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AB ruff-recursive-fix

Run Ruff checks with optional scope and rule overrides, apply safe and unsafe autofixes iteratively, review each change, and resolve remaining findings with targeted edits or user decisions.

github/awesome-copilot Agent Skills author: github MIT 1 file body ≈ 1 489 tokens Open the sourcegithub.com↗ analyzed 13 h ago

Run Ruff checks with optional scope and rule overrides, apply safe and unsafe autofixes iteratively, review each change, and resolve remaining findings with…

As a process B 66/100 · Nearly there — weak spots: when it triggers, progress reporting

AnalyzerAI and agentsPersonal productivitySoftware 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
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
B
66/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: RA-Skills

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: 1. 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 66/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 60Failures and branches. 2 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 67 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1489 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (11 tags): a typed call is more reliable

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 190: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 67 items
  • +3Output format is stated explicitly
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.