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:

bash

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:

bash

pip install -r <skill_dir>/requirements.txt

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

PriorityLibraryInstall SizeQualityCommand1 (best)sentence-transformers1.5GBSemantic understandingpip install sentence-transformers2 (required)scikit-learn30MBKeyword/taxonomy matchingpip 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#

bash

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#

bash

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:

• Generate a 384-dim embedding locally using sentence-transformers
• Submit tags + embedding + maturity counts to ratemyclaw.com
• Print your score, grade, and a link to the full breakdown

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:

• Taxonomy tags (domains, tools, patterns, integrations) — structured labels only
• Skill slugs (names of installed skills)
• 384 floating-point numbers (the embedding vector)
• Maturity counts (number of memory files, scripts, etc.)
• Automation level and stage

Never sent:

• Raw file contents (SOUL.md, MEMORY.md, scripts, secrets, etc.)
• Workspace text of any kind

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#

• RATEMYCLAW_API_KEY — optional env var. If not set, the script checks for a saved key in .ratemyclaw_key (inside the skill directory). If no key exists anywhere, it prompts before generating one.
• Keys are free and generated via POST /v1/keys on ratemyclaw.com
• Saved key file ( .ratemyclaw_key) is created with chmod 600 and listed in .gitignore

Files#

• scripts/profile_generator.py — Workspace scanner (runs locally, produces JSON)
• scripts/submit_profile.py — Embedding generation + API submission (prompts before any network calls if no key exists)
• references/taxonomy.json — The fixed tag taxonomy (233 tags)