SKILLEMALL.ai
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BF chisel

Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).

simota/agent-skills Agent Skills author: simota 6 files body ≈ 5 888 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and…

As a process F 50/100 · Will not run — References files that are not bundled: _common/BOUNDARIES.md, _common/OPERATIONAL.md

GeneratorAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
F
50/100
Will not run
References files that are not bundled: _common/BOUNDARIES.md, _common/OPERATIONAL.md
Tools and files w 18
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. 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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5888 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: _common/BOUNDARIES.md
  • warning missing-ref reference to a missing file: _common/OPERATIONAL.md

Process rating: all ten parameters 50/100

Will not run. References files that are not bundled: _common/BOUNDARIES.md, _common/OPERATIONAL.md
  • 0Tools and files. 2 referenced file(s) missing: _common/BOUNDARIES.md, _common/OPERATIONAL.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 18 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Steps. 86 steps, 4 vague phrases
  • 70Execution cost. Instruction body is 5888 tokens
  • 100Result and completion. Output format and completion criterion are stated
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 16 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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 516: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 86 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.