Midos Self Improver

Structured learning pipeline with quality-gated promotion. Captures corrections, errors, and patterns — promotes only what proves itself through recurrence.

This skill appears to implement a reasonable capture → gate → promote pipeline, but there are red flags you should address before installing: 1) Missing referenced code — skill.json names hook/tool modules that are not included in the package. Ask the author for the missing files or an explanation of how those hooks are provided in your runtime. 2) Data sensitivity — the skill explicitly logs commands, error traces, and context to files in the repository; these can contain secrets or PII. If you install, restrict write locations, set tight filesystem permissions, and review/clean captured entries before promoting them to permanent memory. 3) provenance — source and homepage are unknown; prefer skills with a clear source or run this one in a sandbox environment first. 4) Test locally: run the included tests and a dry-run in an isolated repo, inspect any created files, and confirm promotion rules won't overwrite important files like CLAUDE.md/AGENTS.md. If you cannot obtain the missing hook modules or a trusted source, treat the package as incomplete and do not enable it in production agents.

Type: OpenClaw Skill Name: midos-self-improver Version: 1.0.0 The midos-self-improver skill is a structured framework designed to help AI agents manage their own 'memory' by logging errors, user corrections, and successful patterns to a local directory structure (.learnings/, .patterns/). It includes a deterministic quality-gate and scoring system to promote high-value learnings into project-level rule files like CLAUDE.md. The provided files (SKILL.md, skill.json, and a Python test suite) contain no evidence of data exfiltration, malicious execution, or harmful prompt injection; instead, they focus on project-specific workflow improvements and include security-conscious tests to prevent secret leakage.