SKILLEMALL.ai

AC coverage-analysis

Measure and improve test coverage meaningfully. Covers Istanbul/V8/coverage.py configuration, coverage gap analysis by risk, coverage-as-ratchet in CI (never let it decrease), PR coverage diff checks, mutation testing for assertion quality, and distinguishing meaningful from vanity coverage. Use when: "code coverage," "coverage gap," "Istanbul," "coverage threshold," "coverage report," "branch coverage." Not for: writing the tests that raise coverage — use unit-testing; coverage as a tracked KPI trend over time — use qa-metrics. Related: unit-testing, ci-cd-integration, qa-metrics, ai-qa-review.

petrkindlmann/qa-skills Agent Skills author: petrkindlmann MIT 4 files body ≈ 4 508 tokens Open the sourcegithub.com analyzed 2 d ago

Measure and improve test coverage meaningfully.

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

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
98
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

    • note edit-residue the text marks something as outdated (lines 58, 71, 152): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    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. 14 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, node) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4508 tokens
    • 100Steps. 32 steps
    • 100Consistency. Name and required fields are in place
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 13 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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

    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 602: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 98.