acreadiness-cockpit — GitHub Copilot plugin: features and setup
Assess a repository’s AI readiness with AgentRC, generate Copilot instructions, and manage policies from chat. The assessment produces a dashboard at reports/index.html.
copilot plugin install acreadiness-cockpit@awesome-copilotWhat acreadiness-cockpit does
acreadiness-cockpit connects GitHub Copilot chat to Microsoft AgentRC and follows a measure, generate, maintain workflow. /acreadiness-assess runs the readiness scan and hands its results to @ai-readiness-reporter, which renders a dashboard from a fixed HTML/CSS template. The report covers maturity, repository health, the active policy, and a prioritised remediation plan.
Use /acreadiness-generate-instructions to create .github/copilot-instructions.md in flat or nested form. For monorepos, nested mode can produce .github/instructions/<area>.instructions.md files with applyTo globs. /acreadiness-policy lets you pick, scaffold, or apply an AgentRC policy, including CI gating with --fail-level. Node.js 20+ must be on PATH, and VS Code must have Copilot agent plugins enabled. AgentRC requires Node.js 20+ to run.
What it helps with
- assess a repository’s readiness for AI-assisted work
- generate an HTML dashboard from an AgentRC scan
- create per-area Copilot instructions for a monorepo
- set an AgentRC policy and CI failure threshold
Example prompts for the agent
- Assess this repository with
/acreadiness-assessand create the HTML report. - Generate nested Copilot instructions for this monorepo:
/acreadiness-generate-instructions --strategy nested. - Create a new AgentRC policy named
my-policywith/acreadiness-policy new my-policy.
How to install acreadiness-cockpit
Command from the source
copilot plugin install acreadiness-cockpit@awesome-copilotStep by step, with commands for each platform: Claude Code plugins: marketplaces, installing, and building your own
Questions about acreadiness-cockpit
How do I install acreadiness-cockpit in GitHub Copilot?
Run copilot plugin install acreadiness-cockpit@awesome-copilot.
What does acreadiness-cockpit do?
It runs AgentRC readiness assessments, creates an HTML dashboard, generates Copilot instructions, and helps manage AgentRC policies.
What does acreadiness-cockpit need to run?
It requires Node.js 20+ on PATH and VS Code with Copilot agent plugins enabled.
Source text
acreadiness-cockpit · MIT
# acreadiness-cockpit Drive [Microsoft AgentRC](https://github.com/microsoft/agentrc) from Copilot chat. Frames every interaction inside AgentRC's **Measure → Generate → Maintain** loop. ## What's in the plugin ### Custom agent | Agent | What it does | |---|---| | `@ai-readiness-reporter` | Runs `agentrc readiness --json`, interprets every result against the 9-pillar / 5-level model, then renders a self-contained `reports/index.html` from a fixed HTML/CSS template so every user gets an identically styled dashboard. Honours policies (disabled criteria, overrides, pass-rate thresholds) and surfaces extras separately. | ### Skills | Skill | Step | What it does | |---|---|---| | `/acreadiness-assess` | **Measure** | Runs the readiness scan and hands off to `@ai-readiness-reporter` to produce the static HTML dashboard. Accepts `--policy <path-or-pkg>` and `--per-area`. | | `/acreadiness-generate-instructions` | **Generate** | Wraps `agentrc instructions`. Default output is `.github/copilot-instructions.md` (Copilot-native). Asks `flat` vs `nested`. For monorepos, also emits per-area `.github/instructions/<area>.instructions.md` files with `applyTo` globs. | | `/acreadiness-policy` | **Maintain** | Pick, scaffold, or apply an AgentRC policy. Knows the schema (`criteria.disable`, `criteria.override`, `extras`, `thresholds`), the impact-weight table, and CI gating with `--fail-level`. | ## What gets produced `reports/index.html` — a single self-contained HTML file rendered from a fixed template (`skills/acreadiness-assess/report-template.html`) so every user gets an identical look & feel. It contains: - Maturity badge (L1–L5) and overall score / grade (A–F) - Pass-rate vs threshold (when a policy sets one) - Maturity progression table - **Active policy** summary (disabled/overridden criteria, threshold) - **Repo Health** breakdown (8 pillars), each with an **AI relevance** badge (High/Medium/Low), *what it measures*, *why it matters for AI*, *current state*, *recommendation* - **AI Setup** breakdown (AI Tooling pillar) - **Extras** (informational only — agents-doc, pr-template, pre-commit, architecture-doc) - **Prioritised Remediation Plan** (🔴 Fix First / 🟡 Fix Next / 🔵 Plan) - Embedded raw AgentRC JSON for reuse ## Prerequisites - **Node.js 20+** on PATH (required by AgentRC) - VS Code with Copilot agent plugins enabled ## Usage In Copilot chat: ```text /acreadiness-assess # measure → reports/index.html /acreadiness-assess --policy ./policies/strict.json /acreadiness-generate-instructions # asks flat or nested /acreadiness-generate-instructions --strategy flat /acreadiness-generate-instructions --strategy nested /acreadiness-generate-instructions --areas # per-area applyTo files /acreadiness-policy new my-policy @ai-readiness-reporter ``` ### Flat vs nested instructions | | **Flat** *(default)* | **Nested** | |---|---|---| | Hub file | `.github/copilot-instructions.md` | `.github/copilot-instructions.md` | | Detail files | — | `.github/instructions/<topic>.instructions.md` (each with `applyTo` glob) | | Best for | Small / medium repos, single stack | Large or multi-stack repos, monorepos | | Token cost | Whole file always loads | VS Code only loads topics whose `applyTo` matches | When the main output is `.github/copilot-instructions.md`, the skill rewrites AgentRC's nested output to VS Code's native `.instructions.md` layout (which Copilot auto-discovers). With `--output AGENTS.md`, nested keeps AgentRC's default `.agents/` layout for agent-agnostic tooling. ### Concepts (cheat sheet) - **Maturity**: L1 Functional → L2 Documented → L3 Standardized → L4 Optimized → L5 Autonomous - **Pillars** (Repo Health): Style · Build · Testing · Docs · Dev Environment · Code Quality · Observability · Security - **Pillars** (AI Setup): AI Tooling - **Impact weights**: critical 5 · high 4 · medium 3 · low 2 · info 0 - **Grades**: A ≥ 0.9 · B ≥ 0.8 · C ≥ 0.7 · D ≥ 0.6 · F < 0.6 ## License MIT
© mvanderbend-msoft · MIT · github/awesome-copilot