Meta-Cognition Parallel Analysis (Experimental)

Meta-Cognition Parallel Analysis (Experimental)

Status: Experimental | Version: 0.1.0

This skill tests parallel three-layer cognitive analysis using context: fork.

Concept

Instead of sequential analysis, this skill launches three parallel subagents - one for each cognitive layer - then synthesizes their results.

User Question
     │
     ▼
┌─────────────────────────────────────────────────────┐
│            meta-cognition-parallel                   │
│                  (Coordinator)                       │
└─────────────────────────────────────────────────────┘
     │
     ├─── Task(fork) ──► layer1-analyzer ──► L1 Result
     │                   (Language Mechanics)
     │
     ├─── Task(fork) ──► layer2-analyzer ──► L2 Result
     │                   (Design Choices)         ├── Parallel
     │                                            │
     └─── Task(fork) ──► layer3-analyzer ──► L3 Result
                         (Domain Constraints)
     │
     ▼
┌─────────────────────────────────────────────────────┐
│              Cross-Layer Synthesis                   │
│         (In main context with all results)          │
└─────────────────────────────────────────────────────┘
     │
     ▼
Domain-Correct Architectural Solution

Usage

/​meta-parallel <your Rust question>

Example:

/​meta-parallel 我的交易系统报 E0382 错误，应该用 clone 吗？

Execution Instructions

Step 1: Parse User Query

Extract from $ARGUMENTS:

• The original question
• Any code snippets
• Domain hints (trading, web, embedded, etc.)

Step 2: Launch Three Parallel Agents

CRITICAL: Launch all three Tasks in a SINGLE message to enable parallel execution.

Read agent files, then launch in parallel:

Task(
  subagent_type: "general-purpose",
  run_in_background: true,
  prompt: <content of agents/​layer1-analyzer.md>
          + "\n\n## User Query\n" + $ARGUMENTS
)

Task(
  subagent_type: "general-purpose",
  run_in_background: true,
  prompt: <content of agents/​layer2-analyzer.md>
          + "\n\n## User Query\n" + $ARGUMENTS
)

Task(
  subagent_type: "general-purpose",
  run_in_background: true,
  prompt: <content of agents/​layer3-analyzer.md>
          + "\n\n## User Query\n" + $ARGUMENTS
)

Step 3: Collect Results

Wait for all three agents to complete. Each returns structured analysis.

Step 4: Cross-Layer Synthesis

With all three results, perform synthesis:

## Cross-Layer Synthesis

### Layer Results Summary

| Layer | Key Finding | Confidence |
|-------|-------------|------------|
| L1 (Mechanics) | [Summary] | [Level] |
| L2 (Design) | [Summary] | [Level] |
| L3 (Domain) | [Summary] | [Level] |

### Cross-Layer Reasoning

1. **L3 → L2:** [How domain constraints affect design choice]
2. **L2 → L1:** [How design choice determines mechanism]
3. **L1 ← L3:** [Direct domain impact on language features]

### Synthesized Recommendation

**Problem:** [Restated with full context]

**Solution:** [Domain-correct architectural solution]

**Rationale:**
- Domain requires: [L3 constraint]
- Design pattern: [L2 pattern]
- Mechanism: [L1 implementation]

### Confidence Assessment

- **Overall:** HIGH | MEDIUM | LOW
- **Limiting Factor:** [Which layer had lowest confidence]

Output Template

# Three-Layer Meta-Cognition Analysis

> Query: [User's question]

---

## Layer 1: Language Mechanics
[L1 agent result]

---

## Layer 2: Design Choices
[L2 agent result]

---

## Layer 3: Domain Constraints
[L3 agent result]

---

## Cross-Layer Synthesis

### Reasoning Chain

L3 Domain: [Constraint] ↓ implies L2 Design: [Pattern] ↓ implemented via L1 Mechanism: [Feature]

### Final Recommendation

**Do:** [Recommended approach]

**Don't:** [What to avoid]

**Code Pattern:**
```rust
// Recommended implementation

---

Analysis performed by meta-cognition-parallel v0.1.0 (experimental)

## Test Scenarios

### Test 1: Trading System E0382

/​meta-parallel 交易系统报 E0382，trade record 被 move 了

Expected: L3 identifies FinTech constraints → L2 suggests shared immutable → L1 recommends Arc<T>

### Test 2: Web API Concurrency

/​meta-parallel Web API 中多个 handler 需要共享数据库连接池

Expected: L3 identifies Web constraints → L2 suggests connection pooling → L1 recommends Arc<Pool>

### Test 3: CLI Tool Config

/​meta-parallel CLI 工具如何处理配置文件和命令行参数的优先级

Expected: L3 identifies CLI constraints → L2 suggests config precedence pattern → L1 recommends builder pattern

## Limitations (Experimental)

- Subagent results are summarized, may lose detail
- Parallel execution depends on Claude Code version
- Cross-layer synthesis quality depends on result structure
- May have higher latency than sequential approach

## Feedback

This is experimental. Please report issues and suggestions to improve the three-layer parallel analysis approach.