Self-improvement-loop-v3
v1.0.0Unifies intent-engineering, execution, and feedback into an autonomous loop that detects drift, learns patterns, adjusts specs, transfers skills, and verifie...
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byDaniel Foo Jun Wei@danielfoojunwei
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
Capability signals
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
OpenClaw
Benign
medium confidencePurpose & Capability
The name/description (self-improvement loop) matches the included Python scripts (signal routing, meta-learning, capability expansion, pipeline). Required resources are minimal (no env vars, no external binaries). The scripts implement the described paradigm shifts (drift detection, meta-learning, auto re-specification, transfer, and chained reports).
Instruction Scope
SKILL.md and the scripts instruct the agent to read and write local JSON files (specs, history, logs, chains) and to autonomously revise specifications and generate patches. That behavior is coherent with the stated purpose but is significant: the skill will modify specification files, append to learnings logs and indexes, and can auto-generate revised_specification.json and patch files. There are no instructions or code that read arbitrary system files, environment secrets, or contact external endpoints in the provided sources.
Install Mechanism
No install spec is provided (instruction-only plus included Python scripts). Nothing is downloaded or installed by the skill itself, which reduces supply-chain risk.
Credentials
The skill declares no required environment variables, no primary credential, and no config paths. The code operates on local files passed as arguments or in the working directory — this is proportionate to the functionality of a local pipeline/orchestrator.
Persistence & Privilege
The skill persistently writes local artifacts (learnings_log.json, goal_similarity_index.json, improvement_chain.json, revised specifications, patch files). It does not set always:true and does not request system-wide privileges, but its default behavior can autonomously change/specs and append logs in the working directory. Users should be aware it can auto-revise targets and update index/log files unless configured otherwise.
Assessment
This package appears coherent with its stated purpose but has behavior you should consciously accept before running it. Recommendations: 1) Review the code (especially pipeline.py, capability_expander.py, and meta_learner.py) to confirm the auto-revision and patch-generation policies match your expectations. 2) Run initial executions in a sandbox or a dedicated project workspace so the skill's JSON files (learnings_log.json, improvement_chain.json, goal_similarity_index.json, revised_specification.json, etc.) don't overwrite important data. 3) If you do not want automatic changes, set configuration flags (e.g., no_auto_revise=true, meta_learning_enabled=false, apply_patches_automatically=false) or run the pipeline with provided flags that avoid auto-revise/transfer. 4) Back up any existing specification files before first run. 5) If you require higher assurance, perform a dynamic test (dry-run with sample inputs) and inspect outputs (revision_rationale.md, meta_patches.json) before enabling automatic flows. 6) If you need a stricter security posture, confirm there are no hidden network calls in any unshown/omitted files; absence of credential requests and absence of network code in the shown files reduces exfiltration risk but does not guarantee it if additional files are present.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.