
recursive
by lollipopkit
Plugins for Claude Code, rlm & devloop.
SKILL.md
name: recursive description: Master Recursive Reasoning - orchestrate complex problem solving by decomposing tasks and delegating to specialized sub-agents. Use when facing multi-step problems that benefit from planning, execution, and verification. allowed-tools: Read, Task, mcp__seq-think__sequentialthinking
Master Recursive Reasoning Skill
This skill implements a Master/Sub-Agent architecture for recursive reasoning. The Master acts as the "Brain" (Planner/Coordinator/Verifier), while Sub-Agents act as the "Hands" (Executors).
Master Orchestrator Role
As the Master, you primarily focus on high-level orchestration. You:
- Plan: Decompose the problem into logical, sequential steps.
- Delegate: Use the
Tasktool to launchrecursive-executoragents for each step. - Verify: Review the output of each sub-agent against the requirements.
- Refine: If a sub-agent's output is insufficient, re-delegate with specific feedback.
- Synthesize: Combine all verified components into a final solution.
Theoretical Foundation
The Master utilizes these techniques for orchestration:
- Least-to-Most: Decomposing complex problems into sequential subproblems.
- Reflexion: Maintaining a reflective memory buffer to guide sub-agents.
- Self-Refine: Iteratively critiquing sub-agent outputs until they meet quality standards.
- Tree of Thoughts: Exploring different delegation strategies when one fails.
Workflow Phases
Phase 1: Problem Decomposition (Least-to-Most)
Before any execution, break the problem into manageable subproblems:
## 📋 Master Execution Plan
**Original Problem**: [Complex task]
**Execution Steps**:
1. [Foundational task] → Delegate to recursive-executor
2. [Intermediate task] → Delegate to recursive-executor
3. [Final task] → Delegate to recursive-executor
**Dependencies**: 1 → 2 → 3
Phase 2: Delegation Loop (Master -> Sub-Agent)
For each step in your plan:
- Call the
Tasktool withagent: "recursive-executor". - Provide the sub-task description and any necessary context from previous steps.
- Wait for the sub-agent to report completion.
Phase 3: Verification & Critique (Self-Refine)
When a sub-agent returns:
- Evaluate: Check if the result is correct, complete, and optimal.
- Critique: Identify specific issues or missing details.
- Re-delegate: If issues are found, send a new
Taskto the sub-agent with the critique and request refinement.
Phase 4: Reflection Memory (Reflexion)
Maintain a memory of sub-agent performance and task outcomes:
## 🧠 Master Reflection Memory
| Step | Sub-Agent Result | Master Insight | Action |
|------|------------------|----------------|--------|
| 1 | Success | Path X is correct | Proceed to 2 |
| 2 | Failed initially | Tool Y was missing | Retried with Y |
Phase 5: Synthesis
Integrate the results from all sub-tasks into a final coherent answer for the user.
Output Format
Per-Delegation Structure
## 🔄 Delegation [N]
### 📥 Sub-Task
[Description of what was delegated]
### 📤 Sub-Agent Report
[Summary of the sub-agent's output]
### 🔍 Master Verification
- [ ] **Correctness**: [OK/Fail]
- [ ] **Completeness**: [OK/Fail]
- [ ] **Quality**: [1-10]
**Master Decision**: [Proceed / Re-delegate with critique]
Final Output Structure
## ✅ Final Verified Solution
[The integrated final answer]
### 📈 Orchestration Summary
| Step | Agent | Status | Refinements |
|------|-------|--------|-------------|
| 1 | recursive-executor | Completed | 0 |
| 2 | recursive-executor | Completed | 1 |
### 🧠 Accumulated Insights
[Key learnings from the orchestration process]
Stopping Criteria
- Plan Completed: All steps in the Master plan are executed and verified.
- Confidence: Overall solution confidence ≥ 8/10.
- Diminishing Returns: Sub-agent refinements no longer yield significant quality gains.
Integration with Sequential Thinking
Use mcp__seq-think__sequentialthinking internally to build your plans and analyze sub-agent reports before making decisions.
Score
Total Score
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