.cursorrules git:20260306.88737f0A

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# AI Skills Library — Cursor Rules

## What This Repository Is

This is the universal AI skills library — 97 production-ready skill packages across 13 professional domains with 178 Python automation tools and 12 sample CI/CD workflows. It works with every major AI coding assistant.

This is NOT a traditional application. It's a library of self-contained skill packages meant to be extracted and deployed into user workflows.

## Repository Structure

- engineering-team/ — 24 core engineering skills + 50 Python tools
- engineering/ — 12 advanced architecture skills + 33 Python tools
- marketing-skill/ — 10 marketing skills + 6 Python tools
- product-team/ — 7 product skills + 6 Python tools
- project-management/ — 9 PM skills + 5 Python tools
- c-level-advisor/ — 5 C-level advisory skills + 4 Python tools
- ra-qm-team/ — 12 regulatory/quality skills + 10 Python tools
- business-growth/ — 3 business skills + 9 Python tools
- data-analytics/ — 5 data analytics skills
- hr-operations/ — 4 HR operations skills
- sales-success/ — 5 sales success skills
- finance/ — 1 finance skill + 4 Python tools
- scripts/ — Skill installer + utility scripts
- standards/ — Best practices library
- templates/ — Reusable templates

## Skill Package Pattern

Every skill follows this structure:
- SKILL.md — Master documentation with workflows
- scripts/ — Python CLI tools (standard library only, no ML/LLM calls)
- references/ — Expert knowledge bases
- assets/ — User-facing templates

Knowledge flows: references/ → SKILL.md workflows → scripts/ execution → assets/ templates.

## Navigation

For domain-specific guidance, see the CLAUDE.md in each domain folder:
- agents/CLAUDE.md — Agent creation
- engineering-team/CLAUDE.md — Engineering skills
- marketing-skill/CLAUDE.md — Marketing skills
- product-team/CLAUDE.md — Product skills
- project-management/CLAUDE.md — PM skills
- c-level-advisor/CLAUDE.md — C-level advisory
- ra-qm-team/CLAUDE.md — Regulatory compliance

## Code Style

- Python scripts: Standard library only, CLI-first with argparse, JSON + human-readable output
- Markdown: YAML frontmatter on all SKILL.md files, consistent formatting
- Commits: Conventional commits — feat(domain):, fix(tool):, docs(skill):

## Key Principles

1. Skills are products — Each skill is deployable as a standalone package
2. Documentation-driven — Clear, actionable docs over generic advice
3. Algorithm over AI — Deterministic analysis (Python scripts) over LLM calls
4. Template-heavy — Ready-to-use templates users customize
5. Self-contained — No dependencies between skills

## Anti-Patterns

- Creating dependencies between skills
- Adding complex build systems or test frameworks
- Generic advice instead of specific, actionable frameworks
- LLM/ML calls in scripts (defeats portability)
- Over-documenting file structure

## Quick Install

python scripts/skill-installer.py list
python scripts/skill-installer.py install <skill-name> --agent cursor
python scripts/skill-installer.py update