Add OpenClaw ecosystem: self-healing, memorybox, self-evolving
Nobody has claimed this yet.
Assessment
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Newbie friendliness
- 45/100
Research direction
Start by reading the repository's contribution guidelines and the existing list sections for tools, utilities, and agent skills. Add entries for the three linked OpenClaw projects in the matching sections, following the list's existing format; done when all three links and descriptions appear in the appropriate sections.
Written by the indexing model from the issue text.
Description
Hi! Requesting addition of three production-tested Claude Code tools from the OpenClaw ecosystem:
1. openclaw-self-healing (★32) — Tools & Utilities
Link: https://github.com/Ramsbaby/openclaw-self-healing
4-tier autonomous crash recovery for any Claude Code service. Preflight config validation → instant KeepAlive restart → HTTP watchdog with exponential backoff → AI emergency recovery (LLM-agnostic: Claude/GPT-4/Gemini/Ollama) → multi-channel alerts. 64% of incidents resolved autonomously in production. Includes Prometheus metrics, Docker Compose, one-line installer. MIT.
2. openclaw-memorybox (★8) — Tools & Utilities
Link: https://github.com/Ramsbaby/openclaw-memorybox
Memory hygiene CLI for Claude Code. Prevents context overflow crashes by keeping MEMORY.md / CLAUDE.md lean. memorybox doctor ~/.claude diagnoses; memorybox split fixes interactively. -83% memory size, -91% context pressure, 0 crashes in production. Zero dependencies, works offline. MIT.
3. openclaw-self-evolving (★2) — Agent Skills
Link: https://github.com/Ramsbaby/openclaw-self-evolving
Weekly self-improvement pipeline. Scans ~/.claude/logs/ JSONL files for retry loops, rule violations, user frustration — generates exact CLAUDE.md before/after diffs. Zero API calls, pure bash + python3. Human approval required; false positive rate ~8%. MIT.
All three are MIT licensed and battle-tested on a 24/7 production instance. They solve complementary problems: crash recovery, memory hygiene, and behavioral learning — the three pillars of a stable long-running agent.
- Dominant language
- No language data
- Stars
- 530
- Forks
- 651
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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