SKILLEMALL.ai

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Security review workflow for OpenClaw skills and other small code folders. Use when auditing a skill before publishing or installing it, checking for dangerous code patterns, possible hardcoded secrets, risky file permissions, or lightweight supply-chain concerns. Best for quick static review and cautious go/no-go recommendations, not full malware analysis or sandbox forensics.

ClawHub Agent Skills author: ShadowLoong v1.0.0 MIT-0 3 files · 1 script body ≈ 1 012 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentInfrastructureSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
87
Run on models
none yet
Process rating
D
49/100
Unfinished process
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security skill-card.md:2
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Security review workflow for OpenClaw skills and other small code folders. Use when auditing a skill before publishing or installing it, checking for dangerous code patterns, possible hardcoded secret
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      description: Security review workflow for OpenClaw skills and other small code folders. Use when auditing a skill before publishing or installing it, checking for dangerous code patterns, possible har

    Files scanned: 3. 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
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 100Steps. 47 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1012 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 380: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 47 items
    • +4Has examples (4 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is a simple local security-scanning helper that inspects folders the user chooses and shows heuristic findings; no hidden network, persistence, or destructive behavior was found.
    LLM: benign (high) · VirusTotal: · 29 May 2026