implementing-tasks

name	implementing-tasks
description	Implements tasks from .plans/ directories by following implementation guidance, writing code and tests, and updating task status. Use when task file is in implementation/ directory and requires code implementation with comprehensive testing. Launches research agents when stuck.

Implementation

Given task file path .plans/<project>/implementation/NNN-task.md:

Process

Load Critical Patterns (if exists)

Before starting implementation, check for .plans/<project>/critical-patterns.md:

• If exists, read and internalize all patterns
• Apply matching patterns during implementation
• Violations will be flagged as CRITICAL in review

Use TodoWrite to track implementation progress:

☐ Read task file (LLM Prompt, Working Result, Validation)
☐ [LLM Prompt step 1]
☐ [LLM Prompt step 2]
...
☐ Write tests for new functionality
☐ Run full test suite
☐ Mark validation checkboxes
☐ Update status to READY_FOR_TESTING

Convert each step from the task's LLM Prompt into a todo. Mark completed as you progress.

1. Read task file - LLM Prompt, Working Result, Validation, Files
2. Follow LLM Prompt step-by-step, write code + tests, run full suite
3. Update task status using Edit tool:
   • For initial implementation: **Status:** READY_FOR_TESTING
   • For revision after rejection: **Status:** READY_FOR_REVIEW (skip testing, go back to review)
4. Append implementation notes using Edit tool (add to end of task file):

   **implementation:**
   - Followed LLM Prompt steps 1-N
   - Implemented [key functionality]
   - Added [N] tests: all passing
   - Full test suite: [M]/[M] passing
   - Working Result verified: ✓ [description]
   - Files: [list with brief descriptions]
   

5. Mark validation checkboxes: [ ] → [x] using Edit tool
6. Report completion

Stuck Handling

When blocked during implementation:

1. Mark Task as Stuck

• Update status using Edit tool:
  • Find: **Status:** [current status]
  • Replace: **Status:** STUCK
• Append notes using Edit tool (add to end of task file):

  **implementation:**
  - Attempted [what tried]
  - BLOCKED: [specific issue]
  - Launching research agents to investigate...
  

2. Launch Research Agents

Based on blocker type, launch 2-3 agents in parallel:

New technology/framework → research-breadth + research-technical:

• research-breadth: General understanding of technology/approach
• research-technical: Official API documentation

Specific error/issue → research-depth + research-technical:

• research-depth: Detailed analysis of specific solutions
• research-technical: Official API documentation

API integration → research-technical + research-depth:

• research-technical: Official API documentation
• research-depth: Detailed implementation examples

Best practices/patterns → research-breadth + research-depth:

• research-breadth: General surveys and comparisons
• research-depth: Detailed analysis of specific approaches

Example:

# Launch agents with specific questions
research-breadth "How to [solve blocker]?"
research-depth "Detailed solutions for [specific issue]"
research-technical "[library/framework] official documentation for [feature]"

3. Synthesize Findings

Use research-synthesis skill (from essentials) to:

• Consolidate findings from all agents
• Identify concrete path forward
• Extract actionable implementation guidance

Update task file with research findings using Edit tool (add to end of task file):

**research findings:**
- [Agent 1]: [key insights]
- [Agent 2]: [key insights]
- [Agent 3]: [key insights]

**resolution:**
[Concrete path forward based on research]

4. Continue or Escalate

If unblocked:

• Update status back to IN_PROGRESS
• Capture the learning (auto-invoked):

  Task(
    description: "Capture learning from blocker resolution",
    prompt: "Extract the learning from this resolved blocker.
  
    Problem context:
    - STUCK notes: [from task file]
    - Research findings: [from task file]
  
    Resolution:
    - What worked: [resolution notes]
    - Task: [task file path]
  
    Generate a learning document following the template in experimental/templates/learning.md.
    Save to: .plans/<project>/learnings/[YYYYMMDD-NNN-slug].md
    Update: .plans/<project>/learnings/index.md with new entry",
    subagent_type: "general-purpose",
    model: "haiku"
  )
  

• Resume implementation following research guidance
• Complete normally as per main Process section

If still stuck after research:

• Keep status as STUCK
• Append escalation notes using Edit tool (add to end of task file):

  **escalation:**
  - Research completed but blocker remains
  - Reason: [why research didn't unblock]
  - Need: [what's needed - human decision, missing requirement, etc.]
  

• Then STOP and report blocker with full context.

Rejection Handling

If task moved back from review (check for **review:** notes in task file):

1. Read review notes for blocking issues
2. Fix all CRITICAL and HIGH issues
3. Update status to READY_FOR_REVIEW (go back to review, skip testing)
4. Append revision notes:

   **implementation (revision):**
   - Fixed [issue 1]
   - Fixed [issue 2]
   - Re-ran tests: [M]/[M] passing
   

Test Fix Handling

If task moved back from testing (check for **testing:** notes with NEEDS_FIX):

1. Read testing notes for failures
2. Fix the failing tests or code
3. Update status to READY_FOR_TESTING (go back to testing)
4. Append fix notes:

   **implementation (test fix):**
   - Fixed [test issue]
   - Re-ran tests: [M]/[M] passing
   

Completion

When implementation is complete:

• Initial implementation: Status = READY_FOR_TESTING
• After review rejection: Status = READY_FOR_REVIEW
• After test failure: Status = READY_FOR_TESTING

Collect Implementation Metadata

Before setting final status, collect metadata for review triage:

**implementation_metadata:**
- files_changed: [count from git diff --stat]
- lines_changed: [insertions + deletions from git diff --stat]
- was_stuck: [true/false - was task ever marked STUCK?]
- research_agents_used: [list agents invoked, or 'none']
- severity_indicators: [list any detected: auth, crypto, payment, database-migration, etc.]
- complexity_indicators: [list any detected: state-machine, external-api, async-patterns, etc.]

Detection rules for severity_indicators:

• Scan Files for: auth, login, password, session, token, jwt, crypto, encrypt, secret, payment, billing, migration, permission, api_key
• If any found, add to severity_indicators list

Detection rules for complexity_indicators:

• Check for: state machines, external API calls, async/await patterns, database queries, caching logic
• If any found, add to complexity_indicators list

This metadata enables the review skill to route to LIGHTWEIGHT or FULL review.

Report: ✅ Implementation complete. Status: [STATUS]

Phrase-Based Learning Capture

During implementation, watch for phrases that indicate problem resolution:

• "that worked"
• "it's fixed"
• "figured it out"
• "problem solved"
• "got it working"

When detected:

1. Pause implementation
2. Ask: "Capture this as a learning? (y/n)"
3. If yes, invoke knowledge-capturer:

   Task(
     description: "Capture learning from resolution",
     prompt: "Extract the learning from this problem resolution.
   
     Context:
     - What was being attempted: [from recent conversation]
     - What was tried: [approaches that failed]
     - What worked: [the resolution]
     - Task: [task file path]
   
     Generate a learning document following the template in experimental/templates/learning.md.
     Save to: .plans/<project>/learnings/[YYYYMMDD-NNN-slug].md
     Update: .plans/<project>/learnings/index.md with new entry",
     subagent_type: "general-purpose",
     model: "haiku"
   )
   

4. Resume implementation

This captures solutions while context is fresh, before details are forgotten.