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BC SJ-IRAC Non-Use Engine（撤三证据推理系统）

name: jiang-nonuse slug: jiang-irac-nonuse-evidence displayname: 蒋道理｜撤三证据链与风险审核引擎（SJ-IRAC） version: 3.0.1 description: CNIPA撤三（连续三年不使用）双轨证据引擎：答辩证据链构建 + 质证审计（SJ-6 + IRAC + 风险A–E）。 homepage: https://...

modbender/skill-library-mcp Agent Skills author: modbender MIT 19 files body ≈ 1 865 tokens Open the sourcegithub.com↗ analyzed 2 d ago

name: jiang-nonuse slug: jiang-irac-nonuse-evidence displayname: 蒋道理｜撤三证据链与风险审核引擎（SJ-IRAC） version: 3.0.1 description: CNIPA撤三（连续三年不使用）双轨证据引擎：答辩证据链构建 +…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMarketingtype 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
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 19. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (SJ-IRAC Non-Use Engine（撤三证据推理系统）) differs from the folder (jiang-irac-nonuse-evidence)
  • 100Tools and files. No external tools needed
  • 100Steps. 93 steps
  • 100Execution cost. Instruction body is 1865 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 16 top-level sections: this looks like several domains in one skill

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
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 200: enough signal without eating the budget
  • +4Structure: 51 headings
  • +3Step-by-step instructions: 93 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.