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AC blueclaw1

Search and discover AI agents via the OpenClaw (8004scan.io) API. Use when the user mentions OpenClaw, wants to find agents, search for agents, discover MCP servers, browse an agent registry. On first use, prompts for an API key and stores it securely.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 465 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Search and discover AI agents via the OpenClaw (8004scan.io) API. Use when the user mentions OpenClaw, wants to find agents, search for agents, discover MCP…

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationAI and agentstype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
30
Consistency w 8
40
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 · 0

    ✓ No critical or high findings

    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 63/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 40Consistency. Frontmatter name (blueclaw1) differs from the folder (some-skill)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 11 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Execution cost. Instruction body is 465 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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: 5 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (3 code blocks)

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