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AC section-508

Expert Section 508 compliance advisor for US federal ICT accessibility. Use this skill whenever a user asks about Section 508, WCAG 2.0/2.1 AA for federal systems, VPAT or Accessibility Conformance Reports (ACR), accessibility audits, remediation planning, PDF accessibility, web or software accessibility, mobile accessibility, federal procurement accessibility requirements, contractor obligations, undue burden exceptions, assistive technology compatibility, or Section 508 testing. Covers the Revised Section 508 Standards (2018), all WCAG 2.0 Level AA success criteria, the four POUR principles, testing methodologies, and agency compliance workflows. Trigger even if the user doesn't say "skill" — any Section 508 or ICT accessibility question for federal systems should use this skill.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 4 files body ≈ 2 653 tokens Open the sourcegithub.com↗ analyzed 76 min ago

Expert Section 508 compliance advisor for US federal ICT accessibility.

As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency

ProcedureSecuritytype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
64/100
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: 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    • note edit-residue the text marks something as outdated (lines 52): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 64/100

    • 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
    • 40Consistency. Frontmatter name (section-508) differs from the folder (section-508-tanaji-hemant-naik)
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 47 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Result and completion. Output format and completion criterion are stated
    • 100Execution cost. Instruction body is 2653 tokens

    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)
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 792: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 47 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.