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AC skill-compatibility-checker

Pre-installation skill vetter that checks for conflicts, system requirement mismatches, missing dependencies, and security issues before you install a skill. Analyzes skill manifests, scans for name/CLI/port conflicts with existing skills, verifies OS/architecture/Node version compatibility, checks for missing CLI tools and API keys, and runs automated security scanning. Returns GO/CAUTION/BLOCKED with detailed remediation steps.

modbender/skill-library-mcp Agent Skills author: modbender MIT 10 files · 2 scripts body ≈ 2 644 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Pre-installation skill vetter that checks for conflicts, system requirement mismatches, missing dependencies, and security issues before you install a skill.

As a process C 52/100 · Has gaps — weak spots: when it triggers, failures and branches, consistency

IntegrationSoftware developmentData and analyticsAI 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
52/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: skill-compatibility-checker (modbender/skill-library-mcp)

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: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 52/100

  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (skill-compatibility-checker) differs from the folder (skill-compatibility-checker-skill)
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 60Steps. 87 steps, 5 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Execution cost. Instruction body is 2644 tokens
  • 100Running it twice. No mutating operations
  • low 13 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)
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 433: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 87 items
  • +3Output format is stated explicitly
  • +4Has examples (11 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.