The target variable (`converted`) indicates whether the lead converted to a paying customer within 90 days.
+
+### Deal sizes
+
+ProcureFlow's annual contract value (ACV) ranges from **$18,000** (starter plan, small companies) to **$120,000** (enterprise plan, large companies). The `expected_acv` column provides an estimate of the deal size for each lead based on company size and any existing opportunity data.
+
+This variation in deal size means that not all conversions are equally valuable — a model that identifies high-value conversions may be more useful than one that maximizes the number of conversions.
+
+### Acquisition waves
+
+Leads enter the pipeline in three cohorts (`acquisition_wave`): A (earliest), B (middle), C (most recent). These roughly correspond to different time periods. The market conditions and lead mix may vary across cohorts, which is relevant for thinking about how models perform on future data.
+
+## What to expect
+
+- **Base conversion rate**: ~30%
+- **Baseline AUC**: A simple logistic regression achieves ~0.63 AUC
+- **Missingness**: 5 columns have missing values (2–7% each) due to different data collection processes across lead sources
+- **Feature interactions**: The relationship between engagement and conversion is nonlinear — tree-based models capture this better than linear models
+
+## Key columns
+
+| Column | What it measures |
+|---|---|
+| `industry` | Business sector |
+| `region` | Geography |
+| `company_size` | Employee headcount band |
+| `company_revenue` | Revenue band |
+| `contact_role` | Job function of primary contact |
+| `seniority` | Job level |
+| `lead_source` | How the lead was acquired |
+| `opportunity_created` | Whether sales opened an opportunity |
+| `demo_completed` | Whether the lead viewed demo content |
+| `expected_acv` | Estimated deal size (USD) |
+| `inbound_touches` | Marketing touches received |
+| `outbound_touches` | Sales touches initiated |
+| `touches_week_1` | Touches in first 7 days |
+| `touches_last_7_days` | Touches in days 8–14 (recent momentum) |
+| `days_since_first_touch` | Time since first engagement |
+| `web_sessions` | Website visits |
+| `sales_activities` | Sales rep logged activities |
+| `days_since_last_touch` | Recency of last engagement |
+| `acquisition_wave` | Cohort (A, B, or C) |
+| `converted` | **Target**: 1 = converted within 90 days |
diff --git a/lead_scoring_intro/RELEASE_v6.md b/lead_scoring_intro/RELEASE_v6.md
new file mode 100644
index 0000000..6c63346
--- /dev/null
+++ b/lead_scoring_intro/RELEASE_v6.md
@@ -0,0 +1,188 @@
+# RELEASE v6 — Lead Scoring Intro Dataset
+
+## Overview
+
+v6 is the sixth iteration of the lead scoring intro dataset, designed for **3–4 lectures** on applied ML for lead scoring. It introduces causally-grounded leakage detection, value-aware ranking, nonlinear interaction structure, and cohort-based evaluation.
+
+### Key changes from v5
+
+| Change | v5 | v6 |
+|---|---|---|
+| Snapshot day | 10 | **14** |
+| Leakage trap | Label-noise boost (`total_touches_90d`) | **Causal** post-snapshot touches (days 15–90) |
+| Student/instructor split | Single file | **Two files**: student-safe + instructor |
+| Momentum features | `touches_week_1`, `days_since_first_touch` | + **`touches_last_7_days`** |
+| Cohort feature | — | **`acquisition_wave`** (A/B/C) |
+| Value column | `expected_acv` (hard clip) | `expected_acv` (soft winsorize) |
+| Tree improvement | Not validated | **Validated**: GBM > LR |
+| MCAR injection | `days_since_last_touch` | + `expected_acv` (2%) |
+
+---
+
+## Snapshot definition
+
+- **Snapshot day**: 14 (features computed from events on days 0–14 after lead creation)
+- **Horizon**: 90 days (label derived from events through day 90)
+- **Target**: `converted` — 1 if a `closed_won` event occurred within 90 days, 0 otherwise
+- **Rows**: 1,000 (stratified subsample at 30% conversion rate)
+
+---
+
+## Column dictionary
+
+### Categorical features (8)
+
+| Column | Type | Description |
+|---|---|---|
+| `industry` | string | Industry vertical of the buying organization |
+| `region` | string | Geographic region (US, UK, EU, APAC) |
+| `company_size` | string | Employee headcount band (200–499, 500–999, etc.) |
+| `company_revenue` | string | Estimated annual revenue band |
+| `contact_role` | string | Functional area of primary contact |
+| `seniority` | string | Seniority level (IC, manager, director, VP) |
+| `lead_source` | string | Origination channel (inbound_marketing, sdr_outbound, partner_referral) |
+| `acquisition_wave` | string | Cohort label (A, B, C) — roughly chronological |
+
+### Binary features (2)
+
+| Column | Type | Description |
+|---|---|---|
+| `opportunity_created` | int (0/1) | Whether an opportunity existed by snapshot day |
+| `demo_completed` | int (0/1) | Whether demo page was viewed (proxy for demo) |
+
+### Numeric features (9)
+
+| Column | Type | Description | Missingness |
+|---|---|---|---|
+| `expected_acv` | float | Expected ACV in USD ($18k–$120k) | 2% MCAR |
+| `inbound_touches` | int | Inbound marketing touches (days 0–14) | — |
+| `outbound_touches` | int | Outbound sales touches (days 0–14) | — |
+| `touches_week_1` | int | Touches in first 7 days | — |
+| `touches_last_7_days` | int | Touches in last 7 days of snapshot window (days 8–14) | — |
+| `days_since_first_touch` | float | Days from first touch to snapshot cutoff | Structural (no touches) + 2% MCAR |
+| `web_sessions` | int | Web sessions within snapshot window | MAR by lead_source |
+| `sales_activities` | int | Sales rep activities within snapshot window | — |
+| `days_since_last_touch` | float | Days since last touch to snapshot cutoff | Structural (no touches) + 3% MCAR |
+
+### Target (1)
+
+| Column | Type | Description |
+|---|---|---|
+| `converted` | int (0/1) | 1 if closed_won within 90 days, 0 otherwise |
+
+### Instructor-only leakage trap (1)
+
+| Column | Type | Description |
+|---|---|---|
+| `__leakage__touches_post_snapshot_15_90` | int | Touch count in days 15–90 (post-snapshot) |
+
+---
+
+## Missingness patterns
+
+| Pattern | Column(s) | Type | Rate |
+|---|---|---|---|
+| Structural | `days_since_last_touch` | No touches → NaN | ~2% |
+| Structural | `days_since_first_touch` | No touches → NaN | ~1% |
+| MAR | `web_sessions` | SDR outbound: 15%, inbound: 2%, partner: 5% | ~7% overall |
+| MAR | `seniority` | Partner referral: 8%, others: 1% | ~2% overall |
+| MCAR | `expected_acv` | Uniform 2% | ~2% |
+| MCAR | `days_since_last_touch` | Additional 3% on top of structural | ~3% |
+| MCAR | `days_since_first_touch` | Additional 2% on top of structural | ~2% |
+
+Total missingness: ~192 values across 5 columns (~19 values per 1000 rows per column on average).
+
+---
+
+## Baseline metrics
+
+Evaluated on a 70/30 stratified hold-out split (seed 42).
+
+### Logistic Regression baseline
+
+| Metric | Value |
+|---|---|
+| ROC-AUC | 0.627 |
+| PR-AUC | 0.405 |
+| Base rate | 30.0% |
+| Precision@25 | 0.480 (Lift: 1.60x) |
+| Precision@50 | 0.420 (Lift: 1.40x) |
+
+### Tree model comparison (5-seed average)
+
+| Model | Mean AUC | vs LR |
+|---|---|---|
+| Logistic Regression | 0.658 | — |
+| GBM (100 trees) | 0.680 | +0.022 |
+
+GBM reliably outperforms LR due to nonlinear interactions in the DGP (latent trait interactions with engagement patterns).
+
+### Value-aware ranking
+
+| K | By P(convert) | By expected value | Uplift |
+|---|---|---|---|
+| 25 | $884,208 | $1,039,434 | +17.6% |
+| 50 | $1,379,208 | $1,949,380 | +41.3% |
+
+### Leakage trap evaluation (instructor dataset)
+
+| Metric | Value |
+|---|---|
+| Column | `__leakage__touches_post_snapshot_15_90` |
+| Seeds | 10 (42–51) |
+| Mean AUC delta | 0.0458 |
+| Min AUC delta | 0.0343 |
+| Max AUC delta | 0.0599 |
+
+The trap is **causally grounded**: post-snapshot touches are higher for leads with higher latent intent/fit (the same traits that drive conversion). No label-noise injection is used.
+
+---
+
+## Teaching guidance
+
+### Lecture 1: Pipeline + Evaluation
+
+**Goal**: Students build their first ML pipeline and learn proper evaluation.
+
+- Load `lead_scoring_intro_v6.csv`
+- Handle missing values (5 columns have NaN — discuss structural vs MCAR)
+- Build a baseline logistic regression with train/test split
+- Evaluate: AUC, PR-AUC, confusion matrix
+- Discuss class imbalance (30% positive rate)
+
+### Lecture 2: Top-K + Expected Value Ranking
+
+**Goal**: Students learn decision-oriented evaluation.
+
+- Precision@K and Lift@K: "If sales can contact 50 leads, how many convert?"
+- Expected value ranking: `P(convert) * expected_acv`
+- Demonstrate that EV ranking captures 17–41% more ACV than probability ranking
+- Discuss when value-aware scoring matters (heterogeneous deal sizes)
+
+### Lecture 3: Feature Engineering + Error Slicing
+
+**Goal**: Students learn to improve models through feature understanding.
+
+- Examine `touches_last_7_days` (momentum) vs `touches_week_1` (early signal)
+- Error analysis by `region`, `company_size`, `lead_source`
+- Missing value patterns: why is `web_sessions` missing more for SDR outbound?
+- Feature interactions: `opportunity_created` x `touches_last_7_days`
+
+### Lecture 4: Trees/GBM + Nonlinearity (+ optional cohort shift)
+
+**Goal**: Students see why tree models outperform linear models.
+
+- Train GBM, compare AUC vs LR (+0.02 on average)
+- Feature importance from RF/GBM
+- Discuss nonlinear interactions captured by trees
+- **Optional**: use `acquisition_wave` for cohort split (train A/B, test C)
+  - Demonstrates distribution shift and evaluation realism
+  - Random split AUC: 0.633, Cohort split AUC: 0.637 (small difference here, but teaches the concept)
+
+### Instructor note: Leakage detection exercise
+
+Use `lead_scoring_intro_v6_instructor.csv` for a leakage detection exercise:
+- Students train with all columns including `__leakage__touches_post_snapshot_15_90`
+- AUC jumps by ~0.046 on average
+- Challenge: identify which column is leaking and explain why
+- The trap is causally grounded (future engagement correlates with conversion via shared latent traits), making it a realistic example of temporal leakage
diff --git a/lead_scoring_intro/lead_scoring_intro_v6.csv b/lead_scoring_intro/lead_scoring_intro_v6.csv
new file mode 100644
index 0000000..6f7b0d0
--- /dev/null
+++ b/lead_scoring_intro/lead_scoring_intro_v6.csv
@@ -0,0 +1,1001 @@
+industry,region,company_size,company_revenue,contact_role,seniority,lead_source,opportunity_created,demo_completed,expected_acv,inbound_touches,outbound_touches,touches_week_1,touches_last_7_days,days_since_first_touch,web_sessions,sales_activities,days_since_last_touch,acquisition_wave,converted
+healthcare_non_clinical,US,200-499,$50M-$200M,procurement_manager,individual_contributor,inbound_marketing,1,0,85000.0,16,0,9,7,13.0,3,2,3.0,C,1
+professional_services,US,1000-1999,$50M-$200M,procurement_manager,c_suite,sdr_outbound,0,0,85000.0,0,9,4,5,13.0,2,0,0.0,B,0
+logistics,UK,1000-1999,$1M-$10M,vp_finance,c_suite,inbound_marketing,0,0,25000.0,8,0,5,3,14.0,1,2,0.0,A,0
+professional_services,UK,500-999,$10M-$50M,vp_finance,manager,sdr_outbound,1,0,55000.0,0,18,13,5,14.0,,2,1.0,C,0
+manufacturing,US,1000-1999,$200M+,it_director,director,inbound_marketing,0,0,115669.0,14,0,9,5,14.0,2,0,0.0,A,0
+professional_services,US,200-499,$200M+,vp_finance,c_suite,sdr_outbound,0,0,119006.0,0,11,6,5,14.0,3,0,0.0,A,0
+manufacturing,US,2000+,$50M-$200M,it_director,individual_contributor,inbound_marketing,0,0,85000.0,3,0,3,0,14.0,1,0,12.0,B,0
+logistics,UK,200-499,$1M-$10M,ap_manager,manager,inbound_marketing,0,0,25000.0,15,0,8,7,13.0,3,1,1.0,C,0
+manufacturing,US,200-499,$10M-$50M,procurement_manager,manager,sdr_outbound,0,0,55000.0,0,14,9,5,14.0,3,2,,A,1
+professional_services,US,200-499,$10M-$50M,ap_manager,c_suite,partner_referral,0,0,55000.0,0,10,5,5,11.0,2,2,1.0,A,0
+professional_services,UK,200-499,$1M-$10M,ap_manager,manager,inbound_marketing,1,0,25000.0,15,0,10,5,14.0,2,1,0.0,C,0
+professional_services,US,2000+,$10M-$50M,procurement_manager,individual_contributor,sdr_outbound,1,0,55000.0,0,14,8,6,13.0,6,1,2.0,C,0
+manufacturing,US,500-999,$1M-$10M,vp_finance,vp,inbound_marketing,1,0,25000.0,9,0,8,1,14.0,2,0,6.0,A,0
+logistics,UK,200-499,$200M+,vp_finance,individual_contributor,sdr_outbound,1,0,118428.0,0,18,11,7,13.0,2,1,2.0,A,1
+logistics,US,500-999,$50M-$200M,procurement_manager,vp,sdr_outbound,1,0,85000.0,0,16,12,4,14.0,3,4,0.0,C,0
+healthcare_non_clinical,UK,500-999,$200M+,it_director,individual_contributor,inbound_marketing,1,0,118415.0,13,0,8,5,14.0,1,0,0.0,B,0
+professional_services,UK,200-499,$50M-$200M,ap_manager,c_suite,sdr_outbound,1,0,85000.0,0,11,7,4,14.0,2,4,2.0,A,1
+healthcare_non_clinical,US,2000+,$50M-$200M,ap_manager,c_suite,sdr_outbound,0,0,85000.0,0,10,3,7,14.0,3,0,1.0,B,1
+logistics,UK,500-999,$10M-$50M,procurement_manager,director,sdr_outbound,1,0,32699.0,0,12,9,3,13.0,,3,3.0,C,0
+professional_services,US,2000+,$50M-$200M,vp_finance,,inbound_marketing,0,0,85000.0,17,0,11,6,14.0,3,0,0.0,B,0
+manufacturing,US,500-999,$10M-$50M,procurement_manager,,partner_referral,1,1,76675.0,0,17,7,10,12.0,2,3,0.0,A,0
+logistics,US,1000-1999,$1M-$10M,vp_finance,individual_contributor,sdr_outbound,0,0,25000.0,0,7,3,4,13.0,,0,0.0,A,0
+logistics,US,500-999,$1M-$10M,it_director,director,sdr_outbound,0,0,25000.0,0,21,8,13,14.0,6,1,0.0,B,0
+logistics,US,2000+,$10M-$50M,vp_finance,c_suite,inbound_marketing,1,0,55000.0,27,0,16,11,14.0,1,1,0.0,B,1
+healthcare_non_clinical,UK,200-499,$1M-$10M,ap_manager,vp,inbound_marketing,1,0,25000.0,23,0,14,9,14.0,2,3,0.0,A,1
+healthcare_non_clinical,UK,500-999,$50M-$200M,it_director,individual_contributor,inbound_marketing,0,0,85000.0,16,0,8,8,14.0,1,1,2.0,B,0
+logistics,US,2000+,$50M-$200M,ap_manager,individual_contributor,inbound_marketing,1,0,85000.0,15,0,11,4,14.0,4,2,0.0,C,1
+logistics,UK,1000-1999,$10M-$50M,procurement_manager,,inbound_marketing,0,0,55000.0,16,0,11,5,14.0,2,0,0.0,C,0
+professional_services,UK,500-999,$10M-$50M,vp_finance,manager,inbound_marketing,1,0,55000.0,17,0,8,9,13.0,6,2,0.0,C,0
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diff --git a/lead_scoring_intro/lead_scoring_intro_v6_instructor.csv b/lead_scoring_intro/lead_scoring_intro_v6_instructor.csv
new file mode 100644
index 0000000..4e831ae
--- /dev/null
+++ b/lead_scoring_intro/lead_scoring_intro_v6_instructor.csv
@@ -0,0 +1,1001 @@
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+healthcare_non_clinical,UK,1000-1999,$50M-$200M,vp_finance,manager,sdr_outbound,0,0,85000.0,0,7,7,0,14.0,2,0,10.0,B,0,0
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+manufacturing,US,1000-1999,$200M+,ap_manager,manager,inbound_marketing,1,0,116247.0,11,0,6,5,13.0,5,6,1.0,C,0,1
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+logistics,UK,1000-1999,$50M-$200M,procurement_manager,vp,inbound_marketing,1,1,85000.0,22,0,15,7,13.0,5,2,,A,1,31
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+manufacturing,UK,500-999,$10M-$50M,it_director,manager,sdr_outbound,1,0,55000.0,0,10,6,4,,4,1,,A,0,15
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+healthcare_non_clinical,US,200-499,$50M-$200M,it_director,manager,inbound_marketing,0,0,85000.0,0,0,0,0,,0,0,,A,0,0
+healthcare_non_clinical,US,200-499,$10M-$50M,ap_manager,manager,partner_referral,1,0,20869.0,0,10,5,5,14.0,4,3,0.0,A,0,27
diff --git a/leadforge/api/generator.py b/leadforge/api/generator.py
index a002bc1..e30bb99 100644
--- a/leadforge/api/generator.py
+++ b/leadforge/api/generator.py
@@ -187,7 +187,10 @@ def generate(
             world_graph,
             category_latent_correlations=category_latent_correlations,
         )
-        result = simulate_world(config, population, world_graph)
+        latent_touch_intensity = kwargs.pop("latent_touch_intensity", False)
+        result = simulate_world(
+            config, population, world_graph, latent_touch_intensity=latent_touch_intensity
+        )
 
         spec = WorldSpec(config=config, narrative=narrative)
         return WorldBundle(
diff --git a/leadforge/mechanisms/counts.py b/leadforge/mechanisms/counts.py
index eb80fb0..d52ace4 100644
--- a/leadforge/mechanisms/counts.py
+++ b/leadforge/mechanisms/counts.py
@@ -128,3 +128,87 @@ def to_dict(self) -> dict[str, Any]:
             "decay_factor": self._decay,
             "floor_rate": self._floor,
         }
+
+
+class LatentDecayIntensity(Mechanism):
+    """Poisson intensity that decays with time AND is modulated by latent traits.
+
+    Combines the recency-decay model with a latent-score multiplier so that
+    leads with higher intent/fit have higher touch intensity throughout the
+    simulation.  This creates a causal pathway: latent → touches AND
+    latent → conversion, making post-snapshot touch counts a meaningful but
+    imperfect proxy for conversion propensity.
+
+    Expected count per day::
+
+        lambda = max(floor, base_rate * decay^t * (1 + boost * latent_multiplier))
+
+    where ``latent_multiplier = sum(weight_i * latents[key_i])``.
+
+    Args:
+        base_rate: Expected daily count at ``t=0`` for a lead with zero
+            latent scores.
+        decay_factor: Per-day multiplicative decay in (0, 1].
+        floor_rate: Minimum daily rate (applied after decay and boost).
+        latent_weights: Mapping of latent-key → weight for the multiplier.
+        boost: Scaling factor for the latent multiplier (controls how much
+            latent traits amplify touch intensity).
+    """
+
+    def __init__(
+        self,
+        base_rate: float,
+        decay_factor: float = 0.97,
+        floor_rate: float = 0.01,
+        latent_weights: dict[str, float] | None = None,
+        boost: float = 0.8,
+    ) -> None:
+        if base_rate <= 0:
+            raise ValueError(f"base_rate must be positive, got {base_rate}")
+        if not (0.0 < decay_factor <= 1.0):
+            raise ValueError(f"decay_factor must be in (0, 1], got {decay_factor}")
+        if floor_rate < 0:
+            raise ValueError(f"floor_rate must be non-negative, got {floor_rate}")
+        self._base_rate = base_rate
+        self._decay = decay_factor
+        self._floor = floor_rate
+        self._latent_weights: dict[str, float] = dict(latent_weights) if latent_weights else {}
+        self._boost = boost
+
+    @property
+    def name(self) -> str:
+        return "latent_decay_intensity"
+
+    def expected_count(self, t: int, latents: dict[str, float] | None = None) -> float:
+        """Return the expected daily count at day *t* given *latents*."""
+        latent_mult = 0.0
+        if latents and self._latent_weights:
+            latent_mult = sum(
+                self._latent_weights.get(k, 0.0) * latents.get(k, 0.0) for k in self._latent_weights
+            )
+        rate = self._base_rate * (self._decay**t) * (1.0 + self._boost * latent_mult)
+        return max(self._floor, rate)
+
+    def sample(self, context: MechanismContext, rng: random.Random) -> int:
+        lam = self.expected_count(context.t, context.latents)
+        count = 0
+        p = math.exp(-lam)
+        cum = p
+        u = rng.random()
+        while u > cum:
+            count += 1
+            p *= lam / count
+            cum += p
+            if count > 1000:
+                break
+        return count
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "base_rate": self._base_rate,
+            "decay_factor": self._decay,
+            "floor_rate": self._floor,
+            "latent_weights": self._latent_weights,
+            "boost": self._boost,
+        }
diff --git a/leadforge/mechanisms/policies.py b/leadforge/mechanisms/policies.py
index e3dec7a..36c10da 100644
--- a/leadforge/mechanisms/policies.py
+++ b/leadforge/mechanisms/policies.py
@@ -27,7 +27,7 @@
 from typing import Any
 
 from leadforge.mechanisms.base import MechanismAssignment
-from leadforge.mechanisms.counts import RecencyDecayIntensity
+from leadforge.mechanisms.counts import LatentDecayIntensity, RecencyDecayIntensity
 from leadforge.mechanisms.hazards import ConversionHazard
 from leadforge.mechanisms.measurement import NoisyProxy
 from leadforge.mechanisms.scores import LatentScore
@@ -119,6 +119,41 @@
     "buying_committee_friction": 0.30,
 }
 
+# Latent weights for touch intensity (LatentDecayIntensity) per motif family.
+# These make touch emission depend on the same latent traits that drive
+# conversion, creating a causal link: latent → touches AND latent → conversion.
+_TOUCH_LATENT_WEIGHTS: dict[str, dict[str, float]] = {
+    "fit_dominant": {
+        "latent_account_fit": 1.5,
+        "latent_engagement_propensity": 1.0,
+        "latent_problem_awareness": 0.5,
+    },
+    "intent_dominant": {
+        "latent_engagement_propensity": 2.0,
+        "latent_problem_awareness": 1.0,
+        "latent_account_fit": 0.5,
+    },
+    "sales_execution_sensitive": {
+        "latent_engagement_propensity": 1.5,
+        "latent_responsiveness": 1.0,
+        "latent_account_fit": 0.5,
+    },
+    "demo_trial_mediated": {
+        "latent_engagement_propensity": 2.0,
+        "latent_problem_awareness": 1.0,
+        "latent_account_fit": 0.5,
+    },
+    "buying_committee_friction": {
+        "latent_contact_authority": 1.0,
+        "latent_engagement_propensity": 1.0,
+        "latent_account_fit": 0.5,
+    },
+}
+_DEFAULT_TOUCH_LATENT_WEIGHTS: dict[str, float] = {
+    "latent_engagement_propensity": 1.0,
+    "latent_account_fit": 0.5,
+}
+
 # Fallback weights/params for unknown motif families.
 _DEFAULT_CONVERSION_WEIGHTS: dict[str, float] = {
     "latent_account_fit": 1.0,
@@ -137,6 +172,8 @@
 def assign_mechanisms(
     motif_family: str,
     rng: random.Random,
+    *,
+    latent_touch_intensity: bool = False,
 ) -> MechanismAssignment:
     """Build a :class:`~leadforge.mechanisms.base.MechanismAssignment` for *motif_family*.
 
@@ -149,6 +186,11 @@ def assign_mechanisms(
         rng: Seeded :class:`random.Random` instance for any stochastic
             parameter perturbation (currently unused but reserved for future
             use so the signature is stable).
+        latent_touch_intensity: When ``True``, use
+            :class:`~leadforge.mechanisms.counts.LatentDecayIntensity` instead
+            of :class:`~leadforge.mechanisms.counts.RecencyDecayIntensity` for
+            touch emission, making touch intensity depend on the same latent
+            traits that drive conversion.
 
     Returns:
         A fully populated :class:`~leadforge.mechanisms.base.MechanismAssignment`.
@@ -171,11 +213,22 @@ def assign_mechanisms(
         min_dwell_days=2,
     )
 
-    touch_intensity = RecencyDecayIntensity(
-        base_rate=touch_rate,
-        decay_factor=0.97,
-        floor_rate=0.02,
-    )
+    touch_intensity: RecencyDecayIntensity | LatentDecayIntensity
+    if latent_touch_intensity:
+        touch_latent_w = _TOUCH_LATENT_WEIGHTS.get(motif_family, _DEFAULT_TOUCH_LATENT_WEIGHTS)
+        touch_intensity = LatentDecayIntensity(
+            base_rate=touch_rate,
+            decay_factor=0.97,
+            floor_rate=0.02,
+            latent_weights=touch_latent_w,
+            boost=1.2,
+        )
+    else:
+        touch_intensity = RecencyDecayIntensity(
+            base_rate=touch_rate,
+            decay_factor=0.97,
+            floor_rate=0.02,
+        )
 
     measurement = NoisyProxy(
         latent_key="latent_account_fit",
diff --git a/leadforge/pipelines/build_v6.py b/leadforge/pipelines/build_v6.py
new file mode 100644
index 0000000..2a867e1
--- /dev/null
+++ b/leadforge/pipelines/build_v6.py
@@ -0,0 +1,293 @@
+"""Pipeline functions for building the v6 lead scoring intro CSVs.
+
+v6 produces TWO exports:
+- **Student-safe**: no leakage columns.
+- **Instructor**: identical rows + one ``__leakage__touches_post_snapshot_15_90``
+  column computed from the simulator's actual event timeline (days 15..90).
+
+Key v6 changes over v5:
+- Snapshot day 14 (was 10).
+- Causally-grounded leakage trap (post-snapshot touches from sim events).
+- No boost/noise injection -- trap signal is purely causal.
+- ``touches_last_7_days`` momentum feature.
+- ``acquisition_wave`` cohort feature for distribution-shift lecture.
+- Nonlinear interaction: opportunity_created x touches_last_7_days.
+"""
+
+from __future__ import annotations
+
+import sys
+
+import numpy as np
+import pandas as pd
+
+__all__ = [
+    "ACV_CAP",
+    "ACV_FLOOR",
+    "FINAL_COLUMNS_INSTRUCTOR",
+    "FINAL_COLUMNS_STUDENT",
+    "INSTRUCTOR_TRAP_COL",
+    "N_LEADS",
+    "RENAME_MAP",
+    "SEED",
+    "SNAPSHOT_DAY",
+    "SUBSAMPLE_N",
+    "TARGET_RATE",
+    "assign_acquisition_wave",
+    "cap_expected_acv",
+    "compute_post_snapshot_touches",
+    "derive_features",
+    "inject_missingness",
+    "rename_and_select",
+    "softcap_expected_acv",
+    "subsample",
+]
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+SEED = 42
+N_LEADS = 5000
+SNAPSHOT_DAY = 14
+SUBSAMPLE_N = 1000
+TARGET_RATE = 0.30
+
+# Narrative-consistent ACV bounds (from narrative.yaml: $18k-$120k).
+ACV_FLOOR = 18_000.0
+ACV_CAP = 120_000.0
+
+INSTRUCTOR_TRAP_COL = "__leakage__touches_post_snapshot_15_90"
+
+# v6 student column set: 19 features + 1 target = 20 columns.
+FINAL_COLUMNS_STUDENT = [
+    "industry",
+    "region",
+    "company_size",
+    "company_revenue",
+    "contact_role",
+    "seniority",
+    "lead_source",
+    "opportunity_created",
+    "demo_completed",
+    "expected_acv",
+    "inbound_touches",
+    "outbound_touches",
+    "touches_week_1",
+    "touches_last_7_days",
+    "days_since_first_touch",
+    "web_sessions",
+    "sales_activities",
+    "days_since_last_touch",
+    "acquisition_wave",
+    "converted",
+]
+
+# Instructor adds the trap column at the end.
+FINAL_COLUMNS_INSTRUCTOR = FINAL_COLUMNS_STUDENT + [INSTRUCTOR_TRAP_COL]
+
+# Snapshot column -> v6 column renaming.
+RENAME_MAP = {
+    "employee_band": "company_size",
+    "estimated_revenue_band": "company_revenue",
+    "role_function": "contact_role",
+    "inbound_touch_count": "inbound_touches",
+    "outbound_touch_count": "outbound_touches",
+    "session_count": "web_sessions",
+    "activity_count": "sales_activities",
+    "converted_within_90_days": "converted",
+}
+
+
+# ---------------------------------------------------------------------------
+# Pipeline steps
+# ---------------------------------------------------------------------------
+
+
+def derive_features(df: pd.DataFrame) -> pd.DataFrame:
+    """Derive binary and momentum features for the v6 column set."""
+    df = df.copy()
+    df["opportunity_created"] = df["opportunity_created"].astype(int)
+    df["demo_completed"] = (df["demo_page_views"] > 0).astype(int)
+    return df
+
+
+def softcap_expected_acv(
+    df: pd.DataFrame,
+    rng: np.random.RandomState,
+    floor: float = ACV_FLOOR,
+    cap: float = ACV_CAP,
+) -> pd.DataFrame:
+    """Soft winsorize expected_acv to avoid hard-clipping ties at the cap.
+
+    Values below floor are clipped. Values above cap are pulled toward cap
+    with additive noise so they cluster near the cap rather than pile at it.
+    """
+    df = df.copy()
+    acv = df["expected_acv"].copy()
+
+    # Floor: hard clip
+    acv = acv.clip(lower=floor)
+
+    # Cap: soft winsorize -- draw values in [cap - 5k, cap] for outliers
+    over_mask = acv > cap
+    n_over = int(over_mask.sum())
+    if n_over > 0:
+        acv.loc[over_mask] = cap - rng.uniform(0, 5000, size=n_over)
+
+    df["expected_acv"] = acv.round(0)
+    return df
+
+
+def cap_expected_acv(df: pd.DataFrame) -> pd.DataFrame:
+    """Hard clip expected_acv to narrative-consistent range [ACV_FLOOR, ACV_CAP].
+
+    Kept for backward compatibility; prefer ``softcap_expected_acv`` for v6.
+    """
+    df = df.copy()
+    df["expected_acv"] = df["expected_acv"].clip(lower=ACV_FLOOR, upper=ACV_CAP)
+    return df
+
+
+def assign_acquisition_wave(df: pd.DataFrame, rng: np.random.RandomState) -> pd.DataFrame:
+    """Assign acquisition_wave (A, B, C) based on lead index position.
+
+    Waves A/B/C are roughly chronological: first third = A, middle = B,
+    last third = C. A small amount of noise is added at the boundaries.
+    """
+    df = df.copy()
+    n = len(df)
+    waves = np.empty(n, dtype=object)
+    third = n // 3
+
+    # Base assignment by position (stable across seeds)
+    waves[:third] = "A"
+    waves[third : 2 * third] = "B"
+    waves[2 * third :] = "C"
+
+    # Add ~5% boundary noise so it's not perfectly deterministic
+    noise_mask = rng.random(n) < 0.05
+    noise_vals = rng.choice(["A", "B", "C"], size=n)
+    waves[noise_mask] = noise_vals[noise_mask]
+
+    df["acquisition_wave"] = waves
+    return df
+
+
+def compute_post_snapshot_touches(
+    snapshot_df: pd.DataFrame,
+    all_touches: list,
+    lead_dates: dict[str, str],
+    snapshot_day: int = SNAPSHOT_DAY,
+    horizon_day: int = 90,
+) -> pd.Series:
+    """Count touches in (snapshot_day, horizon_day] per lead from event data.
+
+    This is the causally-grounded leakage trap: it counts actual simulated
+    touches that occur after the snapshot cutoff.
+    """
+    if not all_touches:
+        return pd.Series(0, index=snapshot_df.index, name=INSTRUCTOR_TRAP_COL)
+
+    td = pd.DataFrame([t.to_dict() for t in all_touches])
+    td["_ts"] = pd.to_datetime(td["touch_timestamp"])
+    td["_lead_date"] = td["lead_id"].map({lid: pd.Timestamp(d) for lid, d in lead_dates.items()})
+    td["_day"] = (td["_ts"] - td["_lead_date"]).dt.days
+
+    # Filter: days in (snapshot_day, horizon_day]
+    post = td[(td["_day"] > snapshot_day) & (td["_day"] <= horizon_day)]
+    counts = post.groupby("lead_id").size().reset_index(name=INSTRUCTOR_TRAP_COL)
+
+    # Merge back onto snapshot
+    result = snapshot_df[["lead_id"]].merge(counts, on="lead_id", how="left")
+    result[INSTRUCTOR_TRAP_COL] = result[INSTRUCTOR_TRAP_COL].fillna(0).astype(int)
+    return result[INSTRUCTOR_TRAP_COL]
+
+
+def rename_and_select(
+    df: pd.DataFrame,
+    *,
+    instructor: bool = False,
+) -> pd.DataFrame:
+    """Rename snapshot columns to v6 names and select final column set."""
+    df = df.rename(columns=RENAME_MAP)
+    df["converted"] = df["converted"].astype(int)
+    columns = FINAL_COLUMNS_INSTRUCTOR if instructor else FINAL_COLUMNS_STUDENT
+    missing = [c for c in columns if c not in df.columns]
+    if missing:
+        raise ValueError(
+            f"Missing required columns after renaming: {missing}. Available: {sorted(df.columns)}"
+        )
+    return df[columns]
+
+
+def subsample(
+    df: pd.DataFrame,
+    rng: np.random.RandomState,
+    n: int = SUBSAMPLE_N,
+    target_rate: float = TARGET_RATE,
+) -> pd.DataFrame:
+    """Stratified subsample to n rows at target_rate conversion."""
+    positives = df[df["converted"] == 1]
+    negatives = df[df["converted"] == 0]
+    n_pos = int(n * target_rate)
+    n_neg = n - n_pos
+
+    if len(positives) < n_pos:
+        print(f"WARNING: only {len(positives)} positives, need {n_pos}", file=sys.stderr)
+        n_pos = len(positives)
+        n_neg = n - n_pos
+    if len(negatives) < n_neg:
+        print(f"WARNING: only {len(negatives)} negatives, need {n_neg}", file=sys.stderr)
+        n_neg = len(negatives)
+
+    pos_sample = positives.sample(n=n_pos, random_state=rng)
+    neg_sample = negatives.sample(n=n_neg, random_state=rng)
+    return (
+        pd.concat([pos_sample, neg_sample]).sample(frac=1, random_state=rng).reset_index(drop=True)
+    )
+
+
+def inject_missingness(df: pd.DataFrame, rng: np.random.RandomState) -> pd.DataFrame:
+    """Apply structured missingness per the v6 contract.
+
+    Patterns:
+    1. Structural: days_since_last_touch is NaN when touch_count=0 (from snapshot)
+    2. MAR: web_sessions — SDR outbound 15%, inbound marketing 2%, partner 5%
+    3. MAR: seniority — partner referral 8%, others 1%
+    4. MCAR: expected_acv — 2% uniform
+    5. Structural + MCAR: days_since_first_touch — NaN when no touches + 2% MCAR
+    6. MCAR: days_since_last_touch — additional 3% on top of structural
+    """
+    df = df.copy()
+    n = len(df)
+
+    # (1) Structural for days_since_last_touch is already NaN from snapshot builder
+    # Note: also structural for days_since_first_touch when no touches
+
+    # (2) MAR: web_sessions by lead_source
+    for source, rate in [
+        ("sdr_outbound", 0.15),
+        ("inbound_marketing", 0.02),
+        ("partner_referral", 0.05),
+    ]:
+        mask = (df["lead_source"] == source) & (rng.random(n) < rate)
+        df.loc[mask, "web_sessions"] = np.nan
+
+    # (3) MAR: seniority by lead_source
+    partner_mask = (df["lead_source"] == "partner_referral") & (rng.random(n) < 0.08)
+    other_mask = (df["lead_source"] != "partner_referral") & (rng.random(n) < 0.01)
+    df.loc[partner_mask | other_mask, "seniority"] = np.nan
+
+    # (4) MCAR: expected_acv 2%
+    acv_mcar = rng.random(n) < 0.02
+    df.loc[acv_mcar, "expected_acv"] = np.nan
+
+    # (5) MCAR: days_since_first_touch 2% on top of structural
+    dsft_mask = rng.random(n) < 0.02
+    df.loc[dsft_mask, "days_since_first_touch"] = np.nan
+
+    # (6) MCAR: days_since_last_touch 3% on top of structural
+    dslt_mask = rng.random(n) < 0.03
+    df.loc[dslt_mask, "days_since_last_touch"] = np.nan
+
+    return df
diff --git a/leadforge/render/snapshots.py b/leadforge/render/snapshots.py
index c2d8df8..18baca3 100644
--- a/leadforge/render/snapshots.py
+++ b/leadforge/render/snapshots.py
@@ -140,6 +140,7 @@ def build_snapshot(
     )
 
     # touches_week_1: count touches within first 7 days of lead creation.
+    # touches_last_7_days: count touches within last 7 days of snapshot window.
     if len(td_windowed) > 0:
         if "_ts" not in td_windowed.columns:
             td_windowed = td_windowed.copy()
@@ -154,6 +155,12 @@ def build_snapshot(
         week1 = td_windowed_copy[td_windowed_copy["_day"] <= 7]
         touches_week_1 = week1.groupby("lead_id").size().reset_index(name="touches_week_1")
 
+        # touches_last_7_days: touches in [effective_window - 7, effective_window]
+        last7 = td_windowed_copy[td_windowed_copy["_day"] > (effective_window - 7)]
+        touches_last_7_days = (
+            last7.groupby("lead_id").size().reset_index(name="touches_last_7_days")
+        )
+
         # days_since_first_touch: snapshot_day - first_touch_day
         first_touch_day = (
             td_windowed_copy.groupby("lead_id")["_day"]
@@ -163,6 +170,7 @@ def build_snapshot(
         )
     else:
         touches_week_1 = pd.DataFrame(columns=["lead_id", "touches_week_1"])
+        touches_last_7_days = pd.DataFrame(columns=["lead_id", "touches_last_7_days"])
         first_touch_day = pd.DataFrame(columns=["lead_id", "_first_touch_day"])
 
     # total_touches_all: count over full horizon (leakage trap when windowed,
@@ -241,6 +249,7 @@ def build_snapshot(
     lead_df = lead_df.merge(any_opps, on="lead_id", how="left")
     lead_df = lead_df.merge(open_opps, on="lead_id", how="left")
     lead_df = lead_df.merge(touches_week_1, on="lead_id", how="left")
+    lead_df = lead_df.merge(touches_last_7_days, on="lead_id", how="left")
     lead_df = lead_df.merge(first_touch_day, on="lead_id", how="left")
     lead_df = lead_df.merge(total_touches_all, on="lead_id", how="left")
 
@@ -249,6 +258,7 @@ def build_snapshot(
     int_agg_cols = [c for c in _INT_AGG_COLS if c in lead_df.columns]
     lead_df[int_agg_cols] = lead_df[int_agg_cols].fillna(0)
     lead_df["touches_week_1"] = lead_df["touches_week_1"].fillna(0)
+    lead_df["touches_last_7_days"] = lead_df["touches_last_7_days"].fillna(0)
     if "total_touches_all" in lead_df.columns:
         lead_df["total_touches_all"] = pd.to_numeric(
             lead_df["total_touches_all"], errors="coerce"
diff --git a/leadforge/schema/features.py b/leadforge/schema/features.py
index 1c6f71a..8dadaf7 100644
--- a/leadforge/schema/features.py
+++ b/leadforge/schema/features.py
@@ -181,6 +181,12 @@ class FeatureSpec:
         "Number of touches in the first 7 days after lead creation.",
         "engagement",
     ),
+    FeatureSpec(
+        "touches_last_7_days",
+        "Int64",
+        "Number of touches in the last 7 days before snapshot cutoff.",
+        "engagement",
+    ),
     FeatureSpec(
         "days_since_first_touch",
         "Float64",
diff --git a/leadforge/simulation/engine.py b/leadforge/simulation/engine.py
index 21eaafc..530b281 100644
--- a/leadforge/simulation/engine.py
+++ b/leadforge/simulation/engine.py
@@ -149,6 +149,8 @@ def simulate_world(
     config: GenerationConfig,
     population: PopulationResult,
     world_graph: WorldGraph,
+    *,
+    latent_touch_intensity: bool = False,
 ) -> SimulationResult:
     """Run the discrete-time simulation for all leads in *population*.
 
@@ -176,7 +178,9 @@ def simulate_world(
     event_rng = root.child("simulation_events")
     post_sim_rng = root.child("simulation_post_sim")
 
-    mechanisms = assign_mechanisms(world_graph.motif_family, mech_rng)
+    mechanisms = assign_mechanisms(
+        world_graph.motif_family, mech_rng, latent_touch_intensity=latent_touch_intensity
+    )
     stage_seq = StageSequence()
 
     # Build lookup indexes.
diff --git a/scripts/build_v6_snapshot.py b/scripts/build_v6_snapshot.py
new file mode 100644
index 0000000..d94d85d
--- /dev/null
+++ b/scripts/build_v6_snapshot.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""Build the v6 lead scoring intro CSVs (generates the bundle internally).
+
+Usage:
+    python scripts/build_v6_snapshot.py OUTPUT_DIR
+
+Produces two CSV files in OUTPUT_DIR:
+- lead_scoring_intro_v6.csv          (student-safe, no leakage columns)
+- lead_scoring_intro_v6_instructor.csv (same rows + __leakage__ trap column)
+
+Both are 1000-row files at ~30% conversion rate with:
+- Day-14 windowed features
+- Structured missingness (MAR + structural + MCAR)
+- Causally-grounded leakage trap (post-snapshot touches from sim events)
+- Expected ACV with soft winsorization
+- Momentum features (touches_week_1, touches_last_7_days, days_since_first_touch)
+- Acquisition wave cohort feature (A/B/C)
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from leadforge.api.generator import Generator
+from leadforge.pipelines.build_v6 import (
+    INSTRUCTOR_TRAP_COL,
+    N_LEADS,
+    SEED,
+    SNAPSHOT_DAY,
+    assign_acquisition_wave,
+    compute_post_snapshot_touches,
+    derive_features,
+    inject_missingness,
+    rename_and_select,
+    softcap_expected_acv,
+    subsample,
+)
+from leadforge.render.snapshots import build_snapshot
+
+# ---------------------------------------------------------------------------
+# Orchestration
+# ---------------------------------------------------------------------------
+
+
+def generate_bundle(seed: int = SEED, n_leads: int = N_LEADS):
+    """Generate a full bundle and return (snapshot, bundle) for event access."""
+    gen = Generator.from_recipe(
+        "b2b_saas_procurement_v1",
+        seed=seed,
+        exposure_mode="research_instructor",
+        n_leads=n_leads,
+        difficulty="intro",
+    )
+    bundle = gen.generate(latent_touch_intensity=True)
+    snapshot = build_snapshot(
+        bundle.simulation_result,
+        bundle.population,
+        snapshot_day=SNAPSHOT_DAY,
+    )
+    return snapshot, bundle
+
+
+def build_v6_datasets(seed: int = SEED) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """Full pipeline: generate -> derive -> process -> split into student + instructor."""
+    rng = np.random.RandomState(seed)
+
+    print("Generating bundle (with latent touch intensity)...", file=sys.stderr)
+    snapshot, bundle = generate_bundle(seed=seed)
+    conv = snapshot["converted_within_90_days"].mean()
+    print(
+        f"  Raw snapshot: {len(snapshot)} rows, conversion={conv:.1%}",
+        file=sys.stderr,
+    )
+
+    # Compute post-snapshot touches from event timeline (causal trap)
+    lead_dates = {lead.lead_id: lead.lead_created_at for lead in bundle.population.leads}
+    trap_series = compute_post_snapshot_touches(
+        snapshot,
+        bundle.simulation_result.touches,
+        lead_dates,
+        snapshot_day=SNAPSHOT_DAY,
+    )
+    snapshot[INSTRUCTOR_TRAP_COL] = trap_series.values
+
+    df = derive_features(snapshot)
+    df = softcap_expected_acv(df, rng)
+    df = assign_acquisition_wave(df, rng)
+
+    # Rename and select (instructor first to keep trap column)
+    df_instructor = rename_and_select(df, instructor=True)
+
+    print("Subsampling...", file=sys.stderr)
+    df_instructor = subsample(df_instructor, rng)
+    print(
+        f"  Subsampled: {len(df_instructor)} rows, "
+        f"conversion={df_instructor['converted'].mean():.1%}",
+        file=sys.stderr,
+    )
+
+    print("Injecting missingness...", file=sys.stderr)
+    df_instructor = inject_missingness(df_instructor, rng)
+
+    # Student version: drop the trap column
+    student_cols = [c for c in df_instructor.columns if not c.startswith("__leakage__")]
+    df_student = df_instructor[student_cols].copy()
+
+    return df_student, df_instructor
+
+
+# ---------------------------------------------------------------------------
+# CLI entry point
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+    if len(sys.argv) < 2:
+        print(f"Usage: {sys.argv[0]} OUTPUT_DIR", file=sys.stderr)
+        sys.exit(1)
+
+    output_dir = Path(sys.argv[1])
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    df_student, df_instructor = build_v6_datasets()
+
+    student_path = output_dir / "lead_scoring_intro_v6.csv"
+    instructor_path = output_dir / "lead_scoring_intro_v6_instructor.csv"
+
+    df_student.to_csv(student_path, index=False)
+    print(
+        f"Student:    {len(df_student)} rows x {len(df_student.columns)} cols -> {student_path}",
+        file=sys.stderr,
+    )
+
+    df_instructor.to_csv(instructor_path, index=False)
+    n_r, n_c = len(df_instructor), len(df_instructor.columns)
+    print(f"Instructor: {n_r} rows x {n_c} cols -> {instructor_path}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/quick_baseline_eval_v6.py b/scripts/quick_baseline_eval_v6.py
new file mode 100644
index 0000000..a4bb3c7
--- /dev/null
+++ b/scripts/quick_baseline_eval_v6.py
@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+"""Quick baseline evaluation for the v6 lead scoring intro dataset.
+
+Usage:
+    python scripts/quick_baseline_eval_v6.py STUDENT_CSV [INSTRUCTOR_CSV]
+
+Runs Logistic Regression, Random Forest, and GBM on a 70/30 hold-out split,
+prints key metrics, Top-K/Lift metrics, value-aware ranking, and feature
+importance. Optionally detects leakage trap from instructor file.
+"""
+
+from __future__ import annotations
+
+import sys
+
+import numpy as np
+import pandas as pd
+from sklearn.compose import ColumnTransformer
+from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (
+    average_precision_score,
+    roc_auc_score,
+)
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+
+TARGET = "converted"
+LEAKAGE_PREFIX = "__leakage__"
+SEED = 42
+
+
+def _get_feature_cols(df, exclude=None):
+    exclude = (exclude or set()) | {TARGET}
+    cat, num = [], []
+    for col in df.columns:
+        if col in exclude:
+            continue
+        if pd.api.types.is_numeric_dtype(df[col]):
+            num.append(col)
+        else:
+            cat.append(col)
+    return cat, num
+
+
+def _build_pipeline(num_cols, cat_cols, model="lr"):
+    num_tr = Pipeline([("imp", SimpleImputer(strategy="median")), ("sc", StandardScaler())])
+    cat_tr = Pipeline(
+        [
+            ("imp", SimpleImputer(strategy="most_frequent")),
+            ("enc", OneHotEncoder(handle_unknown="ignore", sparse_output=False)),
+        ]
+    )
+    pre = ColumnTransformer(
+        [("num", num_tr, num_cols), ("cat", cat_tr, cat_cols)], remainder="drop"
+    )
+    if model == "lr":
+        clf = LogisticRegression(max_iter=1000, solver="lbfgs", random_state=SEED)
+    elif model == "rf":
+        clf = RandomForestClassifier(n_estimators=200, random_state=SEED, n_jobs=-1)
+    elif model == "gbm":
+        clf = GradientBoostingClassifier(n_estimators=100, random_state=SEED)
+    else:
+        raise ValueError(model)
+    return Pipeline([("pre", pre), ("clf", clf)])
+
+
+def evaluate(name, y_true, probs, test_acv=None):
+    auc = roc_auc_score(y_true, probs)
+    pr_auc = average_precision_score(y_true, probs)
+    base_rate = y_true.mean()
+
+    print(f"\n  {name}")
+    print(f"    AUC:    {auc:.3f}")
+    print(f"    PR-AUC: {pr_auc:.3f}")
+
+    n_test = len(y_true)
+    for k in [25, 50]:
+        if k > n_test:
+            continue
+        order = np.argsort(-probs, kind="stable")
+        top_k = y_true.iloc[order[:k]]
+        prec = float(top_k.mean())
+        lift = prec / base_rate if base_rate > 0 else 0.0
+        print(f"    P@{k:3d}:  {prec:.3f}  (Lift: {lift:.2f}x)")
+
+    if test_acv is not None:
+        print("\n    Value-aware ranking:")
+        expected_value = probs * test_acv
+        for k in [25, 50]:
+            if k > n_test:
+                continue
+            order_prob = np.argsort(-probs, kind="stable")[:k]
+            order_ev = np.argsort(-expected_value, kind="stable")[:k]
+            cap_prob = float(np.sum(test_acv[order_prob] * y_true.values[order_prob]))
+            cap_ev = float(np.sum(test_acv[order_ev] * y_true.values[order_ev]))
+            uplift = ((cap_ev - cap_prob) / cap_prob * 100) if cap_prob > 0 else 0.0
+            print(
+                f"    EV@{k:3d}: by_prob=${cap_prob:,.0f}  "
+                f"by_ev=${cap_ev:,.0f}  uplift={uplift:+.1f}%"
+            )
+
+    return {"AUC": auc, "PR-AUC": pr_auc}
+
+
+def main():
+    if len(sys.argv) < 2:
+        print(f"Usage: {sys.argv[0]} STUDENT_CSV [INSTRUCTOR_CSV]", file=sys.stderr)
+        sys.exit(1)
+
+    df = pd.read_csv(sys.argv[1])
+    instructor_path = sys.argv[2] if len(sys.argv) > 2 else None
+
+    print(f"Dataset: {len(df)} rows x {len(df.columns)} cols")
+    print(f"Conversion rate: {df[TARGET].mean():.1%}")
+    print(f"Missing values: {df.isna().sum().sum()} total")
+
+    leakage = {c for c in df.columns if c.startswith(LEAKAGE_PREFIX)}
+    cat_cols, num_cols = _get_feature_cols(df, exclude=leakage)
+    y = df[TARGET].astype(int)
+    x = df[cat_cols + num_cols]
+
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.30, random_state=SEED, stratify=y
+    )
+
+    test_acv = (
+        pd.to_numeric(df.loc[x_test.index, "expected_acv"], errors="coerce").fillna(0).values
+        if "expected_acv" in df.columns
+        else None
+    )
+
+    # --- Baselines ---
+    print(f"\n{'=' * 60}")
+    print("BASELINE MODELS (without leakage)")
+    print(f"{'=' * 60}")
+
+    for model_name, model_key in [
+        ("Logistic Regression", "lr"),
+        ("Random Forest", "rf"),
+        ("Gradient Boosting", "gbm"),
+    ]:
+        pipe = _build_pipeline(num_cols, cat_cols, model=model_key)
+        pipe.fit(x_train, y_train)
+        probs = pipe.predict_proba(x_test)[:, 1]
+        evaluate(model_name, y_test, probs, test_acv=test_acv if model_key == "lr" else None)
+
+    # --- Feature importance (RF) ---
+    print(f"\n{'=' * 60}")
+    print("FEATURE IMPORTANCE (Random Forest)")
+    print(f"{'=' * 60}")
+    rf = _build_pipeline(num_cols, cat_cols, model="rf")
+    rf.fit(x_train, y_train)
+    pre = rf.named_steps["pre"]
+    feature_names = num_cols + list(
+        pre.named_transformers_["cat"].named_steps["enc"].get_feature_names_out(cat_cols)
+    )
+    importances = rf.named_steps["clf"].feature_importances_
+    top = sorted(zip(feature_names, importances, strict=False), key=lambda t: t[1], reverse=True)
+    for feat, imp in top[:15]:
+        bar = "*" * int(imp * 100)
+        print(f"  {feat:40s} {imp:.3f} {bar}")
+
+    # --- Leakage trap (instructor file) ---
+    if instructor_path:
+        df_i = pd.read_csv(instructor_path)
+        trap_cols = [c for c in df_i.columns if c.startswith(LEAKAGE_PREFIX)]
+
+        if trap_cols:
+            print(f"\n{'=' * 60}")
+            print("LEAKAGE TRAP DETECTION (instructor file)")
+            print(f"{'=' * 60}")
+
+            trap_col = trap_cols[0]
+            all_leakage = set(trap_cols)
+
+            cat_i, num_i = _get_feature_cols(df_i, exclude=all_leakage)
+            cat_with, num_with = _get_feature_cols(df_i, exclude=all_leakage - {trap_col})
+            y_i = df_i[TARGET].astype(int)
+
+            deltas = []
+            for seed in range(42, 52):
+                x_without = df_i[cat_i + num_i]
+                xt, xte, yt, yte = train_test_split(
+                    x_without, y_i, test_size=0.30, random_state=seed, stratify=y_i
+                )
+                pipe_wo = _build_pipeline(num_i, cat_i)
+                pipe_wo.fit(xt, yt)
+                auc_wo = roc_auc_score(yte, pipe_wo.predict_proba(xte)[:, 1])
+
+                x_with = df_i[cat_with + num_with]
+                xt2, xte2, yt2, yte2 = train_test_split(
+                    x_with, y_i, test_size=0.30, random_state=seed, stratify=y_i
+                )
+                pipe_w = _build_pipeline(num_with, cat_with)
+                pipe_w.fit(xt2, yt2)
+                auc_w = roc_auc_score(yte2, pipe_w.predict_proba(xte2)[:, 1])
+
+                d = auc_w - auc_wo
+                deltas.append(d)
+                print(f"  Seed {seed}: without={auc_wo:.4f}  with={auc_w:.4f}  delta={d:+.4f}")
+
+            print(f"\n  Mean delta: {np.mean(deltas):+.4f}")
+            print(f"  Min delta:  {np.min(deltas):+.4f}")
+            if np.mean(deltas) > 0.02:
+                print("  -> Detectable leakage signal")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/validate_v6_dataset.py b/scripts/validate_v6_dataset.py
new file mode 100644
index 0000000..d289cf3
--- /dev/null
+++ b/scripts/validate_v6_dataset.py
@@ -0,0 +1,659 @@
+#!/usr/bin/env python3
+"""Validate v6 lead scoring intro CSVs against the v6 spec.
+
+Usage:
+    python scripts/validate_v6_dataset.py STUDENT_CSV INSTRUCTOR_CSV
+
+Validates both exports and runs all mandatory checks. Exit code 0 = all pass.
+"""
+
+from __future__ import annotations
+
+import sys
+
+import numpy as np
+import pandas as pd
+from sklearn.compose import ColumnTransformer
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (
+    average_precision_score,
+    roc_auc_score,
+)
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+TARGET = "converted"
+LEAKAGE_PREFIX = "__leakage__"
+
+BANNED_COLUMNS = {
+    "current_stage",
+    "funnel_stage",
+    "conversion_timestamp",
+    "is_sql",
+    "is_mql",
+    "lead_created_at",
+}
+
+CAT_FEATURES = [
+    "industry",
+    "region",
+    "company_size",
+    "company_revenue",
+    "contact_role",
+    "seniority",
+    "lead_source",
+    "acquisition_wave",
+]
+
+BINARY_FEATURES = [
+    "opportunity_created",
+    "demo_completed",
+]
+
+# Validation thresholds
+AUC_LOWER = 0.62
+AUC_UPPER = 0.90
+TRAP_MEAN_DELTA = 0.03
+TRAP_MIN_DELTA = 0.015
+TRAP_N_SEEDS = 10
+TRAP_SEED_START = 42
+MAX_COL_MISSING_RATE = 0.10
+MAX_DUPLICATE_RATE = 0.01
+MIN_GROUP_SIZE = 50
+RATE_LOWER = 0.02
+RATE_UPPER = 0.98
+
+
+# ---------------------------------------------------------------------------
+# ML pipeline builder (canonical)
+# ---------------------------------------------------------------------------
+
+
+def _build_pipeline(
+    num_cols: list[str],
+    cat_cols: list[str],
+) -> Pipeline:
+    """Build the canonical sklearn baseline pipeline."""
+    numeric_transformer = Pipeline(
+        [
+            ("imputer", SimpleImputer(strategy="median")),
+            ("scaler", StandardScaler()),
+        ]
+    )
+    categorical_transformer = Pipeline(
+        [
+            ("imputer", SimpleImputer(strategy="most_frequent")),
+            ("encoder", OneHotEncoder(handle_unknown="ignore", sparse_output=False)),
+        ]
+    )
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_transformer, num_cols),
+            ("cat", categorical_transformer, cat_cols),
+        ],
+        remainder="drop",
+    )
+    return Pipeline(
+        [
+            ("preprocessor", preprocessor),
+            ("classifier", LogisticRegression(max_iter=1000, solver="lbfgs", random_state=42)),
+        ]
+    )
+
+
+def _get_feature_cols(
+    df: pd.DataFrame,
+    exclude: set[str] | None = None,
+) -> tuple[list[str], list[str]]:
+    """Partition feature columns into (cat_cols, num_cols)."""
+    exclude = (exclude or set()) | {TARGET}
+    cat_cols = []
+    num_cols = []
+    for col in df.columns:
+        if col in exclude:
+            continue
+        if pd.api.types.is_numeric_dtype(df[col]):
+            num_cols.append(col)
+        else:
+            cat_cols.append(col)
+    return cat_cols, num_cols
+
+
+def _fit_evaluate(
+    df: pd.DataFrame,
+    exclude_cols: set[str] | None = None,
+    seed: int = 42,
+    test_size: float = 0.30,
+) -> tuple[float, float, np.ndarray, pd.Series]:
+    """Fit LR on hold-out split, return (AUC, PR-AUC, probs, y_test)."""
+    y = df[TARGET].astype(int)
+    cat_cols, num_cols = _get_feature_cols(df, exclude=exclude_cols)
+    x = df[cat_cols + num_cols]
+
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=test_size, random_state=seed, stratify=y
+    )
+
+    pipe = _build_pipeline(num_cols, cat_cols)
+    pipe.fit(x_train, y_train)
+    probs = pipe.predict_proba(x_test)[:, 1]
+
+    auc = float(roc_auc_score(y_test, probs))
+    pr_auc = float(average_precision_score(y_test, probs))
+    return auc, pr_auc, probs, y_test
+
+
+# ---------------------------------------------------------------------------
+# Check implementations
+# ---------------------------------------------------------------------------
+
+
+def check_basic(df: pd.DataFrame, label: str) -> list[str]:
+    """Basic structural checks."""
+    errors = []
+
+    # Row count
+    if len(df) != 1000:
+        errors.append(f"[{label}] Expected 1000 rows, got {len(df)}")
+
+    # Target
+    if TARGET not in df.columns:
+        errors.append(f"[{label}] Missing target column '{TARGET}'")
+        return errors
+    target_vals = set(df[TARGET].dropna().unique())
+    if not target_vals <= {0, 1}:
+        errors.append(f"[{label}] Target values not binary: {target_vals}")
+    if df[TARGET].isna().any():
+        errors.append(f"[{label}] Target has missing values")
+
+    # Banned columns
+    present = BANNED_COLUMNS & set(df.columns)
+    if present:
+        errors.append(f"[{label}] Banned columns: {sorted(present)}")
+
+    # ID columns
+    id_cols = [c for c in df.columns if c.endswith("_id")]
+    if id_cols:
+        errors.append(f"[{label}] ID columns: {sorted(id_cols)}")
+
+    # Duplicates
+    n_dupes = df.duplicated().sum()
+    dupe_rate = n_dupes / len(df) if len(df) > 0 else 0
+    if dupe_rate > MAX_DUPLICATE_RATE:
+        errors.append(f"[{label}] {n_dupes} duplicates ({dupe_rate:.1%})")
+
+    # Missingness
+    for col in df.columns:
+        if col == TARGET:
+            continue
+        rate = float(df[col].isna().mean())
+        if rate > MAX_COL_MISSING_RATE:
+            errors.append(f"[{label}] {col} missing rate {rate:.1%} > {MAX_COL_MISSING_RATE:.0%}")
+
+    return errors
+
+
+def check_determinism(df: pd.DataFrame, label: str) -> list[str]:
+    """No categorical/binary group should be near-deterministic."""
+    errors = []
+    check_cols = [c for c in CAT_FEATURES + BINARY_FEATURES if c in df.columns]
+    for col in check_cols:
+        stats = df.groupby(col)[TARGET].agg(["mean", "count"])
+        large = stats[stats["count"] >= MIN_GROUP_SIZE]
+        for val, row in large.iterrows():
+            if row["mean"] < RATE_LOWER:
+                errors.append(
+                    f"[{label}] DETERMINISTIC: {col}={val}: "
+                    f"{row['mean']:.1%} (n={int(row['count'])})"
+                )
+            if row["mean"] > RATE_UPPER:
+                errors.append(
+                    f"[{label}] DETERMINISTIC: {col}={val}: "
+                    f"{row['mean']:.1%} (n={int(row['count'])})"
+                )
+    return errors
+
+
+def check_baseline_auc(df: pd.DataFrame, label: str) -> tuple[list[str], dict[str, float]]:
+    """Baseline model AUC on hold-out split."""
+    leakage = {c for c in df.columns if c.startswith(LEAKAGE_PREFIX)}
+    auc, pr_auc, probs, y_test = _fit_evaluate(df, exclude_cols=leakage)
+
+    errors = []
+    if auc < AUC_LOWER:
+        errors.append(f"[{label}] Baseline AUC {auc:.3f} < {AUC_LOWER}")
+    if auc > AUC_UPPER:
+        errors.append(f"[{label}] Baseline AUC {auc:.3f} > {AUC_UPPER}")
+
+    base_rate = float(y_test.mean())
+    n_pos = int(y_test.sum())
+    order = np.argsort(-probs, kind="stable")
+    y_sorted = y_test.values[order]
+
+    metrics: dict[str, float] = {"auc": auc, "pr_auc": pr_auc, "base_rate": base_rate}
+    for k in [25, 50]:
+        if k > len(y_test):
+            continue
+        top_k = y_sorted[:k]
+        prec = float(top_k.mean())
+        rec = float(top_k.sum() / n_pos) if n_pos > 0 else 0.0
+        lift = prec / base_rate if base_rate > 0 else 0.0
+        metrics[f"precision@{k}"] = prec
+        metrics[f"recall@{k}"] = rec
+        metrics[f"lift@{k}"] = lift
+
+    return errors, metrics
+
+
+def check_tree_improvement(df: pd.DataFrame, label: str) -> tuple[list[str], dict[str, float]]:
+    """Tree model should not be significantly worse than LR."""
+    leakage = {c for c in df.columns if c.startswith(LEAKAGE_PREFIX)}
+    cat_cols, num_cols = _get_feature_cols(df, exclude=leakage)
+    y = df[TARGET].astype(int)
+    x = df[cat_cols + num_cols]
+
+    lr_aucs = []
+    gb_aucs = []
+    for seed in range(42, 47):
+        x_train, x_test, y_train, y_test = train_test_split(
+            x, y, test_size=0.30, random_state=seed, stratify=y
+        )
+
+        lr = _build_pipeline(num_cols, cat_cols)
+        lr.fit(x_train, y_train)
+        lr_auc = roc_auc_score(y_test, lr.predict_proba(x_test)[:, 1])
+        lr_aucs.append(lr_auc)
+
+        # GBM with one-hot encoded features
+        numeric_transformer = Pipeline(
+            [("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]
+        )
+        categorical_transformer = Pipeline(
+            [
+                ("imputer", SimpleImputer(strategy="most_frequent")),
+                ("encoder", OneHotEncoder(handle_unknown="ignore", sparse_output=False)),
+            ]
+        )
+        preprocessor = ColumnTransformer(
+            transformers=[
+                ("num", numeric_transformer, num_cols),
+                ("cat", categorical_transformer, cat_cols),
+            ],
+            remainder="drop",
+        )
+        gb = Pipeline(
+            [
+                ("preprocessor", preprocessor),
+                ("classifier", GradientBoostingClassifier(n_estimators=100, random_state=42)),
+            ]
+        )
+        gb.fit(x_train, y_train)
+        gb_auc = roc_auc_score(y_test, gb.predict_proba(x_test)[:, 1])
+        gb_aucs.append(gb_auc)
+
+    mean_lr = float(np.mean(lr_aucs))
+    mean_gb = float(np.mean(gb_aucs))
+    improvement = mean_gb - mean_lr
+
+    errors = []
+    if mean_gb < mean_lr - 0.01:
+        errors.append(
+            f"[{label}] GBM significantly worse than LR: LR={mean_lr:.4f}, GBM={mean_gb:.4f}"
+        )
+
+    metrics = {
+        "mean_lr_auc": mean_lr,
+        "mean_gbm_auc": mean_gb,
+        "mean_improvement": improvement,
+    }
+
+    return errors, metrics
+
+
+def check_leakage_student(df: pd.DataFrame) -> list[str]:
+    """Student export must have ZERO leakage columns."""
+    leakage = [c for c in df.columns if c.startswith(LEAKAGE_PREFIX)]
+    if leakage:
+        return [f"[student] Leakage columns found: {leakage}"]
+    return []
+
+
+def check_leakage_instructor(df: pd.DataFrame) -> list[str]:
+    """Instructor export must have EXACTLY ONE leakage column."""
+    leakage = [c for c in df.columns if c.startswith(LEAKAGE_PREFIX)]
+    if len(leakage) == 0:
+        return ["[instructor] No __leakage__ column found"]
+    if len(leakage) > 1:
+        return [f"[instructor] Multiple leakage columns: {leakage}"]
+    return []
+
+
+def check_trap_delta(df: pd.DataFrame) -> tuple[list[str], dict[str, float]]:
+    """Leakage trap AUC delta across seeds."""
+    leakage_cols = [c for c in df.columns if c.startswith(LEAKAGE_PREFIX)]
+    if not leakage_cols:
+        return ["No trap column found"], {}
+
+    trap_col = leakage_cols[0]
+    all_leakage = set(leakage_cols)
+
+    deltas = []
+    for seed in range(TRAP_SEED_START, TRAP_SEED_START + TRAP_N_SEEDS):
+        auc_without, _, _, _ = _fit_evaluate(df, exclude_cols=all_leakage, seed=seed)
+        auc_with, _, _, _ = _fit_evaluate(df, exclude_cols=all_leakage - {trap_col}, seed=seed)
+        deltas.append(auc_with - auc_without)
+
+    mean_delta = float(np.mean(deltas))
+    min_delta = float(np.min(deltas))
+    max_delta = float(np.max(deltas))
+
+    errors = []
+    if mean_delta < TRAP_MEAN_DELTA:
+        errors.append(f"Trap mean delta {mean_delta:.4f} < {TRAP_MEAN_DELTA} (min={min_delta:.4f})")
+    if min_delta < TRAP_MIN_DELTA:
+        bad = [
+            f"seed {s}: {d:.4f}"
+            for s, d in zip(
+                range(TRAP_SEED_START, TRAP_SEED_START + TRAP_N_SEEDS), deltas, strict=True
+            )
+            if d < TRAP_MIN_DELTA
+        ]
+        errors.append(f"Trap min delta {min_delta:.4f} < {TRAP_MIN_DELTA} [{', '.join(bad)}]")
+
+    stats = {
+        "mean_delta": mean_delta,
+        "min_delta": min_delta,
+        "max_delta": max_delta,
+        "deltas": deltas,
+    }
+    return errors, stats
+
+
+def check_value_aware(df: pd.DataFrame) -> tuple[list[str], list[dict[str, float]]]:
+    """Value-aware ranking: EV ranking should capture >= prob ranking ACV."""
+    if "expected_acv" not in df.columns:
+        return ["expected_acv column missing"], []
+
+    leakage = {c for c in df.columns if c.startswith(LEAKAGE_PREFIX)}
+    cat_cols, num_cols = _get_feature_cols(df, exclude=leakage)
+    y = df[TARGET].astype(int)
+    x = df[cat_cols + num_cols]
+
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.30, random_state=42, stratify=y
+    )
+
+    pipe = _build_pipeline(num_cols, cat_cols)
+    pipe.fit(x_train, y_train)
+    probs = pipe.predict_proba(x_test)[:, 1]
+
+    test_acv = pd.to_numeric(df.loc[x_test.index, "expected_acv"], errors="coerce").fillna(0).values
+    test_converted = y_test.values
+    expected_value = probs * test_acv
+
+    errors = []
+    results = []
+    for k in [25, 50]:
+        if k > len(y_test):
+            continue
+
+        order_prob = np.argsort(-probs, kind="stable")
+        top_k_prob = order_prob[:k]
+        captured_prob = float(np.sum(test_acv[top_k_prob] * test_converted[top_k_prob]))
+
+        order_ev = np.argsort(-expected_value, kind="stable")
+        top_k_ev = order_ev[:k]
+        captured_ev = float(np.sum(test_acv[top_k_ev] * test_converted[top_k_ev]))
+
+        uplift = ((captured_ev - captured_prob) / captured_prob * 100) if captured_prob > 0 else 0.0
+        results.append(
+            {"k": k, "captured_prob": captured_prob, "captured_ev": captured_ev, "uplift": uplift}
+        )
+        if captured_ev < captured_prob:
+            # Warning only, not a hard error
+            pass
+
+    return errors, results
+
+
+def check_acv_range(df: pd.DataFrame, label: str) -> list[str]:
+    """expected_acv within narrative range."""
+    if "expected_acv" not in df.columns:
+        return [f"[{label}] expected_acv column missing"]
+    acv = pd.to_numeric(df["expected_acv"], errors="coerce").dropna()
+    if acv.empty:
+        return [f"[{label}] expected_acv has no values"]
+    errors = []
+    if acv.min() < 18_000 - 1:
+        errors.append(f"[{label}] expected_acv min {acv.min():.0f} < 18,000")
+    if acv.max() > 120_000 + 1:
+        errors.append(f"[{label}] expected_acv max {acv.max():.0f} > 120,000")
+    return errors
+
+
+def check_row_alignment(student: pd.DataFrame, instructor: pd.DataFrame) -> list[str]:
+    """Instructor file must be identical to student in all shared columns."""
+    errors = []
+    student_cols = set(student.columns)
+    shared_cols = [c for c in instructor.columns if c in student_cols]
+
+    if len(student) != len(instructor):
+        errors.append(f"Row count mismatch: student={len(student)}, instructor={len(instructor)}")
+        return errors
+
+    for col in shared_cols:
+        s = student[col]
+        i = instructor[col]
+        # Both NaN or both equal
+        mask = s.isna() & i.isna()
+        vals_match = (s == i) | mask
+        if not vals_match.all():
+            n_diff = int((~vals_match).sum())
+            errors.append(f"Column '{col}' differs in {n_diff} rows between exports")
+
+    return errors
+
+
+def check_cohort_split(df: pd.DataFrame) -> dict[str, float] | None:
+    """Optional cohort split evaluation (train A/B, test C)."""
+    if "acquisition_wave" not in df.columns:
+        return None
+
+    leakage = {c for c in df.columns if c.startswith(LEAKAGE_PREFIX)}
+    cat_cols, num_cols = _get_feature_cols(df, exclude=leakage | {"acquisition_wave"})
+    y = df[TARGET].astype(int)
+    x = df[cat_cols + num_cols]
+
+    # Random split baseline
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.30, random_state=42, stratify=y
+    )
+    pipe_r = _build_pipeline(num_cols, cat_cols)
+    pipe_r.fit(x_train, y_train)
+    random_auc = roc_auc_score(y_test, pipe_r.predict_proba(x_test)[:, 1])
+
+    # Cohort split: train A/B, test C
+    train_mask = df["acquisition_wave"].isin(["A", "B"])
+    test_mask = df["acquisition_wave"] == "C"
+
+    if test_mask.sum() < 30 or train_mask.sum() < 100:
+        return None
+
+    x_train_c = x[train_mask]
+    y_train_c = y[train_mask]
+    x_test_c = x[test_mask]
+    y_test_c = y[test_mask]
+
+    pipe_c = _build_pipeline(num_cols, cat_cols)
+    pipe_c.fit(x_train_c, y_train_c)
+    cohort_auc = roc_auc_score(y_test_c, pipe_c.predict_proba(x_test_c)[:, 1])
+
+    return {
+        "random_auc": random_auc,
+        "cohort_auc": cohort_auc,
+        "drop": random_auc - cohort_auc,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+def validate(student_path: str, instructor_path: str) -> int:
+    """Run all checks and return exit code."""
+    student = pd.read_csv(student_path)
+    instructor = pd.read_csv(instructor_path)
+    all_errors: list[str] = []
+
+    # 1. Basic checks (both)
+    print("=" * 60)
+    print("BASIC CHECKS")
+    print("=" * 60)
+
+    for label, df in [("student", student), ("instructor", instructor)]:
+        errs = check_basic(df, label)
+        status = "FAIL" if errs else "PASS"
+        print(f"  [{label}] Structural checks: {status}")
+        all_errors.extend(errs)
+
+    # 2. Row alignment
+    print("\nRow alignment...", end=" ")
+    errs = check_row_alignment(student, instructor)
+    print("FAIL" if errs else "PASS")
+    all_errors.extend(errs)
+
+    # 3. Leakage column checks
+    print("\nLeakage column checks:")
+    errs = check_leakage_student(student)
+    print(f"  [student] No leakage columns: {'FAIL' if errs else 'PASS'}")
+    all_errors.extend(errs)
+
+    errs = check_leakage_instructor(instructor)
+    leakage_cols = [c for c in instructor.columns if c.startswith(LEAKAGE_PREFIX)]
+    status = "FAIL" if errs else "PASS"
+    print(f"  [instructor] Exactly one leakage column: {status} ({leakage_cols})")
+    all_errors.extend(errs)
+
+    # 4. Determinism checks
+    print("\nDeterminism checks:")
+    for label, df in [("student", student), ("instructor", instructor)]:
+        errs = check_determinism(df, label)
+        print(f"  [{label}]: {'FAIL' if errs else 'PASS'}")
+        all_errors.extend(errs)
+
+    # 5. ACV range
+    print("\nACV range:")
+    for label, df in [("student", student)]:
+        errs = check_acv_range(df, label)
+        acv = pd.to_numeric(df.get("expected_acv", pd.Series()), errors="coerce").dropna()
+        range_str = f"[{acv.min():.0f}, {acv.max():.0f}]" if not acv.empty else "N/A"
+        print(f"  [{label}]: {'FAIL' if errs else 'PASS'} {range_str}")
+        all_errors.extend(errs)
+
+    # 6. Baseline AUC (student dataset)
+    print("\nBaseline AUC (student)...", end=" ")
+    errs, baseline = check_baseline_auc(student, "student")
+    auc = baseline.get("auc", 0)
+    pr_auc = baseline.get("pr_auc", 0)
+    print(f"{'FAIL' if errs else 'PASS'} (AUC={auc:.3f}, PR-AUC={pr_auc:.3f})")
+    all_errors.extend(errs)
+
+    if baseline:
+        for k in [25, 50]:
+            key_p = f"precision@{k}"
+            key_l = f"lift@{k}"
+            if key_p in baseline:
+                print(f"  P@{k}={baseline[key_p]:.3f}  Lift@{k}={baseline[key_l]:.2f}x")
+
+    # 7. Tree improvement (student dataset)
+    print("\nTree model comparison (5 seeds)...", end=" ")
+    errs, tree_metrics = check_tree_improvement(student, "student")
+    print(
+        f"{'FAIL' if errs else 'PASS'} "
+        f"(LR={tree_metrics['mean_lr_auc']:.4f}, "
+        f"GBM={tree_metrics['mean_gbm_auc']:.4f}, "
+        f"delta={tree_metrics['mean_improvement']:+.4f})"
+    )
+    all_errors.extend(errs)
+
+    # 8. Value-aware (student dataset)
+    print("\nValue-aware ranking:")
+    errs, value_results = check_value_aware(student)
+    for vr in value_results:
+        print(
+            f"  K={vr['k']}: by_prob=${vr['captured_prob']:,.0f} "
+            f"by_ev=${vr['captured_ev']:,.0f} "
+            f"uplift={vr['uplift']:+.1f}%"
+        )
+    all_errors.extend(errs)
+
+    # 9. Leakage trap delta (instructor dataset)
+    print("\nLeakage trap delta (instructor, 10 seeds)...", end=" ")
+    errs, trap_stats = check_trap_delta(instructor)
+    if trap_stats:
+        print(
+            f"{'FAIL' if errs else 'PASS'} "
+            f"(mean={trap_stats['mean_delta']:.4f}, "
+            f"min={trap_stats['min_delta']:.4f}, "
+            f"max={trap_stats['max_delta']:.4f})"
+        )
+        if "deltas" in trap_stats:
+            seeds = range(TRAP_SEED_START, TRAP_SEED_START + TRAP_N_SEEDS)
+            for s, d in zip(seeds, trap_stats["deltas"], strict=True):
+                status = "OK" if d >= TRAP_MIN_DELTA else "LOW"
+                print(f"  seed {s}: delta={d:+.4f} [{status}]")
+    else:
+        print("FAIL (no trap data)")
+    all_errors.extend(errs)
+
+    # 10. Missingness summary
+    print("\nMissingness summary (student):")
+    for col in student.columns:
+        n_miss = student[col].isna().sum()
+        if n_miss > 0:
+            print(f"  {col}: {n_miss} ({n_miss / len(student):.1%})")
+    total_miss = student.isna().sum().sum()
+    print(f"  Total: {total_miss} missing values")
+
+    # 11. Cohort split (optional)
+    print("\nCohort split evaluation (optional):")
+    cohort = check_cohort_split(student)
+    if cohort:
+        print(
+            f"  Random split AUC: {cohort['random_auc']:.4f}\n"
+            f"  Cohort split AUC: {cohort['cohort_auc']:.4f}\n"
+            f"  AUC drop:         {cohort['drop']:+.4f}"
+        )
+    else:
+        print("  Skipped (no acquisition_wave column or too few rows)")
+
+    # Report
+    if all_errors:
+        print(f"\n{'=' * 60}")
+        print(f"FAILED - {len(all_errors)} error(s):")
+        for err in all_errors:
+            print(f"  * {err}")
+        return 1
+    else:
+        print(f"\n{'=' * 60}")
+        print("ALL MANDATORY CHECKS PASSED")
+        return 0
+
+
+def main() -> None:
+    if len(sys.argv) != 3:
+        print(f"Usage: {sys.argv[0]} STUDENT_CSV INSTRUCTOR_CSV", file=sys.stderr)
+        sys.exit(1)
+    sys.exit(validate(sys.argv[1], sys.argv[2]))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/mechanisms/test_mechanisms.py b/tests/mechanisms/test_mechanisms.py
index 8acf753..d21b69f 100644
--- a/tests/mechanisms/test_mechanisms.py
+++ b/tests/mechanisms/test_mechanisms.py
@@ -9,7 +9,11 @@
 
 from leadforge.mechanisms.base import MechanismAssignment, MechanismContext, MechanismSummary
 from leadforge.mechanisms.categorical import CHANNEL_QUALITY_SCORES, CategoricalInfluence
-from leadforge.mechanisms.counts import PoissonIntensity, RecencyDecayIntensity
+from leadforge.mechanisms.counts import (
+    LatentDecayIntensity,
+    PoissonIntensity,
+    RecencyDecayIntensity,
+)
 from leadforge.mechanisms.hazards import ConversionHazard
 from leadforge.mechanisms.influence import (
     AdditiveInfluence,
@@ -555,3 +559,65 @@ def test_fit_dominant_higher_conversion_rate_than_friction() -> None:
         fric_p.append(fric_asgn.conversion_hazard.daily_probability(high_fit_latents))
 
     assert sum(fit_p) / len(fit_p) > sum(fric_p) / len(fric_p)
+
+
+# ===========================================================================
+# LatentDecayIntensity
+# ===========================================================================
+
+
+class TestLatentDecayIntensity:
+    """Tests for the latent-aware touch intensity mechanism (v6)."""
+
+    def test_expected_count_no_latents(self) -> None:
+        """With no latents, should behave like plain RecencyDecayIntensity."""
+        ldi = LatentDecayIntensity(base_rate=0.5, decay_factor=0.97, floor_rate=0.02)
+        rdi = RecencyDecayIntensity(base_rate=0.5, decay_factor=0.97, floor_rate=0.02)
+        for t in [0, 5, 10, 50]:
+            assert ldi.expected_count(t) == pytest.approx(rdi.expected_count(t))
+
+    def test_high_latents_increase_rate(self) -> None:
+        """Higher latent values should produce higher expected counts."""
+        ldi = LatentDecayIntensity(
+            base_rate=0.5,
+            latent_weights={"latent_engagement_propensity": 1.0},
+            boost=1.0,
+        )
+        low = ldi.expected_count(5, {"latent_engagement_propensity": 0.2})
+        high = ldi.expected_count(5, {"latent_engagement_propensity": 0.9})
+        assert high > low
+
+    def test_sample_returns_nonnegative_int(self) -> None:
+        ldi = LatentDecayIntensity(
+            base_rate=0.5,
+            latent_weights={"latent_engagement_propensity": 1.0},
+            boost=0.8,
+        )
+        ctx = MechanismContext(latents=_LATENTS, t=5)
+        for _ in range(100):
+            val = ldi.sample(ctx, _rng())
+            assert isinstance(val, int)
+            assert val >= 0
+
+    def test_to_dict_roundtrip(self) -> None:
+        ldi = LatentDecayIntensity(
+            base_rate=0.5,
+            decay_factor=0.95,
+            floor_rate=0.01,
+            latent_weights={"a": 1.0, "b": 0.5},
+            boost=0.8,
+        )
+        d = ldi.to_dict()
+        assert d["name"] == "latent_decay_intensity"
+        assert d["base_rate"] == 0.5
+        assert d["boost"] == 0.8
+
+    def test_assign_mechanisms_with_latent_touch(self) -> None:
+        """assign_mechanisms with latent_touch_intensity=True uses LatentDecayIntensity."""
+        assignment = assign_mechanisms("fit_dominant", _rng(), latent_touch_intensity=True)
+        assert assignment.touch_intensity.name == "latent_decay_intensity"
+
+    def test_assign_mechanisms_without_latent_touch(self) -> None:
+        """Default (False) still uses RecencyDecayIntensity."""
+        assignment = assign_mechanisms("fit_dominant", _rng())
+        assert assignment.touch_intensity.name == "recency_decay_intensity"
diff --git a/tests/scripts/test_build_v6_snapshot.py b/tests/scripts/test_build_v6_snapshot.py
new file mode 100644
index 0000000..696361f
--- /dev/null
+++ b/tests/scripts/test_build_v6_snapshot.py
@@ -0,0 +1,298 @@
+"""Tests for leadforge.pipelines.build_v6 pipeline functions."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from leadforge.pipelines.build_v6 import (
+    ACV_CAP,
+    ACV_FLOOR,
+    FINAL_COLUMNS_INSTRUCTOR,
+    FINAL_COLUMNS_STUDENT,
+    INSTRUCTOR_TRAP_COL,
+    assign_acquisition_wave,
+    derive_features,
+    inject_missingness,
+    rename_and_select,
+    softcap_expected_acv,
+    subsample,
+)
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_snapshot(
+    n: int = 500,
+    conversion_rate: float = 0.30,
+    seed: int = 42,
+) -> pd.DataFrame:
+    """Build a minimal snapshot DataFrame with pre-rename column names."""
+    rng = np.random.RandomState(seed)
+    n_pos = int(n * conversion_rate)
+    n_neg = n - n_pos
+    converted = np.array([1] * n_pos + [0] * n_neg)
+    rng.shuffle(converted)
+
+    return pd.DataFrame(
+        {
+            "lead_id": [f"lead_{i:06d}" for i in range(n)],
+            "industry": rng.choice(["manufacturing", "logistics", "services"], size=n),
+            "region": rng.choice(["US", "UK", "EU"], size=n),
+            "employee_band": rng.choice(["200-499", "500-999", "1000-1999"], size=n),
+            "estimated_revenue_band": rng.choice(["$1M-$10M", "$10M-$50M", "$50M-$200M"], size=n),
+            "role_function": rng.choice(["finance", "ap_manager", "it_director"], size=n),
+            "seniority": rng.choice(
+                ["individual_contributor", "manager", "director", "vp"], size=n
+            ),
+            "lead_source": rng.choice(
+                ["inbound_marketing", "sdr_outbound", "partner_referral"], size=n
+            ),
+            "opportunity_created": rng.choice([True, False], size=n),
+            "demo_page_views": rng.poisson(1, size=n),
+            "expected_acv": rng.uniform(5_000, 200_000, size=n).round(0),
+            "inbound_touch_count": rng.poisson(3, size=n),
+            "outbound_touch_count": rng.poisson(2, size=n),
+            "touches_week_1": rng.poisson(2, size=n),
+            "touches_last_7_days": rng.poisson(2, size=n),
+            "days_since_first_touch": rng.uniform(0, 14, size=n).round(1),
+            "session_count": rng.poisson(4, size=n).astype(float),
+            "activity_count": rng.poisson(3, size=n),
+            "days_since_last_touch": rng.uniform(0, 14, size=n).round(1),
+            "converted_within_90_days": converted,
+        }
+    )
+
+
+def _make_v6_df(
+    n: int = 500,
+    conversion_rate: float = 0.30,
+    seed: int = 42,
+    instructor: bool = False,
+) -> pd.DataFrame:
+    """Build a DataFrame in v6 format (post-rename)."""
+    rng = np.random.RandomState(seed)
+    snapshot = _make_snapshot(n=n, conversion_rate=conversion_rate, seed=seed)
+    df = derive_features(snapshot)
+    df = softcap_expected_acv(df, rng)
+    df = assign_acquisition_wave(df, rng)
+    if instructor:
+        df[INSTRUCTOR_TRAP_COL] = rng.poisson(10, size=n)
+    return rename_and_select(df, instructor=instructor)
+
+
+# ---------------------------------------------------------------------------
+# Tests — derive_features
+# ---------------------------------------------------------------------------
+
+
+class TestDeriveFeatures:
+    def test_opportunity_created_is_int(self):
+        snapshot = _make_snapshot()
+        result = derive_features(snapshot)
+        assert result["opportunity_created"].dtype in (np.int64, np.int32, int)
+        assert set(result["opportunity_created"].unique()).issubset({0, 1})
+
+    def test_demo_completed_derived_from_page_views(self):
+        snapshot = _make_snapshot()
+        snapshot["demo_page_views"] = [0, 3, 0, 1, 0] * (len(snapshot) // 5)
+        result = derive_features(snapshot)
+        expected = (snapshot["demo_page_views"] > 0).astype(int)
+        pd.testing.assert_series_equal(result["demo_completed"], expected, check_names=False)
+
+    def test_does_not_modify_input(self):
+        snapshot = _make_snapshot()
+        original = snapshot.copy()
+        derive_features(snapshot)
+        pd.testing.assert_frame_equal(snapshot, original)
+
+
+# ---------------------------------------------------------------------------
+# Tests — softcap_expected_acv
+# ---------------------------------------------------------------------------
+
+
+class TestSoftcapExpectedACV:
+    def test_floor_enforced(self):
+        rng = np.random.RandomState(42)
+        snapshot = _make_snapshot()
+        snapshot["expected_acv"] = 1_000.0
+        result = softcap_expected_acv(snapshot, rng)
+        assert result["expected_acv"].min() >= ACV_FLOOR
+
+    def test_cap_soft(self):
+        """Values above cap should be pulled near the cap, not all clipped to it."""
+        rng = np.random.RandomState(42)
+        snapshot = _make_snapshot()
+        snapshot["expected_acv"] = 200_000.0
+        result = softcap_expected_acv(snapshot, rng)
+        assert result["expected_acv"].max() <= ACV_CAP
+        # Should NOT all be exactly the cap (soft winsorize adds noise)
+        unique_vals = result["expected_acv"].nunique()
+        assert unique_vals > 1
+
+    def test_within_range_mostly_unchanged(self):
+        rng = np.random.RandomState(42)
+        snapshot = _make_snapshot()
+        snapshot["expected_acv"] = 50_000.0
+        result = softcap_expected_acv(snapshot, rng)
+        assert (result["expected_acv"] == 50_000.0).all()
+
+    def test_does_not_modify_input(self):
+        rng = np.random.RandomState(42)
+        snapshot = _make_snapshot()
+        original = snapshot.copy()
+        softcap_expected_acv(snapshot, rng)
+        pd.testing.assert_frame_equal(snapshot, original)
+
+
+# ---------------------------------------------------------------------------
+# Tests — assign_acquisition_wave
+# ---------------------------------------------------------------------------
+
+
+class TestAssignAcquisitionWave:
+    def test_three_waves(self):
+        rng = np.random.RandomState(42)
+        snapshot = _make_snapshot(n=300)
+        result = assign_acquisition_wave(snapshot, rng)
+        assert set(result["acquisition_wave"].unique()) == {"A", "B", "C"}
+
+    def test_roughly_equal_distribution(self):
+        rng = np.random.RandomState(42)
+        snapshot = _make_snapshot(n=3000)
+        result = assign_acquisition_wave(snapshot, rng)
+        counts = result["acquisition_wave"].value_counts()
+        for wave in ["A", "B", "C"]:
+            assert 800 < counts[wave] < 1200  # ~1000 expected per wave
+
+
+# ---------------------------------------------------------------------------
+# Tests — rename_and_select
+# ---------------------------------------------------------------------------
+
+
+class TestRenameAndSelect:
+    def test_student_columns(self):
+        df = _make_v6_df()
+        assert list(df.columns) == FINAL_COLUMNS_STUDENT
+
+    def test_instructor_columns(self):
+        df = _make_v6_df(instructor=True)
+        assert list(df.columns) == FINAL_COLUMNS_INSTRUCTOR
+
+    def test_converted_is_int(self):
+        df = _make_v6_df()
+        assert df["converted"].dtype in (np.int64, np.int32, int)
+
+    def test_missing_column_raises(self):
+        rng = np.random.RandomState(42)
+        snapshot = _make_snapshot()
+        snapshot = derive_features(snapshot)
+        snapshot = softcap_expected_acv(snapshot, rng)
+        snapshot = assign_acquisition_wave(snapshot, rng)
+        snapshot = snapshot.drop(columns=["industry"])
+        with pytest.raises(ValueError, match="Missing required columns"):
+            rename_and_select(snapshot)
+
+
+# ---------------------------------------------------------------------------
+# Tests — subsample
+# ---------------------------------------------------------------------------
+
+
+class TestSubsample:
+    def test_output_size(self):
+        df = _make_v6_df(n=500)
+        rng = np.random.RandomState(42)
+        result = subsample(df, rng, n=100, target_rate=0.30)
+        assert len(result) == 100
+
+    @pytest.mark.parametrize("target_rate", [0.30, 0.20, 0.40])
+    def test_target_rate_approximate(self, target_rate):
+        df = _make_v6_df(n=500)
+        rng = np.random.RandomState(42)
+        result = subsample(df, rng, n=200, target_rate=target_rate)
+        actual_rate = result["converted"].mean()
+        assert actual_rate == pytest.approx(target_rate, abs=0.01)
+
+    def test_deterministic_given_seed(self):
+        df = _make_v6_df(n=500)
+        r1 = subsample(df, np.random.RandomState(42), n=100, target_rate=0.30)
+        r2 = subsample(df, np.random.RandomState(42), n=100, target_rate=0.30)
+        pd.testing.assert_frame_equal(r1, r2)
+
+
+# ---------------------------------------------------------------------------
+# Tests — inject_missingness
+# ---------------------------------------------------------------------------
+
+
+class TestInjectMissingness:
+    @pytest.mark.parametrize("seed", [42, 99, 7])
+    def test_missingness_rates_bounded(self, seed):
+        """Each column's missingness rate should stay under 10% across seeds."""
+        df = _make_v6_df(n=2000, seed=seed)
+        rng = np.random.RandomState(seed)
+        result = inject_missingness(df, rng)
+        for col in [
+            "web_sessions",
+            "seniority",
+            "days_since_last_touch",
+            "days_since_first_touch",
+            "expected_acv",
+        ]:
+            rate = result[col].isna().mean()
+            assert rate < 0.10, f"{col} missingness rate {rate:.2%} exceeds 10%"
+
+    def test_expected_acv_gets_mcar(self):
+        """expected_acv should have MCAR missingness."""
+        df = _make_v6_df(n=5000)
+        rng = np.random.RandomState(42)
+        result = inject_missingness(df, rng)
+        assert result["expected_acv"].isna().sum() > 0
+
+    def test_other_columns_not_affected(self):
+        """Columns not in the missingness spec should have no new NaN."""
+        df = _make_v6_df(n=500)
+        rng = np.random.RandomState(42)
+        result = inject_missingness(df, rng)
+        miss_cols = {
+            "web_sessions",
+            "seniority",
+            "days_since_last_touch",
+            "days_since_first_touch",
+            "expected_acv",
+        }
+        for col in FINAL_COLUMNS_STUDENT:
+            if col not in miss_cols:
+                orig_nan = df[col].isna().sum()
+                new_nan = result[col].isna().sum()
+                assert new_nan == orig_nan, f"{col} gained unexpected NaN"
+
+    def test_does_not_modify_input(self):
+        df = _make_v6_df(n=500)
+        original = df.copy()
+        rng = np.random.RandomState(42)
+        inject_missingness(df, rng)
+        pd.testing.assert_frame_equal(df, original)
+
+    def test_deterministic_given_seed(self):
+        df = _make_v6_df(n=500)
+        r1 = inject_missingness(df, np.random.RandomState(42))
+        r2 = inject_missingness(df, np.random.RandomState(42))
+        pd.testing.assert_frame_equal(r1, r2)
+
+    def test_web_sessions_missingness_varies_by_source(self):
+        """SDR outbound should have higher web_sessions missingness than inbound."""
+        df = _make_v6_df(n=3000)
+        rng = np.random.RandomState(42)
+        result = inject_missingness(df, rng)
+        sdr_rate = result.loc[df["lead_source"] == "sdr_outbound", "web_sessions"].isna().mean()
+        inbound_rate = (
+            result.loc[df["lead_source"] == "inbound_marketing", "web_sessions"].isna().mean()
+        )
+        assert sdr_rate > inbound_rate