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").
معلومات المصدر
- المستودع
- warpdotdev/fraud-detection-agent-oss
- آخر نشاط في المصدر
- ٦ يوليو ٢٠٢٦ في ٠٢:٢٩
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ١
- التفرعات
- ١
خيارات التثبيت
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
مراجعة ملفات المصدر
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
عرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- 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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