plugins/utopia-studio-cobuild-product/skills/prompt-engineer/SKILL.md
Makes a repository AI-native by creating AGENTS.md, .cursorrules, and documentation that helps any AI agent understand the codebase. Follows the WHAT/WHY/HOW framework from best practices. Use when the user asks to "make repo AI-ready", "add cursor rules", "create AGENTS.md", "set up for AI coding", or "make agents work better". Don't use for deployment (use deployment-engineer), monitoring (use monitoring-setup), or code organization (use repo-structurer).
npx skillsauth add The-Utopia-Studio/skills prompt-engineerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Makes your repository "AI-native" so any coding agent (Claude Code, Cursor, Copilot, Gemini CLI, Codex) can understand and work with your codebase effectively.
Source: HumanLayer - "Writing a good CLAUDE.md"
Claude Code injects CLAUDE.md with a system note: "this context may or may not be relevant to your tasks." This means agents WILL IGNORE instructions that aren't universally applicable. Only include information that's relevant to EVERY task.
# Identify tech stack
cat package.json 2>/dev/null | head -30
cat requirements.txt pyproject.toml 2>/dev/null | head -20
# Identify naming conventions
find . -name "*.ts" -not -path "*/node_modules/*" | head -5 | xargs head -20
# Identify file structure pattern
find . -type f -not -path "./.git/*" -not -path "*/node_modules/*" -not -path "*/.next/*" -maxdepth 3 | head -40
# Identify available scripts/commands
cat package.json 2>/dev/null | python3 -c "import sys,json; [print(f' {k}: {v}') for k,v in json.load(sys.stdin).get('scripts',{}).items()]" 2>/dev/null
# Check for existing agent config
ls AGENTS.md CLAUDE.md .cursorrules .cursor/rules/ .github/copilot/ 2>/dev/null
Framework: WHAT (tech & structure) → WHY (purpose) → HOW (commands & verification)
Generate this template filled with detected values:
# AGENTS.md
## Project Overview
{One sentence: what this project does and who it's for.}
## Tech Stack
- **Runtime:** {Node.js 20 / Python 3.11 / etc.}
- **Framework:** {Next.js 14 / FastAPI / etc.}
- **Database:** {PostgreSQL + Prisma / MongoDB + Mongoose / etc.}
- **Styling:** {Tailwind CSS / styled-components / etc.}
- **Auth:** {NextAuth / Clerk / Supabase Auth / etc.}
- **Hosting:** {Vercel / AWS / Railway / etc.}
## Commands
```bash
{package_manager} run dev # Start development server
{package_manager} run build # Production build
{package_manager} run test # Run tests
{package_manager} run lint # Run linter
{root}/
├── app/ # {description}
├── components/ # {description}
├── lib/ # {description}
├── prisma/ # {description}
└── public/ # {description}
prisma/schema.prisma without running npx prisma generate.env files — use .env.example for documentationany type in TypeScript — use proper types or unknown{package_manager} run build before committing to verify no errors
### Step 3: Create .cursorrules
```markdown
# {Project Name} — Cursor Rules
## Code Style
- Use {detected naming convention}
- Prefer {detected import style}
- {Framework-specific: e.g., "Use Server Components by default in Next.js App Router"}
- {Error handling: e.g., "Always use try/catch, never silently swallow errors"}
## File Organization
- New pages go in `app/{route}/page.tsx`
- Shared components go in `components/`
- Utility functions go in `lib/`
- API routes go in `app/api/{route}/route.ts`
## Do NOT
- Use `var` — use `const` or `let`
- Use `any` type — use proper types
- Put business logic in components — extract to `lib/`
- Commit without building first
Test by asking the agent: "What tech stack does this project use?"
## 🤖 AI-Readiness Report
### Files Created
| File | Purpose | Size |
|------|---------|------|
| `AGENTS.md` | Onboards any AI agent to the codebase (WHAT/WHY/HOW) | {N} lines |
| `.cursorrules` | Code style + framework rules for Cursor IDE | {N} lines |
| `.cursor/rules/{name}.mdc` | Scoped rules for specific directories | {N} lines |
### What Each File Teaches the Agent
- **AGENTS.md:** Tech stack ({stack}), commands ({N} scripts), structure ({N} directories), conventions ({N} rules), boundaries ({N} guardrails)
- **.cursorrules:** Code style ({N} rules), file organization ({N} patterns), anti-patterns ({N} "Do NOTs")
### Verification
- [x] AGENTS.md covers WHAT/WHY/HOW
- [x] Only universally-applicable instructions included (per HumanLayer best practice)
- [x] Commands listed early (per GitHub AGENTS.md analysis)
- [x] Boundaries set (what NOT to do)
- [ ] Test: Ask agent "What tech stack does this project use?" → should answer from AGENTS.md
development
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.
development
Updates a presentation with new numbers — quarterly refreshes, earnings updates, comp rolls, rebased market data. Use whenever the user asks to "update the deck with Q4 numbers", "refresh the comps", "roll this forward", "swap in the new earnings", "change all the $485M to $512M", or any request to swap figures across an existing deck without rebuilding it.
development
Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
tools
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.