SKILLEMALL.ai

BC code-analysis-toolkit

面向团队与企业的高级 Git 历史分析工具,在免费版基础上扩展团队复盘、同意管理、趋势追踪等能力。核心能力: - 团队复盘分析(需全员同意) - 同意管理与审计追踪 - 多仓库批量扫描与聚合报告 - 历史趋势追踪与基线对比 - 匿名化输出与隐私保护 适用场景: - 团队迭代复盘与改进 - 多仓库代码质量审计 - 代码质量趋势追踪 差异化: - 兼容免费版全部能力,无缝升级 - 支持团队复盘与同意管理 - 提供趋势追踪与基线对比 - 优先技术支持与更新通道

ClawHub Agent Skills author: 天轰穿 v1.0.1 MIT-0 2 files body ≈ 2 442 tokens Open the sourceclawhub.ai analyzed 2 d ago

面向团队与企业的高级 Git 历史分析工具,在免费版基础上扩展团队复盘、同意管理、趋势追踪等能力。核心能力: - 团队复盘分析(需全员同意) - 同意管理与审计追踪 - 多仓库批量扫描与聚合报告 - 历史趋势追踪与基线对比 - 匿名化输出与隐私保护 适用场景: - 团队迭代复盘与改进 - 多仓库代码质量审计 -…

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
55/100
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: 0. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "edition"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "suggested_price"
  • note frontmatter-key unknown frontmatter key "pricing_tier"
  • note frontmatter-key unknown frontmatter key "pricing_model"

Process rating: all ten parameters 55/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2442 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 11 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
  • +2Single-language instructions
  • +3Description length 231: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (16 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
The skill is mostly a disclosed Git analysis workflow, but it asks to analyze sensitive repository and author data while making conflicting local-only and LLM/API data-flow claims.
LLM: suspicious (high) · VirusTotal: · 24 Jul 2026