HVE Core is a collection of agents, prompts, coding instructions, and skills for building repeatable software-development workflows with GitHub Copilot. It is intended for individuals and teams that want structured AI-assisted research, planning, implementation, and review, while the catalogue entries provide many of its reusable workflow components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add microsoft/hve-core --skill accessibility-coverage-matrixgit clone --depth 1 https://github.com/microsoft/hve-coreWrote 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.
[](https://agentmods.dev/skills/microsoft/hve-core/accessibility-coverage-matrix)<a href="https://agentmods.dev/skills/microsoft/hve-core/accessibility-coverage-matrix"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/accessibility-coverage-matrix/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.
<a href="https://agentmods.dev/skills/microsoft/hve-core/accessibility-coverage-matrix"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/accessibility-coverage-matrix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01798 |
| Opus 5.5 | $0.00017 | $0.00719 |
| Sonnet 5.5 | $0.00008 | $0.00360 |
| Haiku 4.5 | $0.00004 | $0.00180 |
Grade C, and why
accessibility-coverage-matrix scanned grade C with 1 finding 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 1mo 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- cspell:ignore testplan --> How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Accessibility Coverage Matrix
Inputs
scope: Required. Repository or target scope to assess.frameworks: Optional. Comma-separated framework set to evaluate. Defaults towcag-22,aria-apg,coga,section-508,en-301-549.mode: Optional. Matrix execution mode. Allowed values arebuild,refresh,report, orprobe. Defaults tobuild.baseUrl: Optional. Base URL for runtime probing. If omitted, derive it from the target configuration or ask the user.serve: Optional. Serve mode for the harness. Allowed values areautoorexternal. Defaults toauto.
Core model
Treat the matrix as a criterion x surface x method grid.
- Criterion: framework-specific success criteria or control identifiers.
- Surface: discrete UI surfaces such as a page, component, widget, global chrome, or content type.
- Method: evidence method such as static-source, axe-auto, runtime-automation, manual-keyboard, cognitive-walkthrough, screen-reader, or other method names recorded by the engine.
- Cell lifecycle:
not-started -> blocked, partial, fail, pass, or not-applicablebased on newly ingested evidence and method adequacy. - Method-adequacy semantics: a cell is counted as covered only when the winning evidence method is one of the criterion's
adequateMethodsin the reviewed criteria catalog. A pass from an inadequate method does not count as covered. The probe-criteria-map records which criteria a probe reports on. It never authorizes coverage on its own.
Required steps
- Bootstrap or resume the matrix under
.copilot-tracking/accessibility/coverage/and create or update the working artifacts for the target scope. - Load the criteria catalog and
adequateMethodsfrom theaccessibilityskill framework references before evaluating any cells. - Delegate to the Accessibility Surface Inventory subagent as the sole producer of
a11y-runtime.config.json. Do not author that config yourself. Pause for the user to review or override it before proceeding. - Build the grid with the
runtime_a11ymatrix engine, using the loaded framework and surface definitions. - Ingest existing and static evidence as data, including assessor findings, planner
state.jsondata, prior reports, and prior matrix artifacts. Preserve provenance and do not invent evidence. - Load the
accessibilityskill and run the runtime probe harness from that skill's root, which holds the uv project, using its script entrypoint:uv run scripts/runtime_a11y/__main__.py run-all --config a11y-runtime.config.json --out results.jsonfor normal runs.uv run scripts/runtime_a11y/__main__.py probe <probeId> --config ...for probe mode.- From another working directory, pin the project instead:
uv run --project <skill-root> <skill-root>/scripts/runtime_a11y/__main__.py .... - Install the skill-local Node dependencies once with
npm ciinscripts/runtime_a11y/under the loaded skill root before the first run. - Add
--tracewhen a trace is needed and--allow-externalonly when the target is an approved non-loopback host after explicit confirmation.
- Route fail, partial, or blocked cells through the Finding Deep Verifier. Involve the Codebase Profiler and Accessibility Framework Assessor by role as needed to interpret findings and close gaps.
- Compute coverage, residual gaps, and
nextActionswith the engine, then write the matrix JSON and render the canonical evidence bundle. When a cell still requires human-led assistive-technology evidence, route the tester to the shared real screen reader testing runbook instead of writing case-specific manual instructions inline.uv run scripts/runtime_a11y/__main__.py render-artifacts --matrix coverage-matrix-{repo-slug}.json --output-dir .copilot-tracking/accessibility/coverage --repo-slug {repo-slug}- Preserve every file listed by the generated artifact manifest. Do not hand-author the EARL or manual test-plan files.
- Present the coverage summary, EARL result counts, pending manual test count, and artifact manifest path. The Markdown artifacts retain the canonical accessibility disclaimer and unchecked human-review checkbox.
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.
- 1mo ago First seen · 89 lines · 42 tokens per session scan C 672bc41899b8
accessibility-coverage-matrix is a skill published in the GitHub repository microsoft/hve-core (1,606 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 1,798 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-08.
accessibility-coverage-matrix compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| accessibility-coverage-matrixmicrosoft/hve-core | Skill | 1.6k | today | MIT | C | 42 | Claude Code |
| verify-omnigentomnigent-ai/omnigent | Skill | 11k | today | Apache-2.0 | A | 100 | Claude Code, Codex |
| test-masterJeffallan/claude-skills | Skill | 12k | 9d ago | MIT | A | 104 | Claude Code |
| eval-dataset-designbojieli/ai-agent-book | Skill | 57k | 1mo ago | Apache-2.0 | A | 138 | Claude Code, Codex |
| dogfoodcallstack/agent-device | Skill | 5.3k | today | MIT | A | 55 | Claude Code |
| cua-driverdavidondrej/skills | Skill | 4.3k | today | MIT | A | 68 | Claude Code, Codex |
| test-warp-uiwarpdotdev/warp | Skill | 66k | yesterday | AGPL-3.0 | A | 55 | Claude Code |