Meta Skill Optimizer

Self-improving AI skill optimizer that learns from feedback, auto-tunes prompts, optimizes tool usage patterns, and evolves based on success/failure analysis...

This skill roughly does what it says, but before installing consider the following: 1) The SKILL.md encourages auto-recording of all executions — that can capture private inputs/outputs. Ask whether you are comfortable with the optimizer storing that data on disk (default: ~/.meta_optimizer/<skill>.json) and whether redaction/consent is enforced. 2) There is a mismatch between the README and code: the README shows a hook 'record_execution' and 'optimize_skill' but the included code exposes record_success/record_failure and other methods. Ask the author or inspect the full meta_optimizer.py to confirm the actual API and any missing functions. 3) The README suggests 'pip install numpy scipy json' while the registry lists no install spec; 'json' is stdlib (pip install is unnecessary) and SciPy may be unnecessary — audit dependencies before running pip. 4) Search the full source for any network/telemetry/export code (functions named export/merge/send/post/requests/urllib/socket) before giving it access to real data. If you must try it: run in a sandboxed environment with non-sensitive data, review/modify code to add redaction or require explicit user confirmation before recording or exporting, and periodically inspect/clear ~/.meta_optimizer. If you want, I can: (a) scan the remaining truncated portion of meta_optimizer.py for network calls or export functions, (b) list exact lines where the README and code disagree, or (c) produce a minimal safe wrapper that forces redaction and disables automatic after_execution hooks.

Type: OpenClaw Skill Name: meta-skill-optimizer Version: 1.0.0 The meta-skill-optimizer bundle is a utility designed to track and improve AI agent performance through feedback loops and historical analysis. The Python implementation in meta_optimizer.py manages local state in ~/.meta_optimizer/ to record task outcomes, categorize behaviors, and suggest optimizations without any network access, shell execution, or sensitive data retrieval. The SKILL.md instructions are consistent with the code's purpose and do not contain any prompt injection or malicious directives.