# Task Memory: {taskId} - {title}

**Started**: {timestamp}
**Agent**: {agent}

## Good Points

_To be filled during task execution_

## Pain Points

_To be filled during task execution_

## Technical Decisions

_To be filled during task execution_

## Notes

_To be filled during task execution_

┌─────────────────────────────────────────────────────────────────┐
│  1. TASK START                                                  │
│     - Receive task_assign or asset_assign message              │
│     - CREATE task-{taskId}-memory.md                           │
│     - Initialize with task ID, title, timestamp, empty sections│
└─────────────────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────────┐
│  2. DURING TASK EXECUTION                                      │
│     - When something notable happens, APPEND to task memory   │
│     - Good points: solutions that worked well                  │
│     - Pain points: blockers, difficulties, unclear docs        │
│     - Technical decisions: architectural choices, workarounds   │
│     - Notes: context for future reference                      │
└─────────────────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────────┐
│  3. RETROSPECTIVE TRIGGERED                                     │
│     - Receive retrospective_initiate message                    │
│     - READ ALL task-*.md files from agents/{agent}/            │
│     - Use contents to populate retrospective contribution       │
└─────────────────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────────┐
│  4. AFTER CONTRIBUTION                                          │
│     - DELETE ALL task-*.md files from agents/{agent}/          │
│     - Verify files are removed                                 │
└─────────────────────────────────────────────────────────────────┘

## Good Points

_Initial content_

- Used React Three Fiber's InstancedMesh for performance – rendered 1000+ objects at 60fps
- TypeScript strict mode caught a potential null reference early

## Pain Points

_Initial content_

- Rapier documentation unclear on collision event handling – had to inspect source code
- Initial approach used individual meshes – performance was terrible at 15fps

## Technical Decisions

_Initial content_

- Chose InstancedMesh over individual meshes for performance (1000+ objects)
- Decided to use Rapier's collision events instead of manual distance checks

{
  "agent": "{agent}",
  "taskId": "{taskId}",
  "timestamp": "{ISO-8601-UTC}",
  "contributions": {
    "whatWorkedWell": ["Solution 1 that worked well", "Solution 2 that worked well"],
    "technicalChallenges": ["Challenge 1 faced", "Challenge 2 faced"],
    "implementationDecisions": ["Decision 1 with reasoning", "Decision 2 with reasoning"],
    "areasForImprovement": ["Area 1 that could be better", "Area 2 that could be better"],
    "lessonsLearned": ["Lesson 1 for future tasks", "Lesson 2 for future tasks"]
  }
}

# Task Memory: P1-005 - Multiplayer Room State Synchronization

**Started**: 2026-01-26T14:15:00Z
**Agent**: developer

## Good Points

- Colyseus's `Schema` serialization worked perfectly for state sync
- Used @colyseus/schema `ArraySchema` for player list – type-safe and efficient
- Client-side prediction reduced perceived lag significantly

## Pain Points

- Colyseus documentation on `onChange` callbacks was unclear – had to test multiple approaches
- Initial state structure caused 500kb payloads – had to refactor to use `Schema`
- TypeScript types for Colyseus state weren't exported – had to create custom types

## Technical Decisions

- Chose Colyseus `Schema` over raw JSON for bandwidth (80% reduction)
- Implemented client-side prediction for movement despite added complexity
- Decided NOT to sync animation state to server (client-authoritative for cosmetics)

## Notes

- Need to ask PM about weapon fire synchronization – current approach feels laggy
- GDD doesn't specify if physics should be server-authoritative

{
  "agent": "developer",
  "taskId": "P1-005",
  "timestamp": "2026-01-26T15:45:00.000Z",
  "contributions": {
    "whatWorkedWell": [
      "Colyseus Schema serialization worked perfectly once implemented",
      "Client-side prediction significantly reduced perceived lag",
      "Type-safe player list with ArraySchema"
    ],
    "technicalChallenges": [
      "Colyseus documentation on onChange callbacks unclear – had to experiment",
      "Initial state design caused 500kb payloads – required major refactor to Schema",
      "TypeScript types not exported from Colyseus – created custom type definitions"
    ],
    "implementationDecisions": [
      "Used Colyseus Schema serialization instead of raw JSON (80% bandwidth reduction)",
      "Chose ArraySchema for player list to maintain type safety",
      "Implemented client-side prediction for movement despite added complexity",
      "Kept animation state client-authoritative (cosmetics only)"
    ],
    "areasForImprovement": [
      "GDD should specify server-authoritative requirements for physics",
      "Weapon fire synchronization approach needs review – feels laggy",
      "Should prototype state payload size before committing to approach"
    ],
    "lessonsLearned": [
      "Always prototype state payload size before committing to approach",
      "Client-side prediction worth the complexity for fast-paced games",
      "Need clearer specification on what must be server-authoritative"
    ]
  }
}