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user-convo-summary

Summarize a Warp user's agent mode conversation history to understand how they use Warp. Use when asked to profile, summarize, or investigate what a specific user has been doing in agent mode (e.g., "summarize agent mode usage for user@example.com", "what has this user been using Warp for?", "get conversation info for this user").

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Dépôt
warpdotdev/fraud-detection-agent-oss
Dernière activité de la source
6 juillet 2026 à 02:29
Langue détectée de SKILL.md
anglais
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1
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SKILL.md
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name
user-convo-summary
description
Summarize a Warp user's agent mode conversation history to understand how they use Warp. Use when asked to profile, summarize, or investigate what a specific user has been doing in agent mode (e.g., "summarize agent mode usage for user@example.com", "what has this user been using Warp for?", "get conversation info for this user").
# User Conversation Summary Summarize a user's agent mode history from their email address. ## Schema config The `bq` queries below use the **default** logical mapping: project `YOUR_GCP_PROJECT`, `core_user_facts` (dataset `prod`) and `agent_mode_messages_ugc` (dataset `prod_ugc`). Replace them with your physical names from `config/schema.json` (see `SCHEMA.md`). ## Step 1: Look Up user_id ```bash bq query --use_legacy_sql=false --format=csv \ "SELECT user_id FROM \`YOUR_GCP_PROJECT.prod.core_user_facts\` WHERE user_email = '[USER_EMAIL]' LIMIT 1" ``` ## Step 2: Fetch Recent Conversations ```bash bq query --use_legacy_sql=false --format=csv \ "SELECT conversation_id, message_type, timestamp_utc, msg_content FROM `YOUR_GCP_PROJECT.prod_ugc.agent_mode_messages_ugc` WHERE user_id = '[USER_ID]' ORDER BY timestamp_utc DESC LIMIT 500" ``` Key `message_type` values: - `user` — the user's prompt (text at `msg_content` JSON path `$.query`) - `assistant` — what the agent responded/did ## Step 3: Analyze - Extract `user` messages (prompt via `JSON_EXTRACT_SCALAR(msg_content, '$.query')`) to understand what the user asked - Extract `assistant` messages to see what the agent did - Group by `conversation_id` to understand conversation flow - Look for patterns in topics, projects, and workflows ## Step 4: Write Summary Write 1–2 paragraphs capturing: - Primary use cases and topics - Key tools/workflows leveraged - Engagement patterns (conversation length, frequency, iterative vs. one-shot) - Notable preferences or recurring patterns
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