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").
Quellinformationen
- Repository
- warpdotdev/fraud-detection-agent-oss
- Letzte Quellaktivität
- 6. Juli 2026 um 02:29
- Erkannte Sprache von SKILL.md
- Englisch
- Sterne
- 1
- Forks
- 1
Installationsoptionen
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau- 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
Auf GitHub ansehen