Nm Abstract Metacognitive Self Mod
v1.8.3Analyze and improve the improvement process. Use for detecting quality regressions and refining meta-optimization
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by@athola
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
medium confidencePurpose & Capability
The name/description (metacognitive analysis of improvement processes) aligns with the instructions which operate on improvement memory and performance history. Minor inconsistency: SKILL.md uses Python imports (abstract.improvement_memory, abstract.performance_tracker) but the skill declares no dependencies or required packages — this is likely intended to run against a host that already provides those modules, but the expectation isn't documented in the skill metadata.
Instruction Scope
Runtime instructions are narrowly focused on loading and analyzing ~/.claude/skills/improvement_memory.json and performance_history.json and producing meta-insights. These file reads are consistent with the purpose. No instructions to transmit data externally or access unrelated system paths are present. Note: reading user-home JSON files can expose local data; the skill assumes those files exist and contain relevant improvement data.
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest install risk. There is no download/extract activity or external install URLs.
Credentials
The skill requests no environment variables, credentials, or config paths beyond the two JSON files under ~/.claude/skills. Those file accesses are proportionate to the stated goal of analyzing improvement memory. There are no surprising secret or cloud credential requests.
Persistence & Privilege
Skill is not always-enabled and uses normal autonomous invocation settings. It does not request persistent system-wide privileges or modify other skills' configurations in the provided instructions.
Assessment
This skill appears to do what it says: analyze skill-improvement data stored under ~/.claude/skills. Before installing: (1) confirm your environment provides the referenced Python modules (abstract.improvement_memory, abstract.performance_tracker) or be prepared to add them, (2) inspect the ~/.claude/skills/improvement_memory.json and performance_history.json files to ensure they don't contain sensitive secrets you don't want the skill to read, and (3) if you expect the skill to run in a restricted environment, test it manually (run the snippets) to verify behavior. If you see later versions that add network calls, require credentials, or include an install step that downloads code, reconsider installation.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🦞 Clawdis