# AI Skills Library — Cursor Rules

> **Note:** Cursor now prefers per-rule files in `.cursor/rules/*.mdc` (Project Rules).
> This `.cursorrules` file is kept for backward compatibility; see `.cursor/rules/` for the
> scoped, auto-attaching rules. Keep both in sync when counts or structure change.

## What This Repository Is

This is the universal AI skills library — **339 production-ready skill packages across 17 professional domains** with **787 Python automation tools**, 76 agents (incl. 8 cross-domain personas), 27 slash commands, and 8 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 (domain → skill count)

- engineering/ — 82 skills (architecture, fullstack, DevOps, security, AI/ML, data, agent tooling)
- project-management/ — 66 skills (discovery, delivery, career, strategy-frameworks/, gtm/, Jira/Linear/Notion)
- marketing/ — 39 skills (content, SEO/AEO, demand gen, brand, analytics)
- c-level-advisor/ — 31 skills (CEO/CTO/CFO/CMO/CRO/CPO/COO/CHRO/CISO + CAIO/CDO/CCO/GC/VPE)
- ra-qm-team/ — 27 skills (ISO 13485, MDR, FDA, SOC 2, GDPR, EU AI Act, NIS2, DORA, NIST CSF)
- business-growth/ — 20 skills (CRO, pricing, revenue ops, commercial)
- legal/ — 17 skills (EXPERIMENTAL: contracts, NDA, privacy, DPIA, breach response)
- product-team/ — 13 skills (RICE, OKRs, UX research, product analytics, Apple HIG)
- personal-productivity/ — 10 skills (resume, leads, meetings, invoices, email triage)
- vertical-advisors/ — 7 skills (fintech, healthtech, edtech, ecommerce, proptech, climate-tech, marketplace)
- data-analytics/ — 5 skills (analysis, BI, data science, ML ops)
- sales-success/ — 5 skills (account exec, sales ops, solutions architect)
- documents/ — 4 skills (docx, pdf, pptx, xlsx — stdlib OOXML)
- hr-operations/ — 4 skills (talent, people analytics, HRBP)
- research/ — 4 skills (litreview, grants, patent, dossier)
- finance/ — 3 skills (financial analysis, DCF, forecasting)
- workflow/ — 2 skills (skill-router, handoff — meta-skills for routing & context handoff)

Supporting: `scripts/` (manifest builder + installer), `standards/` (best-practices library), `templates/` (reusable templates + sample workflows), `agents/` (cs-* domain agents + personas), `cli/skills.json` (machine-readable manifest).

## Skill Package Pattern

Every skill follows this structure:
- SKILL.md — Master documentation with workflows (progressive disclosure: lean Tier-1, detailed references on demand)
- 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.

## 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, descriptions ≤ ~60 tokens
- 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

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

After adding/removing/renaming any skill, regenerate the manifest: `python scripts/build_manifest.py`.