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Add acreadiness-cockpit plugin (AgentRC measure -> generate -> maintain) 🤖🤖🤖 (#1593)

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* Add acreadiness-cockpit plugin

Adds a new plugin that drives Microsoft AgentRC from Copilot chat,
framing every interaction inside AgentRC's Measure -> Generate ->
Maintain loop.

Custom agent (agents/ai-readiness-reporter.agent.md):
  Runs `agentrc readiness --json`, interprets every result against
  the 9-pillar / 5-level maturity model, then renders a self-contained
  reports/index.html from a fixed HTML/CSS template (bundled with the
  acreadiness-assess skill) so every user gets an identically styled
  dashboard. Honours policies (disabled criteria, overrides, pass-rate
  thresholds) and surfaces extras separately.

Skills:
  - acreadiness-assess: Measure step. Wraps `agentrc readiness --json`
    and hands off to the @ai-readiness-reporter agent. Bundles the
    canonical report-template.html.
  - acreadiness-generate-instructions: Generate step. Wraps
    `agentrc instructions`. Defaults to .github/copilot-instructions.md
    (Copilot-native). Asks flat vs nested. For monorepos, emits per-area
    .github/instructions/<area>.instructions.md files with applyTo
    globs taken from agentrc.config.json.
  - acreadiness-policy: Maintain step. Helps pick, scaffold, or apply an
    AgentRC policy (criteria.disable, criteria.override, extras,
    thresholds) and wire it into CI via --fail-level.

Plugin (plugins/acreadiness-cockpit/):
  Declarative plugin.json referencing the agent and three skills.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address PR review feedback

- Align documented slash-command names with plugin manifest:
  /acreadiness-assess, /acreadiness-generate-instructions,
  /acreadiness-policy (was /assess, /generate-instructions, /policy
  inside SKILL bodies and argument-hints).
- Move the literal % from the report template into the substituted
  values for {{passRate}} and {{threshold}} so an N/A value of '—'
  no longer renders as '—%'. Updated the agent placeholder contract
  accordingly.
- Point the report footer at the canonical plugin folder under
  github/awesome-copilot instead of the personal source fork.
- Add explicit HTML-escaping rules to the agent: HTML-escape every
  {{placeholder}} substitution, and replace </script with <\/script
  inside the embedded JSON block so untrusted repo content cannot
  break the markup or inject scripts.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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2026-05-04 06:11:14 +02:00
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@@ -28,6 +28,9 @@ See [CONTRIBUTING.md](../CONTRIBUTING.md#adding-skills) for guidelines on how to
| Name | Description | Bundled Assets |
| ---- | ----------- | -------------- |
| [acquire-codebase-knowledge](../skills/acquire-codebase-knowledge/SKILL.md)<br />`gh skills install github/awesome-copilot acquire-codebase-knowledge` | Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery. | `assets/templates`<br />`references/inquiry-checkpoints.md`<br />`references/stack-detection.md`<br />`scripts/scan.py` |
| [acreadiness-assess](../skills/acreadiness-assess/SKILL.md)<br />`gh skills install github/awesome-copilot acreadiness-assess` | Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo. | `report-template.html` |
| [acreadiness-generate-instructions](../skills/acreadiness-generate-instructions/SKILL.md)<br />`gh skills install github/awesome-copilot acreadiness-generate-instructions` | Generate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar. | None |
| [acreadiness-policy](../skills/acreadiness-policy/SKILL.md)<br />`gh skills install github/awesome-copilot acreadiness-policy` | Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation. | None |
| [add-educational-comments](../skills/add-educational-comments/SKILL.md)<br />`gh skills install github/awesome-copilot add-educational-comments` | Add educational comments to the file specified, or prompt asking for file to comment if one is not provided. | None |
| [adobe-illustrator-scripting](../skills/adobe-illustrator-scripting/SKILL.md)<br />`gh skills install github/awesome-copilot adobe-illustrator-scripting` | Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices. | `references/object-model-quick-reference.md`<br />`scripts/batch-export-png.jsx`<br />`scripts/create-color-grid.jsx`<br />`scripts/find-replace-text.jsx` |
| [agent-governance](../skills/agent-governance/SKILL.md)<br />`gh skills install github/awesome-copilot agent-governance` | Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when:<br />- Building AI agents that call external tools (APIs, databases, file systems)<br />- Implementing policy-based access controls for agent tool usage<br />- Adding semantic intent classification to detect dangerous prompts<br />- Creating trust scoring systems for multi-agent workflows<br />- Building audit trails for agent actions and decisions<br />- Enforcing rate limits, content filters, or tool restrictions on agents<br />- Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen) | None |