Maintain Accessibility Skills logo

Maintain Accessibility Skills

Organization
zivtech
maintain-accessibility-skills

Safely maintain the public accessibility-skills repository. Use when adding tracked-file hygiene scanners, proving whether reset or rebase work was lost, splitting mixed changes into focused commits, or recovering review-worthy commits made on the local default branch.

Overview

Publisherzivtech
Repositoryaccessibility-skills
Skill namemaintain-accessibility-skills
Stars
12
Forks
2
Bundled files
1
LicenseGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zivtech on GitHub. Read the source before you install it.

Installation

Install the Maintain Accessibility Skills AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zivtech/accessibility-skills/tree/main/.agents/skills/maintain-accessibility-skills
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zivtech/accessibility-skills.git /tmp/accessibility-skills
mkdir -p .claude/skills
cp -r /tmp/accessibility-skills/.agents/skills/maintain-accessibility-skills .claude/skills/maintain-accessibility-skills
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maintain Accessibility Skills in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Maintain Accessibility Skills on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Maintain Accessibility Skills is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Maintain Accessibility Skills

Apply the matching workflow below. Preserve the exact checkout and dirty baseline before changing history or staging files.

Add a tracked-file hygiene scanner

  1. Define the scanner's boundary as Git-tracked files.
  2. Exclude the scanner's own path if its source must contain the forbidden pattern.
  3. Stage the scanner, then run it. An untracked scanner cannot prove its behavior after commit.
  4. Manually inspect the commit message, pull request body, and issue comments because the tracked-file gate cannot see them.

Do not claim a local pass proves the committed gate works unless the scanner was staged during the test.

scripts/check_doc_budget.py is an instance of this pattern for a size boundary rather than a content pattern: it gates the byte size of the always-on context files (CLAUDE.md, AGENTS.md), which regrew from ~13KB to 33KB of inline eval-result and provenance narrative before being cut back. It follows the same shape (report-only by default, --strict exits 1, --self-test proves the thresholds fire) and is wired into ci.yml. Content policy it backstops: dated eval results, model-row deltas, and phase-by-phase provenance belong in docs/*-adoption-assessment.md or evals/results/*/README.md, cited by a one-line pointer — never inline.

Prove history-rewrite safety

  1. Locate the exact reset, rebase, or rewrite operation in the reflog.
  2. Identify the commit immediately before the operation.
  3. Diff that tree against the current tree.
  4. Describe the observed delta precisely before saying no work was lost.

A clean status and empty stash list do not prove a history rewrite was safe.

Split mixed work into focused commits

  1. Read every changed and untracked file before proposing the split.
  2. Back up files that contain more than one theme.
  3. Split mixed files at hunk level instead of assigning the whole file to the first theme.
  4. Run project validators on new fixtures and data before committing them.
  5. Compare the final committed state with the backup to detect dropped or duplicated content.
  6. Run the full applicable suite after the split is complete.

Recover commits made on the default branch

  1. Create a feature branch at the current tip.
  2. Push the feature branch and open the pull request.
  3. After the commits are safe on the remote, remain on the feature branch while moving the local default-branch ref back to its remote-tracking ref.
  4. Check the pull request's remote CI status.

Do not push review-worthy commits directly to the shared default branch. Do not treat local tests as a substitute for the remote gate.

Guard generated and frozen files

Generated or frozen files — receipts, freeze snapshots, hash-bound manifests, rendered reports — carry a hash or signature that a later hand-edit silently invalidates: the file still looks fine, but the chain it anchored is now a lie. Treat this as a repo trap, not a competence failure — more than one actor hitting the same trap is a property of the repo, and the guard is the warning the repo otherwise lacks.

  1. List the project's frozen paths in a manifest (frozen-files.manifest), one repo-relative path per line — see scripts/frozen-files.manifest.example for the format.
  2. Wire scripts/check_frozen_files.py --staged into pre-commit and --range origin/main into CI, so an edit to any listed path fails closed before it lands.
  3. When a frozen file legitimately changes, re-freeze it from stable sources and update the manifest in the same commit — never hand-edit the frozen artifact in place.

Keep the manifest project-specific: never ship a real project's frozen-path list inside a shared skill bundle — only the .example template travels.

Gate state changes on evidence, not assertions

A work item — a fix closed, an operation retested, a check passed — advances state only when a machine check confirms the named, cross-referencing artifacts exist, never on a prose claim that it was verified. The weakest form ("the log says verified") is exactly what a critic pass flags CRITICAL; the strongest form found requires three artifacts that each name the others — a captured file on disk, a structured log containing a required section, and a record entry naming that exact file — with a hard non-zero exit on any miss.

  1. State the artifacts a passed item must produce and how they cross-reference (each names the others).
  2. Write the check — roughly twenty lines of shell is enough; it is a precondition gate, not a runtime — confirming each artifact exists and the cross-references resolve, exiting non-zero on any miss.
  3. Run it as the gate the state transition depends on, not as an after-the-fact report; an item that cannot produce the artifacts does not advance.

For remediation specifically, the artifacts are the fix-closure record's fields (docs/a11y-fix-closure-contract.md): a "fixed" claim without the class-matched interaction evidence that record requires does not close.

Bar for promoting an engagement pattern to a skill rule

An observation from one engagement becomes a general skill rule only when it clears every clause below. This bar sits beside any capture program's gate/score framework, not instead of it, and the two-reproduction clause is binding.

  1. Two independent reproductions — the pattern seen on two distinct surfaces, components, or engagements. A single-source claim drives a doc edit at most, never a skill-behavior change, until a fixture reproduces the behavior — the fixture is the second reproduction, so it lands before the skill text.
  2. Written negative space — state what the rule does NOT cover with the same specificity as what it does.
  3. A BUG/CLEAN fixture pair, or a named manual protocol where a fixture is not possible — the rule must be falsifiable and must leave clean cases unflagged.
  4. Mirrored skill edits — every surface (skill, .agents mirror, and any embedded agent def) changed in the same commit, with check_mirrors.py --strict green.
  5. Targeted checks run and shown, not asserted.
  6. Explicit user approval and a critic acceptance pass before the rule is called promoted.

Do not let a favorable score override a failed clause: a high generality score is not a substitute for the second reproduction, and a single-source claim never ships skill text on score alone.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Maintain Accessibility Skills AI skill do?

Safely maintain the public accessibility-skills repository. Use when adding tracked-file hygiene scanners, proving whether reset or rebase work was lost, splitting mixed changes into focused commits, or recovering review-worthy commits made on the local default branch.

Why use Maintain Accessibility Skills on TypingMind?

Because you install it once and use it with any model. Maintain Accessibility Skills is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Maintain Accessibility Skills in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zivtech/accessibility-skills/tree/main/.agents/skills/maintain-accessibility-skills. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Maintain Accessibility Skills?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Maintain Accessibility Skills?

As many as you like. As long as a model supports skills, you can use Maintain Accessibility Skills with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Maintain Accessibility Skills AI skill free?

Yes. It is published on GitHub by zivtech under the GPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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