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
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:
Task(
subagent_type: "general-purpose",
run_in_background: true,
prompt:
Task(
subagent_type: "general-purpose",
run_in_background: true,
prompt:
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
- L3 → L2: [How domain constraints affect design choice]
- L2 → L1: [How design choice determines mechanism]
- 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
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
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.