SKILLEMALL.ai
GitHub ↗ RU Sign in

AF pattern-extraction

Extract design systems, architecture patterns, and methodology from codebases into reusable skills and documentation. Use when analyzing a project to capture patterns, creating skills from existing code, extracting design tokens, or documenting how a project was built. Triggers on "extract patterns", "extract from this repo", "analyze this codebase", "create skills from this project", "extract design system".

modbender/skill-library-mcp Agent Skills author: modbender MIT 10 files body ≈ 2 448 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Extract design systems, architecture patterns, and methodology from codebases into reusable skills and documentation.

As a process F 44/100 · Will not run — References files that are not bundled: ../../agents/extraction/, ../commands/extraction/extract-patterns.md, ../refinement/

AnalyzerDockerSoftware developmentDesigntype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: ../../agents/extraction/, ../commands/extraction/extract-patterns.md, ../refinement/
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 missing-ref reference to a missing file: ../../agents/extraction/
  • warning missing-ref reference to a missing file: ../commands/extraction/extract-patterns.md
  • warning missing-ref reference to a missing file: ../refinement/

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: ../../agents/extraction/, ../commands/extraction/extract-patterns.md, ../refinement/
  • 0Tools and files. 3 referenced file(s) missing: ../../agents/extraction/, ../commands/extraction/extract-patterns.md, ../refinement/
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Consistency. Frontmatter name (pattern-extraction) differs from the folder (extraction)
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 94 steps
  • 100Execution cost. Instruction body is 2448 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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

  • +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
  • +5Description quotes 5 example trigger phrases
  • +3Description length 412: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 94 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.