ClawHub

Ml Model Eval Benchmark

v0.1.0

Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.

⭐ 0· 346·3 current·3 all-time
byMuhammad Mazhar Saeed@0x-professor
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
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Benign
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Benign
high confidence
✓
Purpose & Capability
Name and description match the included files: SKILL.md, a benchmarking guide, and a Python script that computes weighted scores and rankings. Nothing in the bundle requests unrelated capabilities or credentials.
✓
Instruction Scope
Runtime instructions instruct the agent to run the bundled script and consult the guide. The script only reads a user-supplied JSON input (size-limited), computes scores, and writes an output artifact. The instructions do not ask the agent to read other system files, environment variables, or transmit data externally.
✓
Install Mechanism
No install spec is provided (instruction-only with a bundled script). No downloads, package installs, or external package registry usage are present.
✓
Credentials
The skill declares no environment variables, credentials, or config paths. The script operates solely on an explicit input file and an explicit output path; there are no hidden secret requirements.
✓
Persistence & Privilege
always is false and the skill does not request persistent system presence or modify other skills. The script writes only to the user-specified output path and creates parent directories as needed.
Assessment
This skill appears low-risk and does what it says: run the bundled script with a JSON input to produce a leaderboard. Before installing/using it: (1) review or run the script locally on non-sensitive sample data to confirm behavior; (2) ensure the input JSON and requested output path are trusted (the script will create parent directories and may overwrite the specified output file); (3) note there are no network calls or credential accesses, so it won't exfiltrate data, but it also does minimal validation of metric values and tie-break behavior — verify the weighting/tie-break rules meet your policy for model promotion decisions.

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.
Termshttps://spdx.org/licenses/MIT-0.html

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