RateMyClaw

RateMyClaw

Score your OpenClaw agent and see how it compares to others working on similar problems.

What It Does

1. Scans your workspace (SOUL.md, MEMORY.md, skills, scripts, integrations, etc.) 2. Maps files to a fixed taxonomy of ~230 tags — no raw file content is extracted 3. Generates a local embedding for privacy-preserving matching (auto-detects best available method) 4. Submits only tags + embedding (float array) + maturity counts to ratemyclaw.com 5. Returns your score, grade, and a link to your full breakdown on the web

Prerequisites

After installing the skill, install Python dependencies:

pip install -r skills/ratemyclaw/requirements.txt

This installs scikit-learn (~30MB) for TF-IDF embeddings.

When running this skill for a user, always check and install requirements first:

pip install -r /requirements.txt

The submit script auto-detects and uses the best available embedding method:

| Priority | Library | Install Size | Quality | Command | |----------|---------|-------------|---------|---------| | 1 (best) | sentence-transformers | ~1.5GB | Semantic understanding | pip install sentence-transformers | | 2 (required) | scikit-learn | ~30MB | Keyword/taxonomy matching | pip install -r requirements.txt |

If sentence-transformers is detected, it's used automatically. Otherwise TF-IDF is the default. The script will suggest the upgrade path after each run.

Quick Start

When the user asks to "rate my claw", "score my agent", "check my setup", or similar:

Step 1: Scan the workspace

python3 scripts/profile_generator.py ~/.openclaw/workspace

This produces a generated_profile.json in the skill directory.

Step 2: Review the profile with the user

Show them what tags were detected and what skills were found. They can correct false positives before submission.

Step 3: Submit to RateMyClaw

python3 scripts/submit_profile.py generated_profile.json

If no RATEMYCLAW_API_KEY env var is set and no saved key exists, the script will ask for confirmation before generating a free key via POST /v1/keys. Pass --yes to skip the prompt in automated contexts.

The submit script will:

Step 4: View results!

The full breakdown, insights, and recommendations are on the website at your score URL — not in the terminal.

What Gets Sent

Sent to ratemyclaw.com:

Never sent:

About embeddings: If an embedding library is installed, a numeric vector is generated locally from your tag data. MiniLM produces a 384-dim semantic embedding; TF-IDF produces a taxonomy-sized sparse vector. While embeddings encode semantic meaning and cannot be trivially reversed into text, they should be treated as potentially sensitive — they represent a condensed fingerprint of your agent's focus areas. If no library is installed, no embedding is sent and scoring relies on tag overlap alone.

Credentials

Files

When the user asks to "rate my claw", "score my agent", "check my setup", or similar:

Step 1: Scan the workspace

python3 scripts/profile_generator.py ~/.openclaw/workspace

This produces a generated_profile.json in the skill directory.

Step 2: Review the profile with the user

Show them what tags were detected and what skills were found. They can correct false positives before submission.

Step 3: Submit to RateMyClaw

python3 scripts/submit_profile.py generated_profile.json

If no RATEMYCLAW_API_KEY env var is set and no saved key exists, the script will ask for confirmation before generating a free key via POST /v1/keys. Pass --yes to skip the prompt in automated contexts.

The submit script will:

Step 4: View results!

The full breakdown, insights, and recommendations are on the website at your score URL — not in the terminal.

After installing the skill, install Python dependencies:

pip install -r skills/ratemyclaw/requirements.txt

This installs scikit-learn (~30MB) for TF-IDF embeddings.

When running this skill for a user, always check and install requirements first:

pip install -r /requirements.txt

The submit script auto-detects and uses the best available embedding method:

| Priority | Library | Install Size | Quality | Command | |----------|---------|-------------|---------|---------| | 1 (best) | sentence-transformers | ~1.5GB | Semantic understanding | pip install sentence-transformers | | 2 (required) | scikit-learn | ~30MB | Keyword/taxonomy matching | pip install -r requirements.txt |

If sentence-transformers is detected, it's used automatically. Otherwise TF-IDF is the default. The script will suggest the upgrade path after each run.