Phase 1: Exploration (Propose Approaches)
         ┌─ Agent A → Proposals A1, A2 (with probabilities) ─┐
Task ───┼─ Agent B → Proposals B1, B2 (with probabilities) ─┼─┐
         └─ Agent C → Proposals C1, C2 (with probabilities) ─┘ │
                                                                │
Phase 1.5: Pruning Meta-Judge (runs in parallel with Phase 1) │
         Meta-Judge → Pruning Evaluation Specification YAML ───┤
                                                                │
Phase 2: Pruning (Vote for Best 3)                             │
         ┌─ Judge 1 → Votes + Rationale ─┐                     │
         ├─ Judge 2 → Votes + Rationale ─┼─────────────────────┤
         └─ Judge 3 → Votes + Rationale ─┘                     │
                 │                                              │
                 ├─→ Select Top 3 Proposals                     │
                 │                                              │
Phase 3: Expansion (Develop Full Solutions)                    │
         ┌─ Agent A → Solution A (from proposal X) ─┐          │
         ├─ Agent B → Solution B (from proposal Y) ─┼──────────┤
         └─ Agent C → Solution C (from proposal Z) ─┘          │
                                                                │
Phase 3.5: Evaluation Meta-Judge (runs in parallel w/ Phase 3)│
         Meta-Judge → Evaluation Specification YAML ───────────┤
                                                                │
Phase 4: Evaluation (Judge Full Solutions)                     │
         ┌─ Judge 1 → Report 1 ─┐                              │
         ├─ Judge 2 → Report 2 ─┼──────────────────────────────┤
         └─ Judge 3 → Report 3 ─┘                              │
                                                                │
Phase 4.5: Adaptive Strategy Selection                         │
         Analyze Consensus ────────────────────────────────────┤
                ├─ Clear Winner? → SELECT_AND_POLISH           │
                ├─ All Flawed (<3.0)? → REDESIGN (Phase 3)     │
                └─ Split Decision? → FULL_SYNTHESIS            │
                                         │                      │
Phase 5: Synthesis (Only if FULL_SYNTHESIS)                    │
         Synthesizer ────────────────────┴──────────────────────┴─→ Final Solution

mkdir -p .specs/research .specs/reports

<task>
{task_description}
</task>

<constraints>
{constraints_if_any}
</constraints>

<context>
{relevant_context}
</context>

<output>
{.specs/research/{solution-name}-{date}.proposals.[a|b|c].md - each agent gets unique letter identifier}
</output>

Instructions:

Let's approach this systematically by first understanding what we're solving, then exploring the solution space.

**Step 1: Decompose the problem**
Before generating approaches, break down the task:
- What is the core problem being solved?
- What are the key constraints and requirements?
- What subproblems must any solution address?
- What are the evaluation criteria for success?

**Step 2: Map the solution space**
Identify the major dimensions along which solutions can vary:
- Architecture patterns (e.g., monolithic vs distributed)
- Implementation strategies (e.g., eager vs lazy)
- Trade-off axes (e.g., performance vs simplicity)

**Step 3: Generate 6 distinct high-level approaches**

**Sampling guidance:**
Please sample approaches at random from the [full distribution / tails of the distribution]
- For first 3 approaches aim for high probability, over 0.80
- For last 3 approaches aim for diversity - explore different regions of the solution space, such that the probability of each response is less than 0.10

For each approach, provide:
   - Name and one-sentence summary
   - Detailed description (2-3 paragraphs)
   - Key design decisions and rationale
   - Trade-offs (what you gain vs what you sacrifice)
   - Probability (0.0-1.0)
   - Complexity estimate (low/medium/high)
   - Potential risks and failure modes

**Step 4: Verify diversity**
Before finalizing, check:
- Are approaches genuinely different, not minor variations?
- Do they span different regions of the solution space?
- Have you covered both conventional and unconventional options?


CRITICAL:
- Do NOT implement full solutions yet - only high-level approaches
- Ensure approaches are genuinely different, not minor variations

undefined

**Dispatch:**

undefined

You are evaluating {N} proposed approaches against an evaluation specification produced by the meta judge, to select the top 3 for full development.

CLAUDE_PLUGIN_ROOT=`${CLAUDE_PLUGIN_ROOT}`

{pruning meta-judge's evaluation specification YAML}

**Dispatch:**

undefined

You are developing a full solution based on a selected proposal.

<task>
{task_description}
</task>

<selected_proposal>
{write selected proposal EXACTLY as it is. Including all details provided by the agent}
Read this carefully - it is your starting point.
</selected_proposal>

<judge_feedback>
{concerns and questions from judges about this proposal}
Address these in your implementation.
</judge_feedback>

<output>
solution.[*].md where [*] is your unique identifier (a, b, or c)
</output>

Instructions:

Let's work through this systematically to ensure we build a complete, high-quality solution.

**Step 1: Understand the proposal deeply**
Before implementing, analyze:
- What is the core insight or approach of this proposal?
- What are the key design decisions already made?
- What gaps need to be filled for a complete solution?

**Step 2: Address judge feedback**
For each concern raised by judges:
- What specific change or addition addresses this concern?
- How does this change integrate with the proposal's approach?

**Step 3: Decompose into implementation subproblems**
Break the solution into logical parts:
- What are the main components or sections?
- What must be defined first for other parts to build upon?
- What are the dependencies between parts?

**Step 4: Implement each subproblem**
For each component, work through:
- Core functionality and behavior
- Edge cases and error handling
- Integration points with other components

**Step 5: Self-verification**
Generate 3-5 verification questions about critical aspects, then answer them:
- Review solution against each question
- Identify gaps or weaknesses
- Fix identified issues

**Step 6: Document changes**
Explain what was changed from the original proposal and why.

<example>
**Example of good expansion thinking:**

Proposal: "Use event-driven architecture with message queue"

Step 1 Analysis:
- Core insight: Decouple components via async messaging
- Key decisions: Events as primary communication, eventual consistency
- Gaps: Need to define event schemas, queue technology, error handling

Step 2 - Addressing judge concern "What about message ordering?":
- Add partition keys for ordered processing within entity scope
- Document ordering guarantees and limitations

Step 3 - Subproblems:
1. Event schema definitions (foundational - others depend on this)
2. Producer interfaces (depends on schemas)
3. Consumer handlers (depends on schemas)
4. Error handling and dead letter queues (depends on both)
5. Integration patterns (builds on all above)
</example>

CRITICAL:
- Stay faithful to the selected proposal's core approach
- Do not switch to a different approach midway
- Address judge feedback explicitly
- Produce a complete, implementable solution

undefined

**Dispatch:**

undefined

You are evaluating {number} full solutions against an evaluation specification produced by the meta judge.

CLAUDE_PLUGIN_ROOT=`${CLAUDE_PLUGIN_ROOT}`

{evaluation meta-judge's evaluation specification YAML}

**Dispatch:**

undefined

You are polishing the winning solution based on judge feedback.

<task>
{task_description}
</task>

<winning_solution>
{path_to_winning_solution}
Score: {winning_score}/5.0
Judge consensus: {why_it_won}
</winning_solution>

<runner_up_solutions>
{list of paths to all runner-up solutions}
</runner_up_solutions>

<judge_feedback>
{list of paths to all evaluation reports}
</judge_feedback>

<output>
{final_solution_path}
</output>

Instructions:

Let's approach this polishing task methodically to improve without disrupting what works.

**Step 1: Understand why this solution won**
Analyze the winning solution:
- What are its core strengths that judges praised?
- What makes its approach superior to alternatives?
- Which parts should remain untouched?

**Step 2: Catalog improvement opportunities**
From judge feedback, identify:
- Specific weaknesses mentioned (list each one)
- Missing elements judges noted
- Areas where runner-ups were praised

**Step 3: Prioritize changes by impact**
For each improvement opportunity:
- High impact: Directly addresses judge criticism
- Medium impact: Adds praised element from runner-up
- Low impact: Nice-to-have refinement

Focus on high-impact changes first.

**Step 4: Apply improvements surgically**
For each change:
- Locate the specific section to modify
- Make the minimal change needed to address the issue
- Verify the change integrates cleanly with surrounding content

**Step 5: Cherry-pick from runners-up**
Review runner-up solutions for:
- 1-2 specific elements that judges praised
- Elements that complement (not conflict with) the winning approach
- Only incorporate if clearly superior to winning solution's version

**Step 6: Document all changes**
Record:
- What was changed and why (with reference to judge feedback)
- What was added from other solutions (cite source)
- What was intentionally left unchanged

CRITICAL: Preserve the winning solution's core approach. Make targeted improvements only.

You are analyzing why all solutions failed to meet quality standards, to inform a redesign. And implement new solution based on it.


<task>
{task_description}
</task>

<constraints>
{constraints_if_any}
</constraints>

<context>
{relevant_context}
</context>

<failed_solutions>
{list of paths to all solution files}
Average scores: A={avg_a}/5.0, B={avg_b}/5.0, C={avg_c}/5.0
</failed_solutions>

<evaluation_reports>
{list of paths to all evaluation reports}
All solutions scored below 3.0/5.0 threshold.
</evaluation_reports>

<output>
.specs/research/{solution-name}-{date}.redesign-analysis.md
</output>

Instructions:
Let's break this down systematically to understand what went wrong and how to design new solution based on it.

1. First, analyze the task carefully - what is being asked and what are the key requirements?
2. Read through each solution and its evaluation report
3. For each solution, think step by step about:
   - What was the core approach?
   - What specific issues did judges identify?
   - Why did this approach fail to meet the quality threshold?
4. Identify common failure patterns across all solutions:
   - Are there shared misconceptions?
   - Are there missing requirements that all solutions overlooked?
   - Are there fundamental constraints that weren't considered?
5. Extract lessons learned:
   - What approaches should be avoided?
   - What constraints must be addressed?
6. Generate improved guidance for the next iteration:
   - New constraints to add
   - Specific approaches to try - what are the different ways to solve this?
   - Key requirements to emphasize
7. Think through the tradeoffs step by step and choose the approach you believe is best
8. Implement it completely
9. Generate 5 verification questions about critical aspects
10. Answer your own questions:
   - Review solution against each question
   - Identify gaps or weaknesses
11. Revise solution:
   - Fix identified issues
12. Explain what was changed and why

You are synthesizing the best solution from explored, pruned, and evaluated implementations.

<task>
{task_description}
</task>

<solutions>
{list of paths to all solution files}
</solutions>

<evaluation_reports>
{list of paths to all evaluation reports}
</evaluation_reports>

<selection_rationale>
{path to selection.md explaining why these proposals were chosen}
</selection_rationale>

<output>
{output_path} - The final synthesized solution
</output>

Instructions:

Let's approach this synthesis systematically by first analyzing, then decomposing, then building.

**Step 1: Build the evidence base**
Before synthesizing, gather evidence from judge reports:
- What did multiple judges praise? (consensus strengths)
- What did multiple judges criticize? (consensus weaknesses)
- Where did judges disagree? (areas needing careful analysis)

**Step 2: Decompose into synthesis subproblems**
Break the solution into logical sections or components. For each component:
- Which solution handles this best? (cite evidence)
- Are there complementary elements from multiple solutions?
- What issues were identified that need fixing?

**Step 3: Solve each subproblem**
For each component/section, determine the synthesis strategy:

*Strategy A - Clear winner:* If one solution is clearly superior for this component:
- Copy that section directly
- Document: "Taken from Solution X because [judge evidence]"

*Strategy B - Complementary combination:* If solutions have complementary strengths:
- Identify what each contributes
- Combine carefully, ensuring consistency
- Document: "Combined X from Solution A with Y from Solution B because [rationale]"

*Strategy C - All flawed:* If all solutions have issues in this area:
- Start with the best version
- Apply fixes based on judge criticism
- Document: "Based on Solution X, modified to address [specific issues]"

**Step 4: Integrate and verify consistency**
After synthesizing all components:
- Check that combined elements work together
- Resolve any contradictions between borrowed sections
- Ensure consistent terminology and style

**Step 5: Document synthesis decisions**
Create a synthesis log:
- What you took from each solution (with specific citations)
- Why you made those choices (reference judge feedback)
- How you addressed identified weaknesses
- Any novel combinations or improvements

<example>
**Example synthesis decision for an API design:**

Component: Authentication flow
- Solution A: JWT with refresh tokens (praised for security by 2/3 judges)
- Solution B: Session-based (praised for simplicity by 1 judge, criticized for scalability)
- Solution C: OAuth2 only (criticized as over-engineered for use case)

Decision: Take Solution A's authentication flow directly.
Evidence: Judges 1 and 3 both noted "JWT approach provides good balance of security and statelessness"
Modification: None needed - this section was rated highest across judges.
</example>

**Step 6: Revise your solution**
- Generate 5 verification questions about critical aspects
- Answer your own questions:
   - Review solution against each question
   - Identify gaps or weaknesses
- Revise solution:
   - Fix identified issues
- Explain what was changed and why


CRITICAL:
- Do not create something entirely new - synthesize the best from what exists
- Cite your sources (which solution, which section)
- Explain every major decision
- Address all consensus weaknesses identified by judges

/tree-of-thoughts "Design REST API for user management (CRUD + auth)" \
  --output "specs/api/users.md" \
  --criteria "RESTfulness,security,scalability,developer-experience"