SKILLEMALL.ai
GitHub ↗ RU Sign in

AC open-source-analysis

Analyze an open source GitHub repository and generate a structured report. Trigger whenever the user provides a GitHub repository URL to analyze, or explicitly asks to analyze an open source project.

ClawHub Agent Skills author: Xudong Guo v1.0.2 MIT-0 4 files body ≈ 860 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerGitHubSoftware developmentData and analyticsInfrastructuretype 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%
77
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 Exfiltration net-credential-use SKILL.md:58
      Credential used in a network call (verify the destination is the intended service) (security demo / example; quoted — discussed, not commanded)
      - 在使用 `curl` 或其他工具调用 GitHub API 前，必须先检查环境变量 `GITHUB_TOKEN` 或是否已安装 `gh` CLI。如果存在 `GITHUB_TOKEN`，请在请求头中自动添加认证信息（例如：`-H "Authorization: Bearer $GITHUB_TOKEN"`）。如果安装了 `gh` CLI，优先使用 `gh api` 命令进行请求。
      demoquoted

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "environment_variables"

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 4 branches
    • 85Steps. 18 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 860 tokens
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    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
    • +4No input/output examples
    • -220 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 199: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 18 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

    External checks

    ClawHub: clean
    This is an instruction-only GitHub repository analysis skill with disclosed, purpose-aligned network and optional GitHub token use.
    LLM: benign (high) · VirusTotal: · 29 May 2026