Implementation Approach @shinpr
claudesonnetSkillSelects implementation strategy (vertical slice, horizontal, or hybrid) with risk assessment. Use when planning feature implementation.
Install
curl -o ~/.claude/skills/implementation-approach/SKILL.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/skills-en/implementation-approach/SKILL.mdDescription
Implementation Strategy Selection Framework (Meta-cognitive Approach)
Meta-cognitive Strategy Selection Process
Phase 1: Comprehensive Current State Analysis
Core Question: "What does the existing implementation look like?"
Analysis Framework
Meta-cognitive Question List
- What is the true responsibility of this implementation?
- Which parts are business essence and which derive from technical constraints?
- What dependencies or implicit preconditions are unclear from the code?
- What benefits and constraints does the current design bring?
Phase 2: Strategy Exploration and Creation
Core Question: "When determining before -> after, what implementation patterns or strategies should be referenced?"
Strategy Discovery Process
Reference Strategy Patterns (Creative Combinations Encouraged)
Legacy Handling Strategies:
- Strangler Pattern: Gradual migration through phased replacement
- Facade Pattern: Complexity hiding through unified interface
- Adapter Pattern: Bridge with existing systems
New Development Strategies:
- Feature-driven Development: Vertical implementation prioritizing user value
- Foundation-driven Development: Foundation-first construction prioritizing stability
- Risk-driven Development: Prioritize addressing maximum risk elements
Integration/Migration Strategies:
- Proxy Pattern: Transparent feature extension
- Decorator Pattern: Phased enhancement of existing features
- Bridge Pattern: Flexibility through abstraction
Important: The optimal solution is discovered through creative thinking according to each project's context.
Phase 3: Risk Assessment and Control
Core Question: "What risks arise when applying this to existing implementation, and what's the best way to control them?"
Risk Analysis Matrix
Risk Control Strategies
Phase 4: Constraint Compatibility Verification
Core Question: "What are this project's constraints?"
Constraint Checklist
Phase 5: Implementation Approach Decision
Select optimal solution from basic implementation approaches (creative combinations encouraged):
Vertical Slice (Feature-driven)
Characteristics: Vertical implementation across all layers by feature unit Application Conditions: Low inter-feature dependencies, output in user-usable form, changes needed across all architecture layers Verification Method: End-user value delivery at each feature completion
Horizontal Slice (Foundation-driven)
Characteristics: Phased construction by architecture layer Application Conditions: Foundation system stability important, multiple features depend on common foundation, layer-by-layer verification effective Verification Method: Integrated operation verification when all foundation layers complete
Hybrid (Creative Combination)
Characteristics: Flexible combination according to project characteristics Application Conditions: Unclear requirements, need to change approach per phase, transition from prototyping to full implementation Verification Method: Verify at appropriate L1/L2/L3 levels according to each phase's goals
Phase 6: Decision Rationale Documentation
Design Doc Documentation: Clearly specify implementation strategy selection reasons and rationale.
Verification Level Definitions
Priority for completion verification of each task:
- L1: Functional Operation Verification - Operates as end-user feature (e.g., search executable)
- L2: Test Operation Verification - New tests added and passing (e.g., type definition tests)
- L3: Build Success Verification - No compile errors (e.g., interface definitions)
Priority: L1 > L2 > L3 in order of verifiability importance
Integration Point Definitions
Define integration points according to selected strategy:
- Strangler-based: When switching between old and new systems for each feature
- Feature-driven: When users can actually use the feature
- Foundation-driven: When all architecture layers are ready and E2E tests pass
- Hybrid: When individual goals defined for each phase are achieved
Anti-patterns
- Pattern Fixation: Selecting only from listed strategies without considering unique combinations
- Insufficient Analysis: Skipping Phase 1 analysis framework before strategy selection
- Risk Neglect: Starting implementation without Phase 3 risk analysis matrix
- Constraint Ignorance: Deciding strategy without checking Phase 4 constraint checklist
- Rationale Omission: Selecting strategy without using Phase 6 documentation template
Guidelines for Meta-cognitive Execution
- Leverage Known Patterns: Use as starting point, explore creative combinations
- Active WebSearch Use: Research implementation examples from similar tech stacks
- Apply 5 Whys: Pursue root causes to grasp essence
- Multi-perspective Evaluation: Comprehensively evaluate from each Phase 1-4 perspective
- **C
Capabilities
- What is the true responsibility of this implementation?
- Which parts are business essence and which derive from technical constraints?
- What dependencies or implicit preconditions are unclear from the code?
- What benefits and constraints does the current design bring?
- Strangler Pattern: Gradual migration through phased replacement
- Facade Pattern: Complexity hiding through unified interface
- Adapter Pattern: Bridge with existing systems
- Feature-driven Development: Vertical implementation prioritizing user value
- Foundation-driven Development: Foundation-first construction prioritizing stability
- Risk-driven Development: Prioritize addressing maximum risk elements
- Proxy Pattern: Transparent feature extension
- Decorator Pattern: Phased enhancement of existing features
- Bridge Pattern: Flexibility through abstraction
- L1: Functional Operation Verification - Operates as end-user feature (e.g., search executable)
- L2: Test Operation Verification - New tests added and passing (e.g., type definition tests)
Related Items
From the same repository — designed to work together
curl -o ~/.claude/skills/implementation-approach/SKILL.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/skills-en/implementation-approach/SKILL.md && curl -o ~/.claude/agents/investigator.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/investigator.md && curl -o ~/.claude/agents/ui-spec-designer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/ui-spec-designer.md && curl -o ~/.claude/agents/rule-advisor.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/rule-advisor.md && curl -o ~/.claude/agents/task-decomposer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/task-decomposer.md && curl -o ~/.claude/agents/document-reviewer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/document-reviewer.md && curl -o ~/.claude/agents/skill-reviewer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/skill-reviewer.mdInvestigator
Comprehensively collects problem-related information and creates evidence matrix. Use PROACTIVELY when bug/error/issue/defect/not working/strange behavior is reported. Reports only observations without proposing solutions.
curl -o ~/.claude/agents/investigator.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/investigator.mdUi Spec Designer
Creates UI Specifications from PRD and optional prototype code. Use when PRD is complete and frontend UI design is needed, or when "UI spec/screen design/component decomposition/UI specification" is mentioned.
curl -o ~/.claude/agents/ui-spec-designer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/ui-spec-designer.mdRule Advisor
Selects optimal rulesets for tasks and performs metacognitive analysis. MUST BE USED before any implementation task starts (CLAUDE.md required process). Analyzes task essence with task-analyzer skill and returns structured JSON.
curl -o ~/.claude/agents/rule-advisor.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/rule-advisor.mdTask Decomposer
Decomposes work plans into independent single-commit granularity tasks in docs/plans/tasks. Use PROACTIVELY when work plan (docs/plans/) is created, or when "task decomposition/split/decompose" is mentioned.
curl -o ~/.claude/agents/task-decomposer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/task-decomposer.mdDocument Reviewer
Reviews document consistency and completeness, providing approval decisions. Use PROACTIVELY after PRD/UI Spec/Design Doc/work plan creation, or when "document review/approval/check" is mentioned. Detects contradictions and rule violations with improvement suggestions.
curl -o ~/.claude/agents/document-reviewer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/document-reviewer.mdSkill Reviewer
Evaluates skill file quality against optimization patterns and editing principles. Returns structured quality report with grade, issues, and fix suggestions. Use when reviewing created or modified skill content.
curl -o ~/.claude/agents/skill-reviewer.md https://raw.githubusercontent.com/shinpr/ai-coding-project-boilerplate/main/.claude/agents-en/skill-reviewer.md