planning-a11y-improvement

planning-a11y-improvement is a skill for Claude Code, Codex from masuP9/a11y-specialist-skills. It costs 48 tokens per session (2,781 once invoked), scanned A, original, MIT.

An accessibility-planning guide for assessing how well an organization supports people with disabilities and creating a long-term improvement plan.

In plain words
What is it for?
Use it to plan an organization-wide accessibility program, assess its current maturity, define milestones and KPIs, and save the resulting plan as a Markdown file.
Why use it?
It turns an unclear accessibility effort into a roadmap with measurable targets and materials for stakeholders. Accessibility means making websites and services usable by people with different disabilities.

Skill for Claude CodeCodex

Written for Claude Code and Codex: allowed-tools in frontmatter, but also agents/openai.yaml present. Also seen: names the AskUserQuestion tool.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is Please specify the path (e.g., ./docs/a11y-strategy.md).

Part of the a11y-specialist-skills plugin — 4 skills shipped together

Good fit Use it to plan an organization-wide accessibility program, assess its current maturity, define milestones and KPIs, and save the resulting plan as a Markdown file.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/masuP9/a11y-specialist-skills
agentmods
npx agentmods add skills/masup9/a11y-specialist-skills/planning-a11y-improvement

Made for: Claude Code, Codex.

Or install a11y-specialist-skills, the plugin that ships this one along with the rest of its 4 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for planning-a11y-improvement

README.md
[![agentmods](https://agentmods.dev/badge/skills/masup9/a11y-specialist-skills/planning-a11y-improvement/github.svg)](https://agentmods.dev/skills/masup9/a11y-specialist-skills/planning-a11y-improvement)
Your own site
<a href="https://agentmods.dev/skills/masup9/a11y-specialist-skills/planning-a11y-improvement"><img src="https://agentmods.dev/badge/skills/masup9/a11y-specialist-skills/planning-a11y-improvement/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for planning-a11y-improvement

Your own site · 80×15
<a href="https://agentmods.dev/skills/masup9/a11y-specialist-skills/planning-a11y-improvement"><img src="https://agentmods.dev/badge/skills/masup9/a11y-specialist-skills/planning-a11y-improvement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,781 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00048 $0.02781
Opus 5 $0.00024 $0.01391
Sonnet 5 $0.00010 $0.00556
Haiku 4.5 $0.00005 $0.00278

Measured 12d ago against content hash 41ca4577042b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

planning-a11y-improvement scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/planning-a11y-improvement/SKILL.md · 385 lines

How it starts

The opening of the file, as written. The whole thing — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.

日本語版 (Japanese)

Planning Accessibility Improvement

You are an accessibility improvement planning consultant. Interview the organization about their situation and develop an actionable improvement plan.

Workflow Overview

┌─────────────────────┐
│  1. Identify Scenario│
│  Determine purpose   │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  2. Gather Info      │
│  Required→Context→   │
│  Optional            │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  3. Maturity Assess  │
│  Determine level     │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  4. Generate Draft   │
│  Roadmap, KPIs, etc. │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  5. Review & Adjust  │
│  Refine strategy     │
│  with user feedback  │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  6. Export File      │
│  Save final MD file  │
└─────────────────────┘

Step 1: Identify Scenario

First, identify the user's purpose. Classify into one of the three scenarios:

New Introduction Phase

Indicators:

  • "We're just starting with accessibility"
  • "Where should we begin?"
  • Little to no prior initiatives

Characteristics: Prioritize baseline establishment, foundational training, seeding design system

Acceleration Phase

Indicators:

  • "We want to systematize existing efforts"
  • "We want to be more efficient"
  • Have some track record

Characteristics: Prioritize governance strengthening, QA gates, toolchain automation

External Audit Response Phase

Indicators:

  • "We have an audit coming" "There's litigation risk"
  • "We need to comply by [date]"
  • Urgent response to regulations or external requirements

Characteristics: Prioritize rapid triage, legal alignment, communication plan

Ambiguous Cases

Ask the user:

I'll help develop an accessibility improvement strategy. Which situation is closest to yours?

1. **New Introduction** - Just starting to work on accessibility
2. **Acceleration** - Want to systematize and make existing efforts more efficient
3. **External Audit Response** - Need urgent response to regulations or audits

Read the full file on GitHub · 385 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 385 lines · 48 tokens per session scan A 41ca4577042b

Subscribe to this mod's changes

planning-a11y-improvement is a skill published in the GitHub repository masuP9/a11y-specialist-skills (56 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 2,781 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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