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Ai Compound 1.0.1

Make your AI agent learn and improve automatically. Reviews sessions, extracts learnings, updates memory files, and compounds knowledge over time. Set up nightly review loops that make your agent smarter every day.

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High Signal

Make your AI agent learn and improve automatically. Reviews sessions, extracts learnings, updates memory files, and compounds knowledge over time. Set up nightly review loops that make your agent smarter every day.

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Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
SKILL.md

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.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. 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
1.0.0

Documentation

ClawHub primary doc Primary doc: SKILL.md 14 sections Open source page

Compound Engineering

Make your AI agent learn automatically. Extract learnings from sessions, update memory files, and compound knowledge over time. The idea: Your agent reviews its own work, extracts patterns and lessons, and updates its instructions. Tomorrow's agent is smarter than today's.

Quick Start

# Review last 24 hours and update memory npx compound-engineering review # Create hourly memory snapshot npx compound-engineering snapshot # Set up automated nightly review (cron) npx compound-engineering setup-cron

The Compound Loop

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ DAILY WORK β”‚ β”‚ Sessions, chats, tasks, decisions β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ NIGHTLY REVIEW (10:30 PM) β”‚ β”‚ β€’ Scan all sessions from last 24h β”‚ β”‚ β€’ Extract learnings and patterns β”‚ β”‚ β€’ Update MEMORY.md and AGENTS.md β”‚ β”‚ β€’ Commit and push changes β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ NEXT DAY β”‚ β”‚ Agent reads updated instructions β”‚ β”‚ Benefits from yesterday's learnings β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

What Gets Extracted

Patterns: Recurring approaches that worked Gotchas: Things that failed or caused issues Preferences: User preferences discovered Decisions: Key decisions and their reasoning TODOs: Unfinished items to remember

Automatic Hourly Memory

  • Add to your HEARTBEAT.md:
  • # Hourly Memory Snapshot
  • Every hour, append a brief summary to memory/YYYY-MM-DD.md:
  • What was accomplished
  • Key decisions made
  • Anything to remember
  • Or use cron:
  • # Add to clawdbot config or crontab
  • 0 * * * * clawdbot cron run compound-hourly

Nightly Review Job

Add this cron job to Clawdbot: { "id": "compound-nightly", "schedule": "30 22 * * *", "text": "Review all sessions from the last 24 hours. For each session, extract: 1) Key learnings and patterns, 2) Mistakes or gotchas to avoid, 3) User preferences discovered, 4) Unfinished items. Update MEMORY.md with a summary. Update memory/YYYY-MM-DD.md with details. Commit changes to git." }

Manual Review Command

  • When you want to trigger a review manually:
  • Review the last 24 hours of work. Extract:
  • 1. **Patterns that worked** - approaches to repeat
  • 2. **Gotchas encountered** - things to avoid
  • 3. **Preferences learned** - user likes/dislikes
  • 4. **Key decisions** - and their reasoning
  • 5. **Open items** - unfinished work
  • Update:
  • MEMORY.md with significant long-term learnings
  • memory/YYYY-MM-DD.md with today's details
  • AGENTS.md if workflow changes needed
  • Commit changes with message "compound: daily review YYYY-MM-DD"

MEMORY.md (Long-term)

  • # Long-Term Memory
  • ## Patterns That Work
  • When doing X, always Y first
  • User prefers Z approach for...
  • ## Gotchas to Avoid
  • Don't do X without checking Y
  • API Z has rate limit of...
  • ## User Preferences
  • Prefers concise responses
  • Timezone: PST
  • ...
  • ## Project Context
  • Main repo at /path/to/project
  • Deploy process is...

memory/YYYY-MM-DD.md (Daily)

  • # 2026-01-28 (Tuesday)
  • ## Sessions
  • 09:00 - Built security audit tool
  • 14:00 - Published 40 skills to MoltHub
  • ## Decisions
  • Chose to batch publish in parallel (5 sub-agents)
  • Security tool covers 6 check categories
  • ## Learnings
  • ClawdHub publish can timeout, retry with new version
  • npm publish hangs sometimes, may need to retry
  • ## Open Items
  • [ ] Finish remaining MoltHub uploads
  • [ ] Set up analytics tracker

Hourly Snapshots

For more granular memory, create hourly snapshots: # Creates memory/YYYY-MM-DD-HH.md every hour */60 * * * * echo "## $(date +%H):00 Snapshot" >> ~/clawd/memory/$(date +%Y-%m-%d).md Or have the agent do it via heartbeat by checking time and appending to daily file.

The Compound Effect

Week 1: Agent knows basics Week 2: Agent remembers your preferences Week 4: Agent anticipates your needs Month 2: Agent is an expert in your workflow Knowledge compounds. Every session makes future sessions better.

Nightly Review (launchd - macOS)

<!-- ~/Library/LaunchAgents/com.clawdbot.compound-review.plist --> <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "..."> <plist version="1.0"> <dict> <key>Label</key> <string>com.clawdbot.compound-review</string> <key>ProgramArguments</key> <array> <string>/opt/homebrew/bin/clawdbot</string> <string>cron</string> <string>run</string> <string>compound-nightly</string> </array> <key>StartCalendarInterval</key> <dict> <key>Hour</key> <integer>22</integer> <key>Minute</key> <integer>30</integer> </dict> </dict> </plist>

Hourly Memory (crontab)

# Add with: crontab -e 0 * * * * /opt/homebrew/bin/clawdbot cron run compound-hourly 2>&1 >> ~/clawd/logs/compound.log

Best Practices

Review before sleep - Let the nightly job run when you're done for the day Don't over-extract - Focus on significant learnings, not noise Prune regularly - Remove outdated info from MEMORY.md monthly Git everything - Memory files should be version controlled Trust the compound - Effects are subtle at first, dramatic over time Built by LXGIC Studios - @lxgicstudios Built by LXGIC Studios GitHub: github.com/lxgicstudios/ai-compound Twitter: @lxgicstudios

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
1 Docs
  • SKILL.md Primary doc