Meta-Cognition Parallel Analysis (Experimental)

Status: Experimental | Version: 0.2.0 | Last Updated: 2025-01-27

This skill tests parallel three-layer cognitive analysis.

Concept

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

User Question

     │

     ▼

┌─────────────────────────────────────────────────────┐

│            meta-cognition-parallel                   │

│                  (Coordinator)                       │

└─────────────────────────────────────────────────────┘

     │

     ├─── Layer 1 ──► Language Mechanics ──► L1 Result

     │

     ├─── Layer 2 ──► Design Choices     ──► L2 Result

     │                                            ├── Parallel (Agent Mode)

     │                                            │   or Sequential (Inline)

     └─── Layer 3 ──► Domain Constraints ──► L3 Result

     │

     ▼

┌─────────────────────────────────────────────────────┐

│              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 Mode Detection

CRITICAL: Check agent file availability first to determine execution mode.

Try to read layer analyzer files:

• ../../agents/layer1-analyzer.md

• ../../agents/layer2-analyzer.md

• ../../agents/layer3-analyzer.md

Agent Mode (Plugin Install) - Parallel Execution

**When all layer analyzer files exist at ../../agents/:**

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 per template below.

Inline Mode (Skills-only Install) - Sequential Execution

When layer analyzer files are NOT available, execute analysis directly:

Step 1: Parse User Query

Same as Agent Mode - extract question, code, and domain hints from $ARGUMENTS.

Step 2: Execute Layer 1 - Language Mechanics

Analyze the Rust language mechanics involved:

## Layer 1: Language Mechanics

**Error/Pattern Identified:**

- Error code: E0XXX (if applicable)

- Pattern: ownership/borrowing/lifetime/etc.

**Root Cause:**

[Explain why this error occurs in terms of Rust's ownership model]

**Language-Level Solutions:**

1. [Solution 1]: description

2. [Solution 2]: description

**Confidence:** HIGH | MEDIUM | LOW

**Reasoning:** [Why this confidence level]

Focus areas:

• Ownership rules (move, copy, borrow)

• Lifetime annotations

• Borrowing rules (shared vs mutable)

• Error codes and their meanings

Step 3: Execute Layer 2 - Design Choices

Analyze the design patterns and trade-offs:

## Layer 2: Design Choices

**Design Pattern Context:**

- Current approach: [What pattern is being used]

- Problem: [Why it conflicts with Rust's rules]

**Design Alternatives:**

| Pattern | Pros | Cons | When to Use |

|---------|------|------|-------------|

| Pattern A | ... | ... | ... |

| Pattern B | ... | ... | ... |

**Recommended Pattern:**

[Which pattern fits best and why]

**Confidence:** HIGH | MEDIUM | LOW

**Reasoning:** [Why this confidence level]

Focus areas:

• Smart pointer choices (Box, Rc, Arc)

• Interior mutability patterns (Cell, RefCell, Mutex)

• Ownership transfer vs sharing

• Cloning vs references

Step 4: Execute Layer 3 - Domain Constraints

Analyze domain-specific requirements:

## Layer 3: Domain Constraints

**Domain Identified:** [trading/fintech | web | CLI | embedded | etc.]

**Domain-Specific Requirements:**

- [ ] Performance: [requirements]

- [ ] Safety: [requirements]

- [ ] Concurrency: [requirements]

- [ ] Auditability: [requirements]

**Domain Best Practices:**

1. [Best practice 1]

2. [Best practice 2]

**Constraints on Solution:**

- MUST: [hard requirements]

- SHOULD: [soft requirements]

- AVOID: [anti-patterns for this domain]

**Confidence:** HIGH | MEDIUM | LOW

**Reasoning:** [Why this confidence level]

Focus areas:

• Industry requirements (FinTech regulations, web scalability, etc.)

• Performance constraints

• Safety and correctness requirements

• Common patterns in the domain

Step 5: Cross-Layer Synthesis

Combine all three layers:

## 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

Both modes produce the same output format:

# Three-Layer Meta-Cognition Analysis

> Query: [User's question]

---

## Layer 1: Language Mechanics

[L1 analysis result]

---

## Layer 2: Design Choices

[L2 analysis result]

---

## Layer 3: Domain Constraints

[L3 analysis 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:**

// Recommended implementation

Analysis performed by meta-cognition-parallel v0.2.0 (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

---

Error Handling

Limitations

• Agent Mode: Parallel execution, faster but requires plugin install

• Inline Mode: Sequential execution, slower but works everywhere

• Cross-layer synthesis quality depends on result structure

• May have higher latency than simple single-layer analysis

Feedback

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