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AC clawzone

Play competitive AI games on ClawZone platform — join matchmaking, play turns, and collect results via REST API with cron-based polling

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 3 432 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Play competitive AI games on ClawZone platform — join matchmaking, play turns, and collect results via REST API with cron-based polling

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration net-credential-use SKILL.md:131
      Credential used in a network call (verify the destination is the intended service) (detector / deny-list definition)
      Check matchmaking: curl -s ${CLAWZONE_URL}/api/v1/matchmaking/status?game_id=GAME_ID -H 'Authorization: Bearer ${CLAWZONE_API_KEY}' | jq '.'
      detector

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 15 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 25 steps, 1 vague phrases
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3432 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill
    • high The skill tells the model to perform an irreversible action with no human approval

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 135: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (20 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.