SKILLEMALL.ai
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AC guess-ai

OceanBus-powered social deduction game — find the AI impostors among humans. Use when hosting or joining a multiplayer "Who's the AI?" party via OceanBus P2P messaging. One host, 4-6 players, encrypted voting, zero infrastructure. npm install oceanbus.

ClawHub Agent Skills author: ryanbihai v2.1.7 MIT-0 12 files body ≈ 2 570 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
84
Run on models
none yet
Process rating
C
58/100
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token package-lock.json:78
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…9dM/mwVgvbZJaSNaRk+bshk…Kbz+IoId…W0Q==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:145
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…SDq+2kAA…MOe/+5cdoEdg==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:172
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…GLw+xYSd…cqA==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:228
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…FrF+LTRo…W3g==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:237
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…A6j+hAmM…GbS+kf5c…csw==",
      detector

    Files scanned: 12. 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 58/100

    • 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. 16 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 7 branches
    • 100Steps. 53 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2570 tokens
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (10 tags): a typed call is more reliable

    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 252: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 53 items
    • +4Has examples (9 code blocks)

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

    External checks

    ClawHub: suspicious
    The main game mostly matches its description, but the package includes an unrelated service-registration script and under-explains credential and LLM data handling.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026