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Offer your local changes back to the project — a journal profile you added, a checklist item you fixed, a skill you adapted to your department — as a pull request or an issue, without ever typing a git command. Detects what you changed against the installed version, scans it for patient data and identifiers, shows you every line, and sends nothing until you confirm. Also files feedback: a detector that fired wrongly, a step that failed on your file.

Aperivue/medsci-skills Agent Skills author: Aperivue MIT 8 files · 6 scripts body ≈ 2 863 tokens Open the sourcegithub.com↗ analyzed 32 h ago

Offer your local changes back to the project — a journal profile you added, a checklist item you fixed, a skill you adapted to your department — as a pull…

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerGitHubSoftware developmenttype 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
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

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: 7. 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")
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "tools"

Process rating: all ten parameters 56/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 30Running it twice. 10 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 38 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2863 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 453: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (6 code blocks)
  • +3All 5 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.