SKILLEMALL.ai

AC setup-validator

Validate OpenClaw installation safety by checking for common security misconfigurations and setup issues. Use this skill during initial setup, periodic heartbeats, or whenever security validation is required. Checks include: excessive permissions, unsafe plugins, missing sandboxing, outdated dependencies, and more. Provides actionable warnings with fixes and clear documentation for resolving issues.

ClawHub Agent Skills author: MarcusY2k v1.0.1 MIT-0 6 files body ≈ 497 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

AnalyzerInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
88
Run on models
none yet
Process rating
C
57/100
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security references/CHECKS.md:11
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Potential for privilege escalation attacks.

    Files scanned: 6. 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 57/100

    • 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
    • 50Steps. 2 steps
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 497 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 402: enough signal without eating the budget
    • +4Structure: 11 headings
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a coherent OpenClaw setup checker, but users should run it deliberately because it may contact npm and its documentation includes optional cron and remediation commands.
    LLM: benign (high) · 28 May 2026