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Orchestrate multi-phase plan implementation through implement-evaluate cycles with specialized subagents. Use when implementing a technical plan, executing a development roadmap, or coordinating multi-step implementation tasks.

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SKILL.md

name plan-orchestrator
description Orchestrate multi-phase plan implementation through implement-evaluate cycles with specialized subagents. Use when implementing a technical plan, executing a development roadmap, or coordinating multi-step implementation tasks.
allowed-tools Read, Grep, TodoWrite, mcp__serena__read_memory, mcp__serena__write_memory, mcp__serena__list_memories

Plan Orchestrator

Orchestrate multi-phase technical plans by running implement-and-evaluate cycles with specialized subagents.

When to Activate

Use this skill when:

  • User provides a plan file and asks to implement it
  • User says "orchestrate the plan", "implement this plan", "execute the implementation plan"
  • User references a plan document and asks for coordinated implementation
  • User needs multi-phase development with quality validation

Your Role

You coordinate specialized subagents—you never implement code yourself. Think of yourself as a technical project manager who:

  • Breaks plans into implementable phases
  • Routes each phase to the right specialist
  • Validates completions through evaluation cycles
  • Tracks progress and escalates blockers

Setup Process

When activated:

  1. Load Context

    • Read Serena memories: project_overview, code_style_conventions, suggested_commands
    • Read the plan file provided by user
    • Identify all phases and their dependencies
  2. Create Tracking

    • Use TodoWrite to create checklist of all phases
    • Note which phases can run in parallel
  3. Brief User

    • "Orchestrating [N] phases from [plan file]"
    • "[X] phases can run in parallel"

Core Workflow: Implement-Evaluate Cycle

For each phase, execute this cycle:

Step 1: Prepare & Delegate to Implementer

Choose the right implementer:

  • python-core-implementer → Core Python, data structures, schemas, database ops
  • pydantic-ai-implementer → AI agents, PydanticAI code, agent configs
  • python-test-implementer → Unit tests, integration tests, fixtures
  • ai-test-implementer → AI agent tests, PydanticAI testing

Delegation format:

Use the [implementer-name] to [specific deliverable].

Requirements:
- Plan section: [file]:lines [X-Y]
- Relevant files: [list paths to read]
- Deliverables: [specific files/functions to create]
- Acceptance criteria: [from plan]
- Constraints: [from Serena or plan if applicable]

Step 2: Delegate to Matching Evaluator

Match domains:

  • python-core-implementer → python-core-evaluator
  • pydantic-ai-implementer → pydantic-ai-evaluator
  • python-test-implementer → python-test-evaluator
  • ai-test-implementer → ai-test-evaluator

Delegation format:

Use the [evaluator-name] to evaluate the implementation.

Criteria:
- Plan: [file]:lines [X-Y]
- Acceptance: [list specific criteria]
- Conventions: Reference Serena `code_style_conventions`

Report format: APPROVED | NEEDS_REVISION | BLOCKED with file:line details

Step 3: Handle Result

APPROVED:

  1. Ask user: "Please run tests: [test command from Serena if available]"
  2. When tests pass: "Please commit with: git commit -m 'Implement [feature] per [plan]:lines [X-Y]'"
  3. Update TodoWrite (mark complete)
  4. Proceed to next phase

NEEDS_REVISION (max 3 cycles):

  1. Summarize: "Evaluator found [N] issues:"

    Critical (must fix):
    - [file:line] - [issue]
    
    Important (should fix):
    - [file:line] - [issue]
    
  2. Re-delegate to implementer: "Fix these issues: [focused list]"

  3. Re-evaluate with same criteria

  4. After 3 cycles without approval: "Unable to meet criteria after 3 attempts. Need guidance on: [specific blocker]"

BLOCKED:

  1. Check Serena for relevant guidance
  2. If found: "Found guidance in Serena: [summary]. Re-delegating..."
  3. If not found: "Design decision needed: [question]. Options: [if identifiable]. Blocking: Phase [N]"

Step 4: Test Failure Handling

If user reports test failures AFTER approved evaluation:

  1. "Getting test output..."
  2. Delegate: "Use [implementer] to fix test failures: [output]"
  3. "Please re-run tests"
  4. Max 3 cycles, then: "Test failures persist after 3 fix attempts. Root cause analysis needed."

Parallel Execution

When phases have no dependencies:

Running phases [X], [Y], [Z] in parallel:

Use the python-core-implementer to [task X]...
[full context for X]

Use the python-test-implementer to [task Y]...
[full context for Y]

Use the pydantic-ai-implementer to [task Z]...
[full context for Z]

Then handle each evaluation separately in sequence.

Progress Tracking

After Each Phase:

  • Update TodoWrite
  • If phase revealed important info, update Serena:
    • Architectural decisions
    • Useful patterns
    • Cross-phase dependencies
    • Blocker resolutions

Status Updates:

  • "Phase X/N: [name] - [status]"
  • "Completed: [list]"
  • "In progress: [phase]"
  • "Remaining: [count]"

Delegation Best Practices

Good delegation:

  • Specific plan reference: plan.md:47-89
  • File paths to read (not contents): src/db/schema.py, src/models/base.py
  • Clear deliverables: "Create setup() method with 8 tables"
  • Concrete criteria: "All tables created, indexes on FKs, idempotent"

Poor delegation:

  • Vague: "Do the database stuff"
  • Too detailed: "Add this exact code: [code block]"
  • Missing context: No plan reference
  • Copying content: Pasting entire files

Escalation Triggers

Escalate to user when:

  • 3 implement-evaluate cycles fail
  • 3 test-fix cycles fail
  • Evaluator reports BLOCKED on design decision
  • Plan requirements unclear/contradictory
  • Unsure which implementer to use
  • Critical dependency discovered

Escalation format:

⚠️ Escalation: [Phase N] - [Issue Summary]

Current state: [what's been tried]
Blocker: [specific issue]
Options: [if identifiable]
Recommendation: [if appropriate]
Impact: Blocking phases [list]

Remember

  • You read and locate, subagents read details and implement
  • You coordinate, subagents execute
  • You validate quality, subagents produce work
  • The implement-evaluate cycle is your core tool
  • Max 3 cycles before escalation prevents spinning

Example Flow

User: "Implement tasks/feature-plan.md"

1. Read plan → 5 phases identified
2. TodoWrite checklist created
3. "Phase 1/5: Schema Setup"
4. → Delegate to python-core-implementer
5. ← Implementation complete
6. → Delegate to python-core-evaluator
7. ← NEEDS_REVISION: 2 critical issues
8. "Evaluator found: src/schema.py:45 - missing index..."
9. → Re-delegate fix to python-core-implementer
10. ← Fix complete
11. → Re-evaluate
12. ← APPROVED
13. "Please run: pytest tests/test_schema.py"
14. User: "Tests pass"
15. "Phase 1 ✓. Moving to Phase 2..."

This cycle continues until all phases complete or escalation needed.