RateMyClaw

Score your OpenClaw agent setup against similar agents. Scans your workspace, generates a local embedding for privacy-preserving semantic matching, and submi...

This skill appears to do what it says: it scans your workspace locally, produces structured tags, optionally generates a local embedding, and submits only tags/embedding/maturity counts to ratemyclaw.com. Before using it: (1) Inspect the generated_profile.json (the skill asks you to review tags) to confirm nothing sensitive was mis-tagged. (2) Be aware embeddings can be a sensitive fingerprint; only send them if you accept that risk. (3) The script may prompt to install scikit-learn (or you can manually run pip install -r requirements.txt); sentence-transformers is optional and large. (4) If you do not want any network calls, do not approve API key generation or submission. (5) Note a small doc mismatch: TF-IDF embeddings are taxonomy-sized (not always 384 floats) — this is an informational inconsistency only.

Type: OpenClaw Skill Name: ratemyclaw Version: 0.5.1 The RateMyClaw skill is designed to analyze an OpenClaw agent's workspace and provide a performance score. While it reads sensitive files like SOUL.md and MEMORY.md, it performs local processing (tag matching and embedding generation) to ensure that only abstracted metadata and numeric vectors are sent to the external endpoint (ratemyclaw.com). The scripts (profile_generator.py and submit_profile.py) explicitly limit data collection to taxonomy tags, file counts, and model names, and they avoid exfiltrating raw text or secret values. The use of subprocess for dependency installation is transparently documented and aligned with the skill's functional requirements.