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AA improve-skill

Meta-skill: evaluate any Factory Droid skill against the current project codebase and suggest concrete improvements. Use when: a skill feels incomplete, produces suboptimal results, doesn't cover edge cases in the current project, or the user wants to tighten skill-project fit. Analyzes skill definition, supporting files, invocation history, and codebase structure to produce actionable upgrade recommendations.

modbender/skill-library-mcp Claude Code author: modbender MIT 1 file body ≈ 1 760 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Meta-skill: evaluate any Factory Droid skill against the current project codebase and suggest concrete improvements.

As a process A 84/100 · Runs to the end — weak spots: progress reporting

AnalyzerSoftware developmenttype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
A
84/100
Runs to the end
Progress reporting w 2
0
Failures and branches w 10
55
When it triggers w 12
70
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 84/100

    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 53 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1760 tokens
    • 100Running it twice. No mutating operations

    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 413: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 53 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.