plan-orchestrator
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.
About plan-orchestrator
plan-orchestrator is a Claude AI skill developed by openimagingdata. 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. This powerful Claude Code plugin helps developers automate workflows and enhance productivity with intelligent AI assistance.
Why use plan-orchestrator? With 2 stars on GitHub, this skill has been trusted by developers worldwide. Install this Claude skill instantly to enhance your development workflow with AI-powered automation.
| 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:
-
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
- Read Serena memories:
-
Create Tracking
- Use TodoWrite to create checklist of all phases
- Note which phases can run in parallel
-
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 opspydantic-ai-implementer→ AI agents, PydanticAI code, agent configspython-test-implementer→ Unit tests, integration tests, fixturesai-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:
- Ask user: "Please run tests: [test command from Serena if available]"
- When tests pass: "Please commit with: git commit -m 'Implement [feature] per [plan]:lines [X-Y]'"
- Update TodoWrite (mark complete)
- Proceed to next phase
NEEDS_REVISION (max 3 cycles):
-
Summarize: "Evaluator found [N] issues:"
Critical (must fix): - [file:line] - [issue] Important (should fix): - [file:line] - [issue] -
Re-delegate to implementer: "Fix these issues: [focused list]"
-
Re-evaluate with same criteria
-
After 3 cycles without approval: "Unable to meet criteria after 3 attempts. Need guidance on: [specific blocker]"
BLOCKED:
- Check Serena for relevant guidance
- If found: "Found guidance in Serena: [summary]. Re-delegating..."
- 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:
- "Getting test output..."
- Delegate: "Use [implementer] to fix test failures: [output]"
- "Please re-run tests"
- 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.

openimagingdata
findingmodel
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