per-agent-compression-universal
Zero-config memory consolidation for multi-agent OpenClaw deployments. Auto-discovers agents and registers staggered cron tasks with full state tracking, deduplication, and domain-aware extraction.
Best use case
per-agent-compression-universal is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Zero-config memory consolidation for multi-agent OpenClaw deployments. Auto-discovers agents and registers staggered cron tasks with full state tracking, deduplication, and domain-aware extraction.
Teams using per-agent-compression-universal should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/per-agent-compression-universal/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How per-agent-compression-universal Compares
| Feature / Agent | per-agent-compression-universal | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Zero-config memory consolidation for multi-agent OpenClaw deployments. Auto-discovers agents and registers staggered cron tasks with full state tracking, deduplication, and domain-aware extraction.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Per-Agent Memory Compression Skill
## Overview
This skill automates weekly memory consolidation for multi-agent OpenClaw deployments. It discovers all agents with workspaces and registers staggered cron tasks that compress old daily notes into long-term memory files.
## Key Features
- **Auto-discovery**: Finds all agents via `openclaw agents list`
- **Workspace isolation**: Each agent compresses its own memory
- **State persistence**: Tracks processed notes in `.compression_state.json`
- **Deduplication**: Avoids duplicate entries
- **Domain awareness**: Includes DOMAIN_CONTEXT for tailored extraction
- **Zero config**: Just run `./install.sh`
## Installation
```bash
cd /root/.openclaw/workspace/skills/per-agent-compression-universal
./install.sh
```
This creates 5 staggered tasks (if you have 5 agents) running Sundays 03:00-05:00 Shanghai time.
## What It Does
1. Pre-check paths and initialize state
2. List daily notes older than 7 days (skip recent)
3. Sort oldest first, process up to 5 notes per run
4. For each note:
- Read content
- Extract factual info (preferences, decisions, personal info)
- Append to target files with date headers
- Move original to `memory/processed/`
5. Update state file
6. Clean working buffer
7. Send DingTalk summary
## File Structure
Each agent workspace should have:
- `memory/YYYY-MM-DD.md` (daily notes)
- `USER.md`, `IDENTITY.md`, `SOUL.md`, `MEMORY.md` (targets)
After running:
- `memory/.compression_state.json` (state tracking)
- `memory/processed/` (moved old notes)
## Customization
Edit `install.sh` to adjust:
- Stagger offsets (`OFFSETS` array)
- Domain context per agent (`DOMAIN_CONTEXT` associative array)
- Cron expression (currently Sundays)
## Troubleshooting
- **Task hangs**: Check STATE_FILE path uses `{WORKSPACE}` (uppercase), not `{workspace}`
- **No notes processed**: Ensure there are daily notes older than 7 days
- **Timeout**: Increase `--timeout` in install.sh (default 1200s)
- **Delivery fails**: Verify DingTalk connector configured with `to` field
## Uninstall
```bash
./uninstall.sh
```
Removes all `per_agent_compression_*` tasks.
## Version
Current: 1.3.4 (fixes STATE_FILE case sensitivity bug)
## Support
See README.md for full documentation.Related Skills
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