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name email-assistant
description Draft email responses by pulling context from Gmail through PersonalDataHub
user_invocable true

Email Assistant

Given a natural language email request, pull relevant data from connected sources through PersonalDataHub, analyze the context, and draft a response.

Instructions

1. Read the PersonalDataHub config

Read ~/.pdh/config.json to get the hubUrl. If the file doesn't exist, tell the user to run npx pdh init and npx pdh start first.

2. Verify the hub is running

Run curl -s <hubUrl>/health via Bash. If it fails, tell the user to start the server with npx pdh start.

3. Parse the user's request

Analyze the user's message to identify:

  • People — names, email addresses, affiliations (e.g., "Diego@UCSF")
  • Topics — subjects, keywords, project names (e.g., "proposal", "law school")
  • Time context — any mentioned timeframes ("last month", "recently")
  • Desired output — what kind of email to draft (introduction, follow-up, reply, etc.)
  • Missing information — what needs to be found in email threads or other sources

4. Plan the search strategy

Before searching, plan which queries to run and in what order. Think about:

  • Multiple angles — the same conversation may surface under different queries. Plan 2-4 complementary queries that approach the topic from different angles.
  • Multiple sources — if the request involves information beyond email (e.g., GitHub issues, documents), plan pulls from those sources too.
  • Broad then narrow — start with a broad query to understand the landscape, then narrow down with specific queries based on what you find.

Example strategy for "I've been discussing a proposal with Diego@UCSF, we mentioned law school candidates":

  1. from:diego + to:diego — all recent correspondence with Diego
  2. diego proposal — emails mentioning both Diego and proposal
  3. diego "law school" — emails mentioning law school in the Diego thread
  4. If needed: "law school" professor candidate — broader search for candidate discussions

5. Execute the searches

Pull data from PersonalDataHub using the REST API. Make parallel calls when queries are independent:

curl -s -X POST <hubUrl>/app/v1/pull \
  -H "Content-Type: application/json" \
  -d '{"source": "gmail", "query": "<query>", "limit": 20, "purpose": "<why>"}'

Query syntax (Gmail):

  • from:diego / to:diego — by sender/recipient
  • subject:proposal — keyword in subject
  • "law school" — exact phrase
  • newer_than:90d / older_than:7d — time ranges
  • has:attachment — emails with attachments
  • Combine freely: from:diego subject:proposal newer_than:90d

Cross-source pulls: For non-email sources, change the source field:

curl -s -X POST <hubUrl>/app/v1/pull \
  -H "Content-Type: application/json" \
  -d '{"source": "github", "query": "<query>", "limit": 10, "purpose": "<why>"}'

Guidelines:

  • Run 2-4 queries in parallel when they are independent.
  • Deduplicate results across queries by source_item_id — the same email may appear in multiple query results.
  • If the first round of queries doesn't surface enough context, run follow-up queries based on what you learned (names, thread IDs, keywords discovered in the results).
  • Each call gets its own audit log entry, so provide a specific purpose for each.

6. Analyze and synthesize

Review results from all queries and all sources. Extract:

  • The conversation thread and its progression
  • Key details (names, dates, decisions, open questions)
  • Specific information the user asked about (e.g., "candidates we mentioned")
  • Thread IDs for reply threading
  • Any cross-source connections (e.g., a GitHub issue referenced in an email)

Present a brief summary of what you found to the user before drafting. Include:

  • How many emails/items you found across how many queries
  • The key context you extracted
  • Any gaps (information you couldn't find)

7. Compose the draft

Write the email draft based on:

  • The context extracted from all sources
  • The user's stated intent (introduction, follow-up, etc.)
  • Appropriate tone and formality for the context

Show the draft to the user and ask if they'd like any changes.

8. Propose the draft through PersonalDataHub

Once the user approves the draft, propose it via the staging API:

curl -s -X POST <hubUrl>/app/v1/propose \
  -H "Content-Type: application/json" \
  -d '{"source": "gmail", "action_type": "draft_email", "action_data": {"to": "<recipient>", "subject": "<subject>", "body": "<body>"}, "purpose": "<why this draft is being created>"}'

If the email is a reply to an existing thread, include "in_reply_to": "<threadId>" in action_data.

Tell the user the draft has been proposed and is waiting for their approval in the PersonalDataHub GUI at <hubUrl>.

Important notes

  • All data goes through PersonalDataHub's access control. The gateway applies the owner's filters and policies. You will only see data the owner has authorized.
  • Drafts require owner approval. The propose endpoint stages the draft — it does NOT send. The owner must approve it in the PersonalDataHub GUI.
  • Always state your purpose. Every pull and propose call requires a purpose string that gets logged in the audit trail.
  • Show your work. Always show the user what you found and what context you extracted before drafting. Transparency is key.
  • Deduplicate across queries. The same item may appear in multiple query results. Use source_item_id to identify duplicates and merge the context.