SKILLEMALL.ai
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AC who-is-actor

This skill should be used when the user wants to analyze a Git repository and profile each developer's commit habits, work habits, development efficiency, code style, code quality, and engagement index — all without installing any extra packages or running any custom scripts. It relies purely on native git CLI commands (and standard Unix text-processing utilities already present on the host) and AI-driven interpretation. Trigger phrases include "analyze repository" "profile developers" "commit habits" "developer report card" "代码分析" "研发效率" "开发者画像" "提交习惯" "工作习惯" "参与度".

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 5 files body ≈ 6 435 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill should be used when the user wants to analyze a Git repository and profile each developer's commit habits, work habits, development efficiency…

As a process C 64/100 · Has gaps — weak spots: result and completion

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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Tools and files w 18
60
Steps w 15
60
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.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6435 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 64/100

  • 0Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash, web, git, node) that frontmatter does not declare
  • 60Steps. 137 steps, 5 vague phrases
  • 65Failures and branches. 3 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6435 tokens
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -220 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 573: enough signal without eating the budget
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 137 items
  • +4Has examples (14 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.