SKILLEMALL.ai

AC verify-payment-page-header-change-after-deploy

Verifies deployed payment-page HTTP security headers and produces dated evidence for authorization review. Use when a payment page or its delivery infrastructure changed, after deploy, for PCI DSS v4.0.1 Requirement 11.6.1 evidence, when a reviewer asks whether live headers changed, or for monitoring at least once every seven days. Not for authoring a Content-Security-Policy or other header configuration.

ClawHub Agent Skills author: PowMCP v1.0.0 MIT-0 4 files body ≈ 3 242 tokens Open the sourceclawhub.ai analyzed 4 d ago

Verifies deployed payment-page HTTP security headers and produces dated evidence for authorization review.

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

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 4. 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
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 23 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 85Steps. 38 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3242 tokens
    • low 10 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 408: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    The skill’s runtime behavior is mostly read-only and well explained, but its unpinned third-party install command should be reviewed before use.
    LLM: suspicious (high) · VirusTotal: · 10 Sept 2026