spec

# Spec - Progressive Disclosure Specification

## Core Philosophy

**Write what's needed. Skip what's not.**

Most specs fail because they're either:
- Too thin (unclear, leads to rework)
- Too thick (nobody reads them, decisions buried in prose)

This skill routes you to the right depth:
- **Quick task?** → Write a clear issue
- **Feature?** → Write a lite PRD
- **AI feature?** → Add context requirements and behavior examples

The templates are already excellent. This skill helps you use them.

---

## Linear Method Principles

These principles guide every level:

1. **Issues, not user stories** - Plain language wins. "Add export button to dashboard" beats "As a user, I want to export data so that I can..."

2. **Scope down** - If it can't be done in 1-3 weeks by 1-3 people, break it down further.

3. **Short specs get read** - Long specs get skipped. Write for clarity, not completeness.

4. **Prototype > documentation** - A working demo + 3 paragraphs beats a 10-page spec.

5. **Make decisions, not descriptions** - Every section should decide something.

**See:** `skills/spec/references/philosophy.md` for the full philosophy.

---

## Entry Point

When this skill is invoked, start with:

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 SPEC
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

What are you speccing?

  1. Quick task (hours to days)
     → Clear title + optional description
     → If it fits in one sentence, just write an issue

  2. Feature (1-3 weeks)
     → Problem, solution, success metric, scope
     → Use what's helpful, skip the rest

  3. AI feature (any size)
     → Core AI questions + context requirements + behavior examples
     → Evals are non-negotiable. Model costs early.

  4. Not sure
     → Tell me what you're building, I'll help you decide

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

**Parse intent from context:**
- If user says "issue" or mentions a quick task → Level 1
- If us