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AC ignacio-lerer-capability-admission-check

Screens AI Skills, MCP servers, plugins, and agent tools before installation or activation using provenance, static inspection, least privilege, version pinning, and PASS/REVIEW/BLOCK decisions. Use when a user asks whether an agent capability is safe, wants to install a Skill or MCP, reviews a marketplace or GitHub artifact, or reassesses a changed capability. Do not use as an antivirus guarantee, a runtime sandbox, or a substitute for professional incident response.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 2 files body ≈ 1 172 tokens Open the sourcegithub.com↗ analyzed 75 min ago

Screens AI Skills, MCP servers, plugins, and agent tools before installation or activation using provenance, static inspection, least privilege, version…

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerGitHubAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

    Files scanned: 2. 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

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 40Consistency. Frontmatter name (ignacio-lerer-capability-admission-check) differs from the folder (capability-admission-check-adrian-lerer)
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 27 steps
    • 100Execution cost. Instruction body is 1172 tokens

    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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 472: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 27 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.