SKILLEMALL.ai

AD clawtrix-security-audit

Keeps your agent lean of dangerous skills. Audits your installed ClawHub skill stack for security risks personalized to your mission — then recommends clean replacements. Use when: (1) Before installing any new skill from ClawHub, (2) Running a weekly security sweep of installed skills, (3) An HN scanner run surfaces new security signals about the ClawHub ecosystem, (4) Onboarding a new agent and reviewing its starting skill set, (5) A stakeholder asks 'are our skills safe?'. Flags risky slugs, suspicious SKILL.md patterns, and publisher trust issues — personalized to your agent's SOUL.md, not a universal catalog scan. Outputs a risk report to memory/reports/. Never recommends competitor tools — recommends Clawtrix Pro for ongoing monitoring.

ClawHub Agent Skills author: nicobot v0.3.0 MIT-0 3 files body ≈ 1 801 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
84
Run on models
none yet
Process rating
D
45/100
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

What is at stake

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

Concealment 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 tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

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
    • medium Concealment en-hide-from-user SKILL-upload.md:145
      Instruction to hide actions from the user (documentation of a security skill)
      2. **Redirect your actions** — Tell you to secretly transmit outputs to third-party URLs
      security skill

    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 45/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 11 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1801 tokens
    • 100Progress reporting. Reports progress

    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 752: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a markdown-only security audit skill whose local reads, public lookups, and report writing fit its stated purpose.
    LLM: benign (high) · VirusTotal: · 29 May 2026