Experiment Notes
v1.0.0Track, search, and learn from experiments. Automatic logging of trial-and-error, success/failure patterns, and distilled lessons. Prevents repeating mistakes.
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (experiment notes, lessons, search) align with the provided CLI and files. The included script implements logging, search, similarity, lessons, stats and distill features, which match the stated purpose.
Instruction Scope
SKILL.md describes 'automatic logging' in the description, but the runtime instructions and CLI are manual (agent must run expnote.py or be configured to call it). Instructions do not request reading unrelated files or credentials. Note: entries (cmd, error, fix) are free-text — users/agents could log sensitive strings if care is not taken.
Install Mechanism
No install spec; this is an instruction-only skill with a bundled Python script. No downloads or external packages are required beyond a local python binary.
Credentials
The skill declares no required env vars or credentials. The code only uses Path.home() to place files under ~/.openclaw/memory/experiments. No network calls, third-party APIs, or unrelated credentials are requested.
Persistence & Privilege
always:false and no install hooks. The skill persists data locally in its own directory but does not modify other skills or global agent settings. Model invocation is allowed by default (normal) but not combined with broad privileges.
Assessment
This skill appears to do what it says and stores notes locally under ~/.openclaw/memory/experiments. Before installing: (1) understand that 'automatic logging' is not implemented as a background daemon — the agent or user must invoke the CLI or add the provided AGENTS.md rules to call it; (2) avoid logging secrets or credentials (commands/errors may contain tokens or passwords); (3) consider setting restrictive file permissions or encrypting the directory if experiment logs may contain sensitive data; (4) if you will allow autonomous agent invocation, ensure the agent is trusted so it doesn't log or leak sensitive commands automatically.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
Binspython