Skip to content
cccskills
FreePublished on GitHub

acreadiness-generate-instructions

Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.

OriginalShow the original description

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.

See how to install

Good for

  • Create project instructions for VS Code Copilot
  • Refresh agent rules after project changes
  • Split monorepo instructions by project area
  • Prepare AGENTS.md for multiple coding agents

Files included (1)

  • SKILL.md8.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

/acreadiness-generate-instructions — write AI agent instructions

Use this skill whenever the user wants to create, regenerate, or refresh their custom instructions for AI coding agents (Copilot, Claude, etc.). This is the Generate step in AgentRC's Measure → Generate → Maintain loop and the single highest-leverage action for the AI Tooling pillar.

Output options

VS Code recognises several instruction file types — AgentRC generates the most common ones:

FileScopeWhen to use
.github/copilot-instructions.mdAlways-on, whole workspaceDefault — VS Code Copilot's native instruction file
AGENTS.mdAlways-on, whole workspaceMulti-agent repos (Copilot + Claude + others)
.github/instructions/*.instructions.mdScoped by applyTo globPer-area / per-language rules in monorepos
CLAUDE.mdClaude-specificAdd via --claude-md (nested only)

Strategies

  • flat (default) — single .github/copilot-instructions.md at the chosen path. Simple, easy to review.
  • nested — hub at .github/copilot-instructions.md + per-topic detail files at .github/instructions/<topic>.instructions.md, each with an applyTo glob so VS Code only loads the topic when it's relevant. Better for large or multi-stack repos.

Why .github/instructions/ and not .agents/? AgentRC's default nested layout writes to .agents/, which is the right home for agent-agnostic repos (Copilot + Claude + Cursor reading AGENTS.md). For VS Code Copilot specifically, the native location is .github/instructions/ with applyTo frontmatter — that's what Copilot auto-discovers. This skill rewrites AgentRC's nested output to the VS Code-native location whenever the main output is .github/copilot-instructions.md. If you instead chose --output AGENTS.md, nested keeps AgentRC's default .agents/ layout.

For monorepos, generate area-scoped instructions with --areas, --area <name>, or --areas-only. Areas are defined in agentrc.config.json. Per-area output is written as VS Code .instructions.md files with an applyTo glob (see below).

Topic vs area .instructions.md files

Both end up in .github/instructions/ but they answer different questions:

KindFilename exampleapplyTo exampleWhere it comes from
Topic (nested)testing.instructions.md**/*.{test,spec}.{ts,tsx,js}AgentRC --strategy nested topic split
Area (monorepo)frontend.instructions.mdapps/frontend/**agentrc.config.json areas + --areas

You can have both at once: a nested set of topic files plus per-area files for a monorepo.

Per-area files with applyTo

When the user opts into areas, emit one VS Code-native .instructions.md file per area at .github/instructions/<area>.instructions.md. Each file MUST start with frontmatter declaring the glob the rules apply to:

---
applyTo: "apps/frontend/**"
---

# Frontend area instructions

…AgentRC-generated content for this area…

Workflow:

  1. Read agentrc.config.json to discover declared areas and their paths / globs. If paths is missing, ask the user for the glob (e.g. src/api/**).
  2. Run agentrc instructions --areas (or --area <name>) to produce the per-area body content.
  3. Wrap each area's content in .github/instructions/<area>.instructions.md with the applyTo frontmatter taken from the area's paths. If the user passed --apply-to <glob> on a single-area call, use that glob verbatim.
  4. Leave the main file alone — the root .github/copilot-instructions.md stays as the always-on instructions; .instructions.md files only kick in for matching paths.

Naming: lowercase, kebab-case area name. Examples: .github/instructions/frontend.instructions.md, .github/instructions/api.instructions.md, .github/instructions/infra.instructions.md.

Steps

  1. Pick the target file. Default to .github/copilot-instructions.md. Switch to AGENTS.md only if the user mentions multi-agent / Claude / Cursor support.
  2. Always ask which strategy to use — flat or nested — unless the user already specified one in their message or via --strategy. Present the trade-off briefly:
    • Flat (default) — one .github/copilot-instructions.md. Simple, easy to review in a single PR. Best for small/medium repos with one stack.
    • Nested — hub .github/copilot-instructions.md + per-topic .github/instructions/<topic>.instructions.md files (each with an applyTo glob so VS Code only loads them when relevant). Best for large or multi-stack repos. Add --claude-md to also emit CLAUDE.md. Recommend nested proactively when the repo has > 5 top-level directories, multiple stacks, or already uses a monorepo tool (turbo/nx/pnpm workspaces).
  3. Detect monorepo areas by reading agentrc.config.json. If areas exist, ask the user whether they want per-area .instructions.md files with applyTo in addition to the root file. Default to "yes" when agentrc.config.json declares areas.
  4. Run dry-run first so the user can preview:
    npx -y github:microsoft/agentrc instructions --output <file> --strategy <flat|nested> [--areas|--area <name>] [--claude-md] --dry-run
    
  5. Show a short summary of what would change — files that would be created or overwritten, area count + their applyTo globs, model used (default claude-sonnet-4.6).
  6. On confirmation, run the same command without --dry-run (and optionally --force if files already exist).
  7. Post-process layout for Copilot output:
    • If --output ends in copilot-instructions.md and strategy is nested: move/rewrite AgentRC's .agents/<topic>.md files to .github/instructions/<topic>.instructions.md. Add frontmatter to each file with an appropriate applyTo glob (see "Topic applyTo defaults" below). Delete the now-empty .agents/ directory.
    • If --areas was used: also write .github/instructions/<area>.instructions.md for every area, using each area's paths from agentrc.config.json as the applyTo glob (override with --apply-to for single-area calls).
    • If --output AGENTS.md was chosen: keep AgentRC's native .agents/ layout for nested — agent-agnostic readers expect it there. Create the .github/instructions/ directory if missing.

Topic applyTo defaults

When promoting AgentRC's nested topic files to .instructions.md, use these defaults unless the user specifies otherwise:

TopicDefault applyTo
testing**/*.{test,spec}.{ts,tsx,js,jsx,mjs,cjs}
style / code-quality / formatting**/*.{ts,tsx,js,jsx,mjs,cjs,py,go,rs,java,kt,cs}
build / ci**/{package.json,turbo.json,nx.json,.github/workflows/**}
docs**/*.md
security**
anything else / hub-level**
  1. Verify by reading the generated file(s) back and showing the user a 1-paragraph synopsis: stack detected, conventions captured, length, list of .instructions.md files with their globs.
  2. Suggest next steps:
    • Re-run the assess skill to confirm the AI Tooling pillar score improved.
    • If the user already has both copilot-instructions.md and AGENTS.md, recommend consolidating to a single source of truth (AgentRC flags this at maturity Level 2+).

Notes

  • AgentRC reads your actual code — no templates. Output reflects detected languages, frameworks, and conventions.
  • --claude-md (nested strategy only) also emits CLAUDE.md.
  • VS Code applies .instructions.md files automatically when the active file matches applyTo. The root .github/copilot-instructions.md always loads.
  • Never run this skill non-interactively in CI; instructions are part of the repo and should land via PR.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.

  • Get oriented in an unfamiliar codebase
  • Prepare documentation for a project handover
  • Compare documented architecture with implementation
github/awesome-copilot40.2KUpdated Oct 17, 2026

Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.

  • Assessing a repository’s readiness for AI agents
  • Prioritizing improvements from assessment results
  • Reviewing scores under an organization’s own policy
github/awesome-copilot40.2KUpdated Oct 17, 2026

Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.

  • Set shared readiness standards across an organization
  • Focus assessments on AI tooling
  • Adjust the importance of individual checks
github/awesome-copilot40.2KUpdated Oct 17, 2026

Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.

  • Identify ads and keywords to pause
  • Find campaigns and audiences worth scaling
  • Assess whether A/B test results are meaningful
github/awesome-copilot40.2KUpdated Oct 17, 2026

Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.

  • Add beginner-friendly explanations to existing code
  • Document the reasoning behind syntax and design choices
  • Adjust comment depth for readers with different experience
github/awesome-copilot40.2KUpdated Oct 17, 2026

Create and fix Adobe Illustrator automation scripts using ExtendScript, Adobe’s JavaScript-based scripting language. Automate changes to artwork, text, layers, and artboards, process multiple files, and prepare exports such as PDF, SVG, and PNG.

  • Automate repeated changes to artwork and text
  • Export multiple Illustrator files
  • Generate artwork variations from data
github/awesome-copilot40.2KUpdated Oct 17, 2026

See all skills by github

Report a problem with this skill