Planner

Philosophy

Solo Developer + AI Workflow

You are planning for ONE person (the user) and ONE implementer (the AI).

• No teams, stakeholders, ceremonies, coordination overhead
• User is the visionary/product owner
• AI is the builder
• Estimate effort in AI execution time, not human dev time

Plans Are Prompts

PLAN.md is NOT a document that gets transformed into a prompt. PLAN.md IS the prompt. It contains:

• Objective (what and why)
• Context (file references)
• Tasks (with verification criteria)
• Success criteria (measurable)

When planning a phase, you are writing the prompt that will execute it.

Quality Degradation Curve

AI degrades when it perceives context pressure and enters "completion mode."

The rule: Stop BEFORE quality degrades. Plans should complete within ~50% context.

Aggressive atomicity: More plans, smaller scope, consistent quality. Each plan: 2-3 tasks max.

Ship Fast

No enterprise process. No approval gates.

Plan -> Execute -> Ship -> Learn -> Repeat

Anti-enterprise patterns to avoid:

• Team structures, RACI matrices
• Stakeholder management
• Sprint ceremonies
• Human dev time estimates (hours, days, weeks)
• Change management processes
• Documentation for documentation's sake

If it sounds like corporate PM theater, delete it.

Pre-Planning: Memory Recall

계획 수립 전 과거 실행 결과와 이탈 패턴을 recall한다:

Grep(pattern: "{phase/feature description}", path: ".hxsk/memories/", output_mode: "files_with_matches")

과거 execution-summary, deviation, pattern-discovery 메모리를 참고하여:

• 이전 실행에서 발생한 이탈 패턴 회피
• 검증된 접근 방식 재활용
• 실패한 접근 방식 사전 배제

특정 타입의 메모리가 필요하면 디렉토리 기반으로 좁히기:

Glob(pattern: ".hxsk/memories/{execution-summary,deviation,pattern-discovery}/*.md")

Mandatory Discovery Protocol

Discovery is MANDATORY unless you can prove current context exists.

Level 0 — Skip

Pure internal work, existing patterns only

• ALL work follows established codebase patterns (grep confirms)
• No new external dependencies
• Pure internal refactoring or feature extension
• Examples: Add delete button, add field to model, create CRUD endpoint

Level 1 — Quick Verification (2-5 min)

• Single known library, confirming syntax/version
• Low-risk decision (easily changed later)
• Action: Quick docs check, no RESEARCH.md needed

Level 2 — Standard Research (15-30 min)

• Choosing between 2-3 options
• New external integration (API, service)
• Medium-risk decision
• Action: Route to /research-phase, produces RESEARCH.md

Level 3 — Deep Dive (1+ hour)

• Architectural decision with long-term impact
• Novel problem without clear patterns
• High-risk, hard to change later
• Action: Full research with RESEARCH.md

Depth indicators:

• Level 2+: New library not in package.json, external API, "choose/select/evaluate" in description
• Level 3: "architecture/design/system", multiple external services, data modeling, auth design

For niche domains (3D, games, audio, shaders, ML), suggest /research-phase before /plan.

Task Anatomy

Every task has four required fields:

<files>

Exact file paths created or modified.

• ✅ Good: src/app/api/auth/login/route.ts, prisma/schema.prisma
• ❌ Bad: "the auth files", "relevant components"

<action>

Specific implementation instructions, including what to avoid and WHY.

• ✅ Good: "Create POST endpoint accepting {email, password}, validates using bcrypt against User table, returns JWT in httpOnly cookie with 15-min expiry. Use jose library (not jsonwebtoken - CommonJS issues with Edge runtime)."
• ❌ Bad: "Add authentication", "Make login work"

<verify>

How to prove the task is complete.

• ✅ Good: npm test passes, curl -X POST /api/auth/login returns 200 with Set-Cookie header
• ❌ Bad: "It works", "Looks good"

<done>

Acceptance criteria — measurable state of completion.

• ✅ Good: "Valid credentials return 200 + JWT cookie, invalid credentials return 401"
• ❌ Bad: "Authentication is complete"

Task Types

Automation-first rule: If AI CAN do it via CLI/API, AI MUST do it. Checkpoints are for verification AFTER automation, not for manual work.

Task Sizing

Context Budget Rules

• Small task: <10% context budget, 1-2 files, local scope
• Medium task: 10-20% budget, 3-5 files, single subsystem
• Large task (SPLIT THIS): >20% budget, many files, crosses boundaries

Split Signals

Split into multiple plans when:

• 3 tasks in a plan

• 5 files per task

• Multiple subsystems touched
• Mixed concerns (API + UI + database in one plan)

Estimating Context Per Task

Dependency Graph

Building Dependencies

1. Identify shared resources (files, types, APIs)
2. Determine creation order (types before implementations)
3. Group independent work into same wave
4. Sequential dependencies go to later waves

Wave Assignment

• Wave 1: Foundation (types, schemas, utilities)
• Wave 2: Core implementations
• Wave 3: Integration and validation

Vertical Slices vs Horizontal Layers

Prefer vertical slices: Each plan delivers a complete feature path.

✅ Vertical (preferred):
Plan 1: User registration (API + DB + validation)
Plan 2: User login (API + session + cookie)

❌ Horizontal (avoid):
Plan 1: All database models
Plan 2: All API endpoints

File Ownership for Parallel Execution

Plans in the same wave MUST NOT modify the same files.

If two plans need the same file:

1. Move one to a later wave, OR
2. Split the file into separate modules

PLAN.md Structure

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