数据分析
- 作者仓库星标 103
- 作者更新于 实时读取
- 作者仓库 claude
- 领域
- 通用
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @lyndonkl · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- macOS · Linux · Windows
- 底层运行要求
- Node.js
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 允许外网请求
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: decomposition-reconstruction
description: Breaks complex systems into atomic components, maps their relationships, and reconstructs them i…
category: 通用
runtime: Node.js
---
# decomposition-reconstruction 输出预览
## PART A: 任务判断
- 适用问题:通用任务拆解、检查和交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Workflow / Scoping Questions / Decomposition Strategies”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于通用任务拆解、检查和交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Workflow / Scoping Questions / Decomposition Strategies”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、会按任务需要访问外部网络、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;会按任务需要访问外部网络;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“Workflow / Scoping Questions / Decomposition Strategies”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: decomposition-reconstruction
description: Breaks complex systems into atomic components, maps their relationships, and reconstructs them i…
category: 通用
source: lyndonkl/claude
---
# decomposition-reconstruction
## 什么时候使用
- 把通用方向的常用动作沉淀成 Agent 可调用的技能 适合处理通用任务拆解、检查、交付和复盘,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤;通常不需要额外…
- 面向通用任务拆解、检查和交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Workflow / Scoping Questions / Decomposition Strategies」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;会按任务需要访问外部网络;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "decomposition-reconstruction" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Workflow / Scoping Questions / Decomposition Strategies
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Node.js | 读取文件、写入/修改文件、执行终端命令 | 会按任务需要访问外部网络
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Decomposition & Reconstruction
Workflow
Copy this checklist and track your progress:
Decomposition & Reconstruction Progress:
- [ ] Step 1: Define the system and goal
- [ ] Step 2: Decompose into components and relationships
- [ ] Step 3: Analyze component properties and interactions
- [ ] Step 4: Reconstruct for insight or optimization
- [ ] Step 5: Validate and deliver recommendations
Step 1: Define the system and goal
Ask user to describe the system (what are we analyzing), current problem or goal (what needs improvement, understanding, or redesign), boundaries (what's in scope vs out of scope), and success criteria (what would "better" look like). Clear boundaries prevent endless decomposition. See Scoping Questions for clarification prompts.
Step 2: Decompose into components and relationships
Break system into atomic parts that can't be meaningfully subdivided further. Identify relationships (dependencies, data flow, control flow, temporal ordering). Choose decomposition strategy based on system type. See Decomposition Strategies and resources/template.md for structured process.
Step 3: Analyze component properties and interactions
For each component, identify key properties (cost, time, complexity, reliability, etc.). Map interactions (which components depend on which). Identify critical paths, bottlenecks, or vulnerable points. For complex analysis → See resources/methodology.md for dependency mapping and critical path techniques.
Step 4: Reconstruct for insight or optimization
Based on goal, either: (a) Identify critical components (bottleneck, single point of failure, highest cost driver), (b) Redesign configuration (reorder, parallelize, eliminate, combine components), or (c) Simplify (remove unnecessary components). See Reconstruction Patterns for common approaches.
Step 5: Validate and deliver recommendations
Self-assess using resources/evaluators/rubric_decomposition_reconstruction.json (minimum score ≥ 3.5). Present decomposition-reconstruction.md with clear component breakdown, analysis findings (bottlenecks, dependencies), and actionable recommendations with expected impact.
Scoping Questions
To define the system:
- What is the system we're analyzing? (Be specific: "checkout flow" not "website")
- Where does it start and end? (Boundaries)
- What's in scope vs out of scope? (Prevents endless decomposition)
To clarify the goal:
- What problem are we solving? (Slow performance, high cost, complexity, unreliability)
- What would success look like? (Specific target: "reduce latency to <500ms", "cut costs by 30%")
- Are we optimizing, understanding, or redesigning?
To understand constraints:
- What can't we change? (Legacy systems, budget limits, regulatory requirements)
- What's the time horizon? (Quick wins vs long-term redesign)
- Who are the stakeholders? (Engineering, business, customers)
Decomposition Strategies
Choose based on system type:
Functional Decomposition
When: Business processes, software features, workflows Approach: Break down by function or task Example: E-commerce checkout → Browse products | Add to cart | Enter shipping | Payment | Confirmation
Structural Decomposition
When: Architecture, organizations, physical systems Approach: Break down by component or module Example: Web app → Frontend (React) | API (Node.js) | Database (PostgreSQL) | Cache (Redis)
Data Flow Decomposition
When: Pipelines, ETL processes, information systems Approach: Break down by data transformations Example: Analytics pipeline → Ingest raw events | Clean & validate | Aggregate metrics | Store in warehouse | Visualize in dashboard
Temporal Decomposition
When: Processes with sequential stages, timelines, user journeys Approach: Break down by time or sequence Example: Customer onboarding → Day 1: Signup | Day 2-7: Tutorial | Day 8-30: First value moment | Day 31+: Retention
Cost/Resource Decomposition
When: Budget analysis, resource allocation, optimization Approach: Break down by cost center or resource type Example: AWS bill → Compute ($5K) | Storage ($2K) | Data transfer ($1K) | Other ($500)
Depth guideline: Stop decomposing when further breakdown doesn't reveal useful insights or actionable opportunities.
Component Relationship Types
After decomposition, map relationships:
1. Dependency (A requires B):
- API service depends on database
- Frontend depends on API
- Critical for: Identifying cascading failures, understanding change impact
2. Data flow (A sends data to B):
- User input → Validation → Database → API response
- Critical for: Tracing information, finding transformation bottlenecks
3. Control flow (A triggers B):
- Button click triggers form submission
- Payment success triggers order fulfillment
- Critical for: Understanding execution paths, identifying race conditions
4. Temporal ordering (A before B in time):
- Authentication before authorization
- Compile before deploy
- Critical for: Sequencing, finding parallelization opportunities
5. Resource sharing (A and B compete for C):
- Multiple services share database connection pool
- Teams share budget
- Critical for: Identifying contention, resource constraints
Reconstruction Patterns
Pattern 1: Bottleneck Identification
Goal: Find what limits system throughput or speed Approach: Measure component properties (time, cost, capacity), identify critical path or highest value Example: DB query takes 80% of request time → Optimize DB query first
Pattern 2: Simplification
Goal: Reduce complexity by removing unnecessary parts Approach: Question necessity of each component, eliminate redundant or low-value parts Example: Workflow has 5 approval steps, 3 are redundant → Remove 3 steps
Pattern 3: Reordering
Goal: Improve efficiency by changing sequence Approach: Identify dependencies, move independent tasks earlier or parallel Example: Run tests parallel to build instead of sequential → Reduce CI time
Pattern 4: Parallelization
Goal: Increase throughput by doing work concurrently Approach: Find independent components, execute simultaneously Example: Fetch user data and product data in parallel instead of serial → Cut latency in half
Pattern 5: Substitution
Goal: Replace weak component with better alternative Approach: Identify underperforming component, find replacement Example: Replace synchronous API call with async message queue → Improve reliability
Pattern 6: Consolidation
Goal: Reduce overhead by combining similar components Approach: Find redundant or overlapping components, merge them Example: Consolidate 3 microservices doing similar work into 1 → Reduce operational overhead
Pattern 7: Modularization
Goal: Improve maintainability by separating concerns Approach: Identify tightly coupled components, separate with clear interfaces Example: Extract auth logic from monolith into separate service → Enable independent scaling
When NOT to Use This Skill
Skip decomposition-reconstruction if:
- System is already simple (3-5 obvious components, no complex interactions)
- Problem is not about system structure (purely execution issue, not design issue)
- You need creativity/ideation (not analysis) - use brainstorming instead
- System is poorly understood (need discovery/research first, not decomposition)
- Changes are impossible (no point analyzing if you can't act on findings)
Use instead:
- Simple system → Direct analysis or observation
- Execution problem → Project management, process improvement
- Need ideas → Brainstorming, design thinking
- Unknown system → Discovery interviews, research
- Unchangeable → Workaround planning, constraint optimization
Common Patterns by Domain
Software Architecture:
- Decompose: Modules, services, layers, data stores
- Reconstruct for: Microservices migration, performance optimization, reducing coupling
Business Processes:
- Decompose: Steps, decision points, handoffs, approvals
- Reconstruct for: Cycle time reduction, automation opportunities, removing waste
Problem Solving:
- Decompose: Sub-problems, dependencies, unknowns, constraints
- Reconstruct for: Task sequencing, identifying blockers, finding parallelizable work
Cost Optimization:
- Decompose: Cost centers, line items, resource usage
- Reconstruct for: Identifying biggest cost drivers, finding quick wins
User Experience:
- Decompose: User journey stages, interactions, pain points
- Reconstruct for: Simplifying flows, removing friction, improving conversion
System Reliability:
- Decompose: Components, failure modes, dependencies
- Reconstruct for: Identifying single points of failure, improving resilience
Quick Reference
Process:
- Define system and goal → Set boundaries
- Decompose → Break into components and relationships
- Analyze → Measure properties, map interactions
- Reconstruct → Optimize, simplify, or redesign
- Validate → Check against rubric, deliver recommendations
Decomposition strategies:
- Functional (by task), Structural (by component), Data flow, Temporal, Cost/Resource
Reconstruction patterns:
- Bottleneck ID, Simplification, Reordering, Parallelization, Substitution, Consolidation, Modularization
Resources:
- resources/template.md - Structured decomposition process with templates
- resources/methodology.md - Advanced techniques (dependency graphs, critical path analysis, hierarchical decomposition)
- resources/evaluators/rubric_decomposition_reconstruction.json - Quality checklist
Deliverable: decomposition-reconstruction.md with component breakdown, analysis, and recommendations
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核