SKILLEMALL.ai

AC skill-policy-enforcer

Use when checking an agent Skill against a local or enterprise policy before installation, publication, CI approval, marketplace review, or repository merge.

ClawHub Agent Skills author: CUPVC v1.0.0 MIT-0 6 files body ≈ 480 tokens Open the sourceclawhub.ai analyzed 2 d ago

Enforce a caller-supplied policy over an agent Skill folder.

As a process C 56/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerAI and agentsSoftware developmentCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
90
Quality 40%
94
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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.

Instruction override 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 text contains phrases like "ignore previous instructions" or "you are now…". That is an attempt to hijack the agent: it may break your rules, the system limits or company policy.

For the author

An honest skill does not need them: state the role and the rules directly without overriding other instructions. Otherwise catalog scanners and corporate filters will block the listing.

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 Instruction override en-ignore-previous references/default-policy.yaml:30
      Instruction-override phrase ("ignore previous instructions") (quoted — discussed, not commanded)
      - "ignore previous instructions"
      quoted
    • medium Instruction override en-ignore-previous scripts/enforce_skill_policy.py:37
      Instruction-override phrase ("ignore previous instructions") (detector / deny-list definition)
      "warn_patterns": [r"\bTODO\b", r"placeholder", r"ignore previous instructions", r"reveal secrets"],
      detector

    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 56/100

    • 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. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 18 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 480 tokens

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 157: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 18 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: clean
    This skill is a local policy checker for agent skill folders and does not show hidden network access, persistence, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 18 Jun 2026