🧠 Requirement Comprehension Engine#

A metacognitive skill that equips agents with structured requirement analysis, skill selection & orchestration. Delegates memory management to complex-memory-manager and self-iteration to self-iteration-engine for cross-skill consistency.

Core Capabilities#

1. Requirement Analysis & Decomposition#

When the agent receives a complex or ambiguous request:

Step 1: Intent Classification

• Parse incoming message for explicit intent signals (verbs, domain keywords, output expectations)
• Classify into: Direct Action / Information Request / Creative Generation / Analysis & Insight / Meta-Request / Multi-Step Workflow
• If ambiguous, flag as Under-Specified and use elicitation templates

Step 2: Structured Decomposition Break the request into a tree:

text

Original Request
├── Intent A (primary)
│   ├── Sub-goal A1 → skill/action candidate
│   ├── Sub-goal A2 → skill/action candidate
│   └── Dependency check
├── Intent B (secondary)
│   └── ...
└── Cross-cutting concerns (privacy, cost, performance)

Step 3: Skill Mapping For each sub-goal, select the best matching skill:

• Check name + description of all available skills (always in context as metadata)
• Rank matching skills by semantic relevance
• If no match → fallback to general knowledge
• If multiple → pick the most specific or orchestrate

2. Skill Orchestration Logic#

SituationActionSingle clear intent, single skillDirect invocationMultiple independent intentsParallel executionMulti-step pipeline (output A → input B)Sequential orchestration with handoffFull autonomy requestedAnalyze, decide, execute, explainConflicting skill capabilitiesPrefer skill with more specific description

Handoff Protocol:

text

[ComprehensionEngine] Handoff: Skill-A → Skill-B
  Context: {user's original intent excerpt}
  Skill-A result: {summary}
  Skill-B requirement: {what Skill-B needs to continue}

3. Requirement Elicitation Templates#

When a request is under-specified:

Pattern A: Missing Output Format

"I need to confirm the output format — do you want a daily report, summary, table, chart, code, or something else?"

Pattern B: Missing Scope

"This request seems broad. Could you narrow down the focus? For example: Option A, Option B, or Option C?"

Pattern C: Ambiguous Intent

"I see two possible directions: do you want me to analyze existing data, or go out and collect new data?"

Pattern D: Multi-Step Confirmation

"I plan to do this in three steps: ① collect latest market data ② analyze trends ③ generate a visualization. Does that work?"

4. Delegation to Shared Components#

FunctionDelegated ToHowPersistent memory (T1/T2/T3)complex-memory-managerUse its encrypt/decrypt, storage conventions, and cleanup protocolsUsage logging & performance trackingself-iteration-engineUse its log format, review cycles, and update matrixSelf-iteration decisionsself-iteration-engineCheck triggers, run update decision matrixNew skill creation proposalsself-iteration-engineUse its proposal template

Quick Reference: Skill Selection Flow#

text

User speaks
  ↓
Parse & Classify Intent
  ↓
Is it clear? ──No──→ Use Elicitation Templates
  │                     ↓
  Yes←── User clarifies
  ↓
Decompose into sub-goals
  ↓
For each sub-goal:
  ├── Match skill by name+description
  ├── If match: check last-review-date; if >30d → quick scan SKILL.md
  ├── If no match: fallback to general knowledge
  └── If multi-skill: orchestrate (parallel or sequential)
  ↓
Execute & Deliver
  ↓
Log outcome → Delegate to self-iteration-engine
  ↓
[Periodic] Delegate review to self-iteration-engine + memory cleanup to complex-memory-manager

Dependency Check#

Before each orchestration run:

1. Verify complex-memory-manager metadata description is in context
2. Verify self-iteration-engine metadata description is in context
3. If either is missing, they may not be installed — fallback to built-in behavior

---

🧠 需求理解引擎#

一个元认知技能，赋予Agent结构化的需求分析、技能选择与编排能力。将持久化记忆委托给 complex-memory-manager，将自迭代委托给 self-iteration-engine，实现跨技能一致性。

核心能力#

1. 需求分析与分解#

当Agent收到复杂或模糊请求时：

第一步：意图分类

• 解析输入信息的意图信号
• 分类：直接操作 / 信息查询 / 创意生成 / 分析洞察 / 元请求 / 多步工作流
• 若模棱两可，标记为"信息不足"并使用澄清模板

第二步：结构化分解 将请求拆解为树状结构

第三步：Skill映射 为每个子目标选择最佳匹配skill

2. 技能编排逻辑#

场景行动单一明确意图直接调用多个独立意图并行执行多步流水线顺序编排+交接记录用户要求自主分析→决策→执行→解释技能冲突选择描述更具体的

3. 需求澄清模板#

在请求信息不足时使用四种追问模式（详见英文版）。

4. 委托给共享组件#

功能委托给方式持久化记忆(T1/T2/T3)complex-memory-manager使用其加密/解密、存储规范、清理协议使用日志与性能追踪self-iteration-engine使用其日志格式、审查周期、更新矩阵自迭代决策self-iteration-engine检查触发条件、运行更新决策矩阵新技能创建建议self-iteration-engine使用其提案模板

依赖检查#

每次编排运行前：

1. 检查 complex-memory-manager 的 metadata 是否在上下文中
2. 检查 self-iteration-engine 的 metadata 是否在上下文中
3. 若缺失，可能未安装——回退到内置基础行为

参考文件#

• references/orchestration-patterns.md — 高级多skill编排示例