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每日 Skill 自动工厂 — 让 openclaw 和 Claude 完全自主地调研、设计、生成、测试并发布全新 skill，全程零人工介入。内置 10 阶段流水线（Research → Design → SEO → Create → Review → Self-Run → Self-Check → Upload → Verify → Final Review），每天凌晨 02:00 自动选题跑完整流程，输出推送到 GitHub 和 clawHub 的生产级 skill。也可手动指定 idea 触发，或单独调用某一阶段进行调试/迭代。支持用自身流水线对已有 skill 做升级、SEO 优化和重新发布。Use it when the user asks to auto-generate a skill, check daily pipeline status, iterate an existing skill, or publish to GitHub and clawHub.

ClawHub Agent Skills author: Cosmos Fang v1.4.1 MIT-0 22 files body ≈ 3 100 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions

AnalyzerGitHubInfrastructureMarketingAI and agentstype 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%
98
Quality 40%
77
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
60
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 · 2

    ✓ No critical or high findings

    Medium and low: 2

    ✓ Guard found no suspicious behaviour. 2 matches are attack strings quoted in this security skill's own documentation.

    Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "keywords"
    • note frontmatter-key unknown frontmatter key "requirements"

    Process rating: all ten parameters 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, web, git) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 45 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3100 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 14 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (18 tags): a typed call is more reliable

    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
    • -219 emoji in the instructions: noise for the model
    • -31 of 16 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 446: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 45 items
    • +4Has examples (10 code blocks)

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

    External checks

    ClawHub: suspicious
    This skill is a disclosed automation factory for creating and publishing skills, but it can use your GitHub and ClawHub accounts for unattended public publishing and daily scheduled runs.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026