Planv1.0.0

Core Principle

Some tasks fail when rushed. Recognize when one-shot execution will underdeliver, and choose a slower process that guarantees success.

This skill auto-evolves: learn which tasks need plans, which don't, and which planning strategies work for each type of goal.

Check strategies.md for planning approaches. Check outcomes.md for tracking and learning.

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The Planning Decision

Before executing, ask:

| Signal | One-shot OK | Plan needed |

|--------|-------------|-------------|

| Task done before successfully | ✅ | |

| Clear single deliverable | ✅ | |

| Reversible if wrong | ✅ | |

| Multiple components | | ✅ |

| Dependencies between steps | | ✅ |

| High stakes / hard to redo | | ✅ |

| Ambiguous success criteria | | ✅ |

| Estimated >30 min work | | ✅ |

Default: When uncertain, plan. A quick plan costs minutes; a failed one-shot costs hours.

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Plan Depth Levels

| Level | When | Format |

|-------|------|--------|

| L0 | Trivial, done before | No plan, just execute |

| L1 | Simple, low risk | Mental checklist, no doc |

| L2 | Medium complexity | Bullet list, share with human |

| L3 | Complex, multi-step | Detailed plan with milestones |

| L4 | High stakes, novel | Full plan + human validation required |

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Plan Format (L2-L4)

📋 Plan: [Goal]

Context: [Why this needs planning]

Steps:
1. [Step] — [output/checkpoint]
2. [Step] — [output/checkpoint]
3. [Step] — [output/checkpoint]

Risks:
- [Risk] → [mitigation]

Estimated time: [X hours/days]
Validation needed: [Yes/No]

Ready to start?

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Validation Learning

Track which plan types need human validation:

### Auto-Execute (no validation needed)
- refactor/small: L2 plans [10+ successful]
- deploy/staging: L2 plans [15+ successful]

### Validate First
- feature/new: L3+ plans [human wants to review scope]
- migration/data: L4 plans [high risk]

### Learning
- api/integration: testing L2 auto-execute [3/5 runs]

Promotion rule: After 5+ successful auto-executes of a plan type, confirm: "Should I auto-start [type] plans without validation?"

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Outcome Tracking

After each planned task completes, record:

## [Date] [Task Type]
- Plan level: L3
- Strategy: [approach used]
- Outcome: ✅ success | ⚠️ partial | ❌ failed
- Lesson: [what worked/didn't]
- Adjustment: [change for next time]

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Strategy Learning

Different goals need different planning strategies. Track what works:

### Code Features
- ✅ Works: API design first, then implementation
- ❌ Failed: Parallel implementation without interface agreement
- Adjustment: Always define interfaces before coding

### Migrations  
- ✅ Works: Dry-run → staged rollout → full
- ❌ Failed: Big bang migration without rollback plan
- Adjustment: Always require rollback step in migration plans

### Research
- ✅ Works: Timeboxed exploration with checkpoints
- ❌ Failed: Open-ended research without scope limits
- Adjustment: Always set max time and output format upfront

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Plan Refinement

Plans should get better over time. Track patterns:

Length optimization:

• Task type X: L4 plans were overkill → demote to L3
• Task type Y: L2 plans missed edge cases → promote to L3

Component optimization:

• Always include [X] for [task type] — helped 5+ times
• Skip [Y] for [task type] — never used, wasted time

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Anti-Patterns

| Don't | Do instead |

|-------|------------|

| Plan everything | Learn what doesn't need planning |

| Same plan depth for all tasks | Adapt depth to task type |

| Ignore failed plans | Track outcomes, adjust strategy |

| Over-plan familiar tasks | Demote plan level after successes |

| Under-plan novel tasks | Default to higher plan level |

| Static planning approach | Evolve strategy per task type |

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Empty tracking sections = early stage. Execute, track outcomes, learn. The goal is adaptive planning that matches effort to need.