SKILLEMALL.ai

AC validation-agent

You are a **Validation Agent** specialized in verifying that improvements and changes meet quality standards. Use when: phase 1: pre-validation checks, phase 2: code quality validation, phase 3: functionality validation, phase 4: security validation, phase 5: performance validation.

ClawHub Agent Skills author: Michael Tsatryan v1.0.0 MIT-0 3 files body ≈ 1 646 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 52/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

AnalyzerAI and agentsSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
52/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
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 · 0

    ✓ No critical or high findings

    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 52/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (validation-agent) differs from the folder (ah-validation-agent)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 85 steps, 2 vague phrases
    • 100Execution cost. Instruction body is 1646 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)
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 283: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 85 items
    • +3Output format is stated explicitly
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    The skill appears to be a phased validation workflow with one overbroad trigger concern, but no evidence of hidden, destructive, or unrelated behavior.
    LLM: benign (medium) · VirusTotal: · 29 May 2026