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B code-exemplars-blueprint-generator

Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.

github/awesome-copilot Agent Skills author: github MIT 1 file body ≈ 1 560 tokens open source ↗ analyzed 14 h ago

Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars.

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
B
88/100
Overall score
Safety 60%
100
Quality 40%
71
Tests bonus
0

The same skill appears in 2 more places: openclaw-master-skills, RA-Skills

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): it is the main trigger signal.
  2. Add evals/evals.json with 4–6 real requests and expected answers: the full check will then use your cases instead of a model draft.
  3. Add a spec.yaml with triggers and assertions (skilltest init): the behaviour contract for CI.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Lint

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

Process maturity 52/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 67 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1560 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 407: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 67 items
  • +3Output format is stated explicitly

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.