SKILLEMALL.ai

AB guardrail-testing

Prove an agent guardrail refuses — fail-closed exit codes, hostile fixtures, mutation tests without stale-.pyc false greens. Use when adding a gate or filter. Runs your tests, writes temp files, mutates code only in a mktemp copy, never in place. Trigger on "test my guardrail", "does this gate fail closed", "mutation testing", "the test passes but the guard is broken".

ClawHub Agent Skills author: Alexandre Bloch v1.0.0 MIT-0 3 files · 1 script body ≈ 3 688 tokens Open the sourceclawhub.ai↗ analyzed 11 d ago

Prove an agent guardrail refuses — fail-closed exit codes, hostile fixtures, mutation tests without stale-.pyc false greens. Use when adding a gate or filter.…

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, running it twice

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
95
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Failures and branches w 10
50
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 Exfiltration net-credential-use SKILL.md:214
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      # Output: ./bin/bad.…url -sS -H "Authorization: Bearer $TOKEN" ...  (exit 2); missing dir: scanned 0 files (exit 3)
      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 65/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 6 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python, node) 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
    • 100Steps. 6 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3688 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill

    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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 371: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 6 items
    • +3Output format is stated explicitly
    • +4Has examples (13 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: clean
    This skill is a local guardrail mutation-testing helper whose command execution, temporary file writes, and repo copying are disclosed and aligned with its purpose.
    LLM: benign (high) · VirusTotal: · 30 Sept 2026