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AD github-stars-analyzer

抓取指定 GitHub 用户 Stars 下的所有项目，并生成标准化中文 Markdown 报告。当用户提到"分析 GitHub stars"、"导出收藏项目"、"汇总 GitHub 星标"、"生成 stars 报告"，或粘贴包含 ?tab=stars 的 GitHub 链接时，必须触发此技能。始终通过 bash_tool 运行 Python 脚本完成任务，不要使用浏览器 Artifact 或 web_fetch 抓取 GitHub 数据。

ClawHub Agent Skills author: blckrabbit v0.0.1 MIT-0 4 files body ≈ 563 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubInfrastructureSoftware 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
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
76
Run on models
none yet
Process rating
D
41/100
Unfinished process
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security SKILL.md:66
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | 🔒 安全与隐私 | security, hacking, pentest, crypto, privacy, auth, vulnerability |

Files scanned: 4. 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")

Process rating: all ten parameters 41/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (github-stars-analyzer) differs from the folder (yardor)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 12 steps
  • 100Execution cost. Instruction body is 563 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

  • +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
  • +5Description quotes 4 example trigger phrases
  • +3Description length 222: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 12 items
  • +4Has examples (3 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: clean
The skill does what it claims: it fetches a GitHub user's starred repositories and writes a Markdown report, with a caution around optional token handling.
LLM: benign (high) · VirusTotal: · 29 May 2026