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AD shipcheck

Pre-ship safety net. Scan an npm package, OpenClaw skill folder, or git repo BEFORE publishing to catch personal info leaks (absolute home paths, emails, internal IPs / Tailscale hostnames, private project codenames, AWS/GitHub/OpenAI/Anthropic/Slack tokens, JWT, PEM/SSH keys, soft Chinese first-person NL leaks in markdown). Best-effort PII/secret checkup — NOT a replacement for manual review or a full secret scanner. Use when the user is about to `npm publish` / `clawhub publish` / push a public GitHub repo, or asks "is it safe to share this?", "check leaks", "扫一下个人信息", "发布前体检", "shipcheck".

ClawHub Agent Skills author: SymbolStar v0.1.0 MIT-0 2 files body ≈ 985 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerGitHubSlackAWSSoftware developmentInfrastructureAI 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
D
49/100
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

    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: 2. 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 49/100

    • 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
    • 30Running it twice. 14 mutating operations with no state check
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 985 tokens
    • low The response is described with custom markup (3 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 599: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a disclosed pre-publish leak-checking skill; the main caution is that it installs an external npm CLI and may surface sensitive findings in scan output.
    LLM: benign (medium) · VirusTotal: · 29 May 2026