mm

A CLI that turns AI prompting from guesswork into engineering. Define what you want, delegate with precision, measure the results.

Install

git clone https://github.com/multimodeai/mm-cli.git
cd mm-cli
npm install
npm run build
npm link   # installs 'mm' globally

Requires Node.js 18+ and a Claude API key or OAuth token:

export ANTHROPIC_API_KEY=sk-ant-api-...
# or
export CLAUDE_CODE_OAUTH_TOKEN=sk-ant-oat-...

The Flow

mm produces the documents that make AI coding sessions work. There are three phases:

Phase 1 — Define (you + mm)

mm spec new <feature>       →  specs/<feature>.md     # WHAT to build
mm intent init              →  INTENT.md              # HOW decisions get made
mm constraint <task>        →  constraints/<task>.md   # WHERE the boundaries are

Each command runs an interactive interview. Claude asks you questions, reads your codebase, searches the web for research, and produces a structured document.

If the output file already exists, mm enters edit mode — loads the existing document and asks what you want to refine. Use --fresh to start over.

Phase 2 — Build (your AI agent)

Take the spec and hand it to Claude Code, Cursor, or any AI coding tool:

"Execute specs/content-uniqueness.md. Reference INTENT.md for
decision authority and CLAUDE.md for project context."

The spec is precise enough for autonomous execution — acceptance criteria, task decomposition, constraint architecture, definition of done.

Phase 3 — Measure (mm again)

mm eval new <skill>                    # Build eval suite
mm eval run <skill>                    # Run with skill loaded
mm eval run <skill> --without-skill    # Run baseline
mm eval compare <skill>               # See the delta

A/B test your AI output with and without context engineering. Multi-axis 5-dimension scoring.

All Commands

Command	What it does	Output
mm preflight	Print the 7 pre-prompting questions	stdout
mm diagnose	5-question AI workflow diagnostic	CONTEXT.md
mm diagnose --deep	12-question deep diagnostic + roadmap	DIAGNOSTIC.md
mm diagnose --health	Automated project health check — scores agent-readiness	HEALTH.md
mm rewrite	Rewrite vague requests into clear ones	stdout / REWRITE.md
mm context build	7-domain interview for business context	.claude/skills/business-context/SKILL.md
mm spec new [name]	Specification engineer (3-phase interview)	specs/<name>.md or SPEC.md
mm spec new [name] --type decompose	Break large changes into safe, ordered steps	specs/<name>.md
mm spec new [name] --type qa	QA specification with discovery + coverage math	specs/<name>.md
mm intent init	Intent & delegation framework	INTENT.md
mm constraint <task>	Constraint architecture (must/must-not/prefer/escalate)	constraints/<task>.md
mm eval new <skill>	Build eval suite via interview	evals/<skill>/eval.yaml
mm eval new <skill> --quick	Auto-generate eval from SKILL.md	evals/<skill>/eval.yaml
mm eval run <skill>	Execute eval suite	evals/<skill>/results/<ts>.json
mm eval compare <skill>	A/B comparison table	stdout
mm skill new <name>	Scaffold a new skill	.claude/skills/<name>/SKILL.md
mm skill list	List skills in current project	stdout
mm skill validate	Check skill structure	stdout
mm skill export --format cursor	Export skills to other IDEs	.cursorrules / .windsurfrules
mm harness verify [spec]	Verify codebase against spec	verify/<spec>/<ts>.json
mm harness audit	Lock-in audit (5 dimensions, /25)	HARNESS-AUDIT.md
mm harness audit --security	Security & resilience audit	SECURITY-AUDIT.md
mm harness route <task>	Task-to-harness routing advice	stdout
mm harness brief	Executive switching cost summary	HARNESS-BRIEF.md

Tools During Interviews

Commands that need codebase access (spec, eval, constraint, intent) give Claude tools to explore your project during the interview:

• read_file — Read any project file
• list_files — Find files by pattern
• list_directory — List directory contents
• search_files — Grep file contents
• git_info — Git log, diff, blame for repository context
• web_search — Search the web (DuckDuckGo, no API key needed)
• web_fetch — Fetch and read web pages

Claude reads your code before asking questions, and searches arxiv/docs when research is needed. Specs reference actual files, functions, and line numbers — not generic placeholders.

Global Flags

--model <model>    Override Claude model (default: claude-sonnet-4-6)
--dry-run          Print system prompt without calling API
--fresh            Ignore existing output file, start from scratch

How It Works

The interview engine sends prompt templates as Claude's system message. Claude drives the conversation — asks questions, reads your codebase, does research. The engine routes your answers back. When Claude produces the final artifact, it's auto-saved to disk.

┌─────────────────────────────┐
│  CLI Layer (Commander.js)   │
│  24 commands                │
└──────────┬──────────────────┘
           │
┌──────────▼──────────────────┐
│  Interview Engine           │
│  Multi-phase Claude         │
│  interviews → files         │
└──────────┬──────────────────┘
           │
┌──────────▼──────────────────┐
│  Claude Client              │
│  @anthropic-ai/sdk          │
│  Tool use + OAuth support   │
└─────────────────────────────┘

Background

Built on the insight that prompting split into 4 disciplines:

1. Prompt Craft — writing clear requests
2. Context Engineering — giving AI the right background
3. Intent Engineering — encoding decision-making rules
4. Specification Engineering — precise specs for autonomous execution

Skills + evals = measurable improvement in AI output quality.

License

AGPL-3.0 — Free to use, modify, and distribute. If you build a hosted service on mm's methodology, you must open-source the service code. See LICENSE for details.