Skill: ivangdavila/plan (version 1.0.0)

--- name: Plan description: Auto-learns when to plan vs execute directly. Adapts planning depth to task type. Improves strategy through outcome tracking. --- ## 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. --- ## 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. --- ## 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 | --- ## 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? ``` --- ## 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?" --- ## 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] ``` --- ## 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 ``` --- ## 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 --- ## 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 | --- *Empty tracking sections = early stage. Execute, track outcomes, learn. The goal is adaptive planning that matches effort to need.*


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