SKILLEMALL.ai

AC design-system

Captures the user's brand identity once via a 10-question onboarding wizard (primary/accent HEX + heading + body Google Fonts + design style editorial/technical/minimal/playful + default output directory + syntax theme + TOC behavior + optional logo/company), validates body-text and link contrast against WCAG 2.2 AA, derives 12 CSS custom properties in HSL space, and stores the result for every markdown-html converter to consume. Use before any markdown-html conversion. Triggers on first-run onboarding ("set up the brand", "configure markdown-html", "run onboarding"), on explicit reset ("reset the design system", "re-onboard"), and is checked by every converter via config_loader.py before rendering. Refuses to save if body-text contrast fails AA 4.5:1 or the output dir isn't writable. Precedence is project (./.markdown-html/) > global (~/.config/markdown-html/) > built-in defaults; MARKDOWN_HTML_NO_CONFIG=1 bypasses.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 8 files body ≈ 2 503 tokens Open the sourcegithub.com analyzed 2 d ago

Captures the user's brand identity once via a 10-question onboarding wizard (primary/accent HEX + heading + body Google Fonts + design style…

As a process C 61/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerDesigntype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
61/100
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

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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "compatible_tools"

    Process rating: all ten parameters 61/100

    • 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, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 35 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2503 tokens
    • 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)
    • +3Description length 930: 120–800 characters recommended
    • -5TODO / placeholder text left in the skill
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 35 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.