Coding With Ai Planning To Production Ai Skillset Course

Production-grade engineering skills for AI coding agents. Skills encode the workflows, quality gates, and best practices that senior engineers use when building software. These ones are packaged so AI agents follow them consistently across every phase of development.

DEFINE PLAN BUILD VERIFY REVIEW SHIP ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ Idea │ ───▶ │ Spec │ ───▶ │ Code │ ───▶ │ Test │ ───▶ │ QA │ ───▶ │ Go │ │Refine│ │ PRD │ │ Impl │ │Debug │ │ Gate │ │ Live │ └──────┘ └──────┘ └──────┘ └──────┘ └──────┘ └──────┘ /spec /plan /build /test /review /ship 7 slash commands that map to the development lifecycle. Each one activates the right skills automatically.

Skills also activate automatically based on what you're doing — designing an API triggers api-and-interface-design , building UI triggers frontend-ui-engineering , and so on. Claude Code (recommended) Marketplace install: /plugin marketplace add addyosmani/agent-skills /plugin install agent-skills@addy-agent-skills SSH errors? The marketplace clones repos via SSH.

If you don't have SSH keys set up on GitHub, either add your SSH key or switch to HTTPS for fetches only: git config --global url."https://github.com/".insteadOf "git@github.com:" Local / development: git clone https://github.com/addyosmani/agent-skills.git claude --plugin-dir /path/to/agent-skills Cursor Copy any SKILL.md into .cursor/rules/ , or reference the full skills/ directory. See docs/cursor-setup.md. Gemini CLI Install as native skills for auto-discovery, or add to GEMINI.md for persistent context. See docs/gemini-cli-setup.md.

Install from the repo: gemini skills install https://github.com/addyosmani/agent-skills.git --path skills Install from a local clone: gemini skills install ./agent-skills/skills/ Windsurf Add skill contents to your Windsurf rules configuration. See docs/windsurf-setup.md. OpenCode Uses agent-driven skill execution via AGENTS.md and the skill tool. GitHub Copilot Use agent definitions from agents/ as Copilot personas and skill content in .github/copilot-instructions.md . See docs/copilot-setup.md. Codex / Other Agents Skills are plain Markdown - they work with any agent that accepts system prompts or instruction files. See docs/getting-started.md. The commands above are the entry points.

Under the hood, they activate these 20 skills — each one a structured workflow with steps, verification gates, and anti-rationalization tables. You can also reference any skill directly.

Pre-configured specialist personas for targeted reviews: Quick-reference material that skills pull in when needed: Every skill follows a consistent anatomy: ┌─────────────────────────────────────────────┐ │ SKILL.md │ │ │ │ ┌─ Frontmatter ─────────────────────────┐ │ │ │ name: lowercase-hyphen-name │ │ │ │ description: Use when [trigger] │ │ │ └───────────────────────────────────────┘ │ │ │ │ Overview → What this skill does │ │ When to Use → Triggering conditions │ │ Process → Step-by-step workflow │ │ Rationalizations → Excuses + rebuttals │ │ Red Flags → Signs something's wrong │ │ Verification → Evidence requirements │ └─────────────────────────────────────────────┘ Key design choices: - Process, not prose.

Skills are workflows agents follow, not reference docs they read. Each has steps, checkpoints, and exit criteria. - Anti-rationalization. Every skill includes a table of common excuses agents use to skip steps (e.g., "I'll add tests later") with documented counter-arguments. - Verification is non-negotiable. Every skill ends with evidence requirements - tests passing, build output, runtime data. "Seems right" is never sufficient. - Progressive disclosure. The SKILL.md is the entry point. Supporting references load only when needed, keeping token usage minimal.

agent-skills/ ├── skills/ # 20 core skills (SKILL.md per directory) │ ├── idea-refine/ # Define │ ├── spec-driven-development/ # Define │ ├── planning-and-task-breakdown/ # Plan │ ├── incremental-implementation/ # Build │ ├── context-engineering/ # Build │ ├── source-driven-development/ # Build │ ├── frontend-ui-engineering/ # Build │ ├── test-driven-development/ # Build │ ├── api-and-interface-design/ # Build │ ├── browser-testing-with-devtools/ # Verify │ ├── debugging-and-error-recovery/ # Verify │ ├── code-review-and-quality/ # Review │ ├── code-simplification/ # Review │ ├── security-and-hardening/ # Review │ ├── performance-optimization/ # Review │ ├── git-workflow-and-versioning/ # Ship │ ├── ci-cd-and-automation/ # Ship │ ├── deprecation-and-migration/ # Ship │ ├── documentation-and-adrs/ # Ship │ ├── shipping-and-launch/ # Ship │ └── using-agent-skills/ # Meta: how to use this pack ├── agents/ # 3 specialist personas ├── references/ # 4 supplementary checklists ├── hooks/ # Session lifecycle hooks ├── .claude/commands/ # 7 slash commands └── docs/ # Setup guides per tool AI coding agents default to the shortest path - which often means skipping specs, tests, security reviews, and the practices that make software reliable.

Agent Skills gives agents structured workflows that enforce the same discipline senior engineers bring to production code. Each skill encodes hard-won engineering judgment: when to write a spec, what to test, how to review, and when to ship. These aren't generic prompts - they're the kind of opinionated, process-driven workflows that separate production-quality work from prototype-quality work. Skills bake in best practices from Google's engineering culture — including concepts from Software Engineering at Google and Google's engineering practices guide.

You'll find Hyrum's Law in API design, the Beyonce Rule and test pyramid in testing, change sizing and review speed norms in code review, Chesterton's Fence in simplification, trunk-based development in git workflow, Shift Left and feature flags in CI/CD, and a dedicated deprecation skill treating code as a liability. These aren't abstract principles — they're embedded directly into the step-by-step workflows agents follow.

Skills should be specific (actionable steps, not vague advice), verifiable (clear exit criteria with evidence requirements), battle-tested (based on real workflows), and minimal (only what's needed to guide the agent). See docs/skill-anatomy.md for the format specification and CONTRIBUTING.md for guidelines. MIT - use these skills in your projects, teams, and tools.

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