Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Productivity
Insight Engine
Logs/metrics → Python statistics → LLM interpretation → Notion reports. Use when: generating daily/weekly/monthly operational insights from AI system logs, p...
skill openclawclawhub Free
Logs/metrics → Python statistics → LLM interpretation → Notion reports. Use when: generating daily/weekly/monthly operational insights from AI system logs, p...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/architecture.md, scripts/prompts/daily_analyst.md, scripts/prompts/monthly_analyst.md, scripts/prompts/weekly_analyst.md, scripts/src/__init__.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.3
Provenance
- Publisher
- nissan
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
insight-engine
Data-driven insights from operational logs: collect → stats → LLM interpretation → Notion.
Architecture
collect (Python stats only) ├── Langfuse OTEL traces/scores/observations ├── OpenClaw/gateway logs ├── Git activity └── Control plane scores ↓ build_*_data_packet() ← all stats computed in Python before LLM call ↓ call_claude(system_prompt, structured_json) ← LLM interprets, doesn't compute ↓ write_*_reflection() → Notion See references/architecture.md for full design rationale.
Quick start
# Install deps pip install anthropic requests pyyaml # Configure cp scripts/config/analyst.yaml.example config/analyst.yaml # Edit config/analyst.yaml — set langfuse URL, notion IDs, model choices # Dry run (local Ollama, no Notion write) python3 scripts/src/engine.py --mode daily --dry-run # Print data packet + prompt to stdout (for agent consumption, no API calls) python3 scripts/src/engine.py --mode daily --data-only # Live run python3 scripts/src/engine.py --mode daily python3 scripts/src/engine.py --mode weekly python3 scripts/src/engine.py --mode monthly
Required env vars
ANTHROPIC_API_KEY=sk-ant-... # Anthropic API key NOTION_API_KEY=secret_... # Notion integration token LANGFUSE_BASE_URL=http://localhost:3100 # Langfuse server URL LANGFUSE_PUBLIC_KEY=pk-lf-... # Langfuse public key LANGFUSE_SECRET_KEY=sk-lf-... # Langfuse secret key NOTION_ROOT_PAGE_ID=<uuid> # Root Notion page for reports NOTION_DAILY_DB_ID=<uuid> # Notion database for daily entries Or configure in config/analyst.yaml.
Key design principles
Stats before LLM — Python computes all numbers. The LLM interprets, doesn't aggregate. Citation-enforcing prompts — System prompts require every claim to cite a specific number. No hallucinated trends — < 7 data points → report "insufficient data (n=X)" Dry-run mode — Uses local Ollama (free) to preview output; skip Notion write. Data-only mode — Outputs the full data packet + prompts for agent/subagent use.
Cron setup (LaunchAgent example)
<!-- ~/Library/LaunchAgents/com.yourname.insight-engine-daily.plist --> <key>StartCalendarInterval</key> <dict> <key>Hour</key><integer>23</integer> <key>Minute</key><integer>0</integer> </dict> <key>ProgramArguments</key> <array> <string>/usr/bin/python3</string> <string>/path/to/insight-engine/scripts/src/engine.py</string> <string>--mode</string><string>daily</string> </array>
Extending to new data sources
Add a collector in scripts/src/collectors/: Create my_source.py with a fetch_*() function returning a plain dict Import and call it in build_daily_data_packet() in engine.py Reference the new key in prompts/daily_analyst.md under "Data sources"
See also
references/architecture.md — full design rationale and layer descriptions scripts/prompts/daily_analyst.md — system prompt with citation rules scripts/config/analyst.yaml.example — config template
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs1 Scripts
- SKILL.md
- references/architecture.md
- scripts/prompts/daily_analyst.md
- scripts/prompts/monthly_analyst.md
- scripts/prompts/weekly_analyst.md
- scripts/src/__init__.py