Skill v1.0.1
currentAutomated scan100/1003 files
version: "1.0.1" name: meta-cognition-parallel description: "EXPERIMENTAL: Three-layer parallel meta-cognition analysis. Triggers on: /meta-parallel, 三层分析, parallel analysis, 并行元认知" argument-hint: "<rust_question>"
Meta-Cognition Parallel Analysis (Experimental)
Status: Experimental | Version: 0.2.0 | Last Updated: 2025-01-27This 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]: description2.[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 Reasoning1.**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
| Error | Cause | Solution | |
|---|---|---|---|
| Agent files not found | Skills-only install | Use inline mode (sequential) | |
| Agent timeout | Complex analysis | Wait longer or use inline mode | |
| Incomplete layer result | Agent issue | Fill in with inline analysis |
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.