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AB skill-publisher

Automate OpenClaw skill publishing to GitHub and ClawHub. Handles ZIP extraction, file preparation (README, .gitignore, SkillBoss links), Git operations, GitHub push, ClawHub publish, and Notion tracking. Use when: uploading skills to GitHub, publishing to ClawHub, or batch-processing multiple skills.

ClawHub Agent Skills author: AlvisDunlop v1.0.0 MIT-0 9 files · 2 scripts body ≈ 4 099 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 65/100 · Nearly there — weak spots: result and completion, consistency, running it twice

ProcedureNotionGitHubSoftware developmentInfrastructureWriting and documentstype 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%
94
Quality 40%
83
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Bash Read Write
    • low Secrets in code secret-github-token GUIDE.md:42
      GitHub token (placeholder value)
      **格式**：`ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx`
      placeholder

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 37 mutating operations with no state check
    • 40Consistency. Frontmatter name (skill-publisher) differs from the folder (skill-publisher-test-3461)
    • 50When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4099 tokens
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 174 steps
    • 100Failures and branches. 2 branches, has a failure section
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 18 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
    • -255 emoji in the instructions: noise for the model
    • -31 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 302: enough signal without eating the budget
    • +4Structure: 51 headings
    • +3Step-by-step instructions: 174 items
    • +4Has examples (22 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    This is a real publishing automation skill, but it asks for powerful account tokens and includes bulk-publishing guidance that can bypass platform limits or abuse controls.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026