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AC global-skill-daily

每 3 天扫描 ClawHub + SkillHub 做 10 维度推荐并三处存放。可选扫描 TRAE memory 用于推荐，但派生报告零上下文派生内容，只发布聚合计数，原始扫描仅存本地。当用户说「全球 skill 日报」「global skill daily」时触发。v1.5.0 自依赖改造：仅依赖 Python 标准库，无需第三方包或外部 CLI（lark-cli 改为可选降级）。Do NOT use for 发布新 skill / 修改已装 skill / skill 安全审计 / 本地 skill 生态分析 / 被动 skill 发现。

ClawHub Agent Skills author: AI花生 v1.5.0 MIT-0 12 files body ≈ 1 870 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

每 3 天扫描 ClawHub + SkillHub 做 10 维度推荐并三处存放。可选扫描 TRAE memory 用于推荐，但派生报告零上下文派生内容，只发布聚合计数，原始扫描仅存本地。当用户说「全球 skill 日报」「global skill daily」时触发。v1.5.0 自依赖改造：仅依赖…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationWordObsidianSoftware 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
59/100
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
    • 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

    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "displayName"
    • note frontmatter-key unknown frontmatter key "summary"

    Process rating: all ten parameters 59/100

    • 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. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 29 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1870 tokens

    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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 276: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 29 items
    • +4Has examples (4 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: suspicious
    The skill’s main workflow is disclosed, but it scans sensitive local context by default and publishes reports that still contain context-derived signals despite claiming otherwise.
    LLM: suspicious (high) · 20 Jul 2026