Implementation Skill

Comprehensive feature implementation with coordinated expertise and systematic development.

Quick Start

Basic implementation

/sc:implement [feature-description] --type component|api|service|feature

With framework

/sc:implement dashboard widget --framework react|vue|express

Complex orchestration

/sc:implement [task] --orchestrate --strategy systematic|agile|enterprise

Behavioral Flow

• Analyze - Examine requirements, detect technology context

• Plan - Choose approach, activate relevant personas

• Generate - Create implementation with framework best practices

• Validate - Apply security, quality, and principles validation

• Run KISS validation: python .claude/skills/sc-principles/scripts/validate_kiss.py --scope-root . --json

• Run Purity validation: python .claude/skills/sc-principles/scripts/validate_purity.py --scope-root . --json

• If blocked: Refactor code to comply before proceeding

• Integrate - Update docs, provide testing recommendations

Flags

Flag Type Default Description

--type

string feature component, api, service, feature

--framework

string auto react, vue, express, etc.

--safe

bool false Enable safety constraints

--with-tests

bool false Generate tests alongside code

--fast-codex

bool false Streamlined path, skip multi-persona

--orchestrate

bool false Enable hierarchical task breakdown

--strategy

string systematic systematic, agile, enterprise, parallel, adaptive

--delegate

bool false Enable intelligent delegation

--principles

bool true Enable KISS/Purity validation

--strict-principles

bool false Treat principles warnings as errors

Personas Activated

• architect - System design, architectural decisions

• frontend - UI/component implementation

• backend - API/service implementation

• security - Security validation, auth concerns

• qa-specialist - Testing, quality assurance

• devops - Infrastructure, deployment

• project-manager - Task coordination (with --orchestrate)

• code-warden - Principles enforcement (KISS, Purity)

MCP Integration

PAL MCP (Always Use for Quality)

Tool When to Use Purpose

mcp__pal__consensus

Architectural decisions Multi-model validation before major changes

mcp__pal__codereview

Code quality Review implementation quality, security, performance

mcp__pal__precommit

Before commit Validate all changes before git commit

mcp__pal__debug

Implementation issues Root cause analysis for bugs encountered

mcp__pal__thinkdeep

Complex features Multi-stage analysis for complex implementations

mcp__pal__planner

Large features Sequential planning for multi-step implementations

mcp__pal__apilookup

Dependencies Get current API/SDK documentation

mcp__pal__challenge

Code review feedback Critically evaluate review suggestions

PAL Usage Patterns

Consensus for architectural decision

mcp__pal__consensus( models=[ {"model": "gpt-5.2", "stance": "for"}, {"model": "gemini-3-pro", "stance": "against"}, {"model": "deepseek", "stance": "neutral"} ], step="Evaluate: Should we use Redux or Context API for state management?" )

Pre-commit validation

mcp__pal__precommit( path="/path/to/repo", step="Validating implementation changes", findings="Security, performance, completeness checks", confidence="high" )

Code review after implementation

mcp__pal__codereview( review_type="full", step="Reviewing new authentication implementation", findings="Quality, security, performance, architecture", relevant_files=["/src/auth/login.ts", "/src/auth/middleware.ts"] )

Debug implementation issue

mcp__pal__debug( step="Investigating why API returns 500 on edge case", hypothesis="Null check missing for optional field", confidence="medium" )

Rube MCP (Automation & Integration)

Tool When to Use Purpose

mcp__rube__RUBE_SEARCH_TOOLS

External services Find APIs, SDKs, integrations

mcp__rube__RUBE_MULTI_EXECUTE_TOOL

CI/CD, notifications Trigger builds, notify team, update tickets

mcp__rube__RUBE_REMOTE_WORKBENCH

Code generation Bulk code operations, transformations

mcp__rube__RUBE_CREATE_UPDATE_RECIPE

Reusable workflows Save implementation patterns as recipes

mcp__rube__RUBE_MANAGE_CONNECTIONS

Verify integrations Ensure external service connections

Rube Usage Patterns

Search for integration tools

mcp__rube__RUBE_SEARCH_TOOLS(queries=[ {"use_case": "send slack message", "known_fields": "channel_name:dev-updates"}, {"use_case": "create github pull request", "known_fields": "repo:myapp"} ])

Notify team and update ticket on completion

mcp__rube__RUBE_MULTI_EXECUTE_TOOL(tools=[ {"tool_slug": "SLACK_SEND_MESSAGE", "arguments": { "channel": "#dev-updates", "text": "Feature implemented: User authentication flow" }}, {"tool_slug": "JIRA_UPDATE_ISSUE", "arguments": { "issue_key": "PROJ-123", "status": "In Review" }}, {"tool_slug": "GITHUB_CREATE_PULL_REQUEST", "arguments": { "repo": "myapp", "title": "feat: Add user authentication", "base": "main", "head": "feature/auth" }} ])

Save implementation workflow as recipe

mcp__rube__RUBE_CREATE_UPDATE_RECIPE( name="Feature Implementation Workflow", description="Standard flow for implementing features with notifications", workflow_code="..." )

MCP-Powered Loop Mode

When --loop is enabled, MCP tools are used between iterations:

• Iteration N - Implement feature

• PAL codereview - Assess quality (target: 70+ score)

• PAL debug - Investigate any issues found

• Iteration N+1 - Apply improvements

• PAL precommit - Final validation before marking complete

Guardrails

• Start in analysis mode; produce scoped plan before touching files

• Only mark complete when referencing concrete repo changes (filenames + diff hunks)

• Return plan + next actions if tooling unavailable

• Prefer minimal viable change; skip speculative scaffolding

• Escalate to security persona before modifying auth/secrets/permissions

Evidence Requirements

This skill requires evidence. You MUST:

• Show actual file diffs or code changes

• Reference test results or lint output

• Never claim code exists without proof

Examples

React Component

/sc:implement user profile component --type component --framework react

API with Tests

/sc:implement user auth API --type api --safe --with-tests

Complex Orchestration

/sc:implement "enterprise auth system" --orchestrate --strategy systematic --delegate

Loop Mode & Learning

When using --loop , this skill integrates with the skill persistence layer for cross-session learning:

How Learning Works

• Feedback Recording - Each iteration's quality scores and improvements are persisted

• Skill Extraction - Successful patterns are extracted when quality threshold is met

• Skill Retrieval - Relevant learned skills are injected into subsequent tasks

• Effectiveness Tracking - Applied skills are tracked for success rate

Loop Flags

Flag Type Default Description

--loop

int 3 Enable iterative improvement (max 5)

--learn

bool true Enable learning from this session

--auto-promote

bool false Auto-promote high-quality skills

Example with Learning

Iterative implementation with learning

/sc:implement auth flow --loop 3 --learn

View learned skills

python scripts/skill_learn.py '{"command": "stats"}'

Retrieve relevant skills

python scripts/skill_learn.py '{"command": "retrieve", "task": "auth"}'

Learned Skills Location

Promoted skills are stored in:

.claude/skills/learned/ ├── SKILL.md # Index ├── learned-backend-auth/ # Example promoted skill │ ├── SKILL.md │ └── metadata.json

Resources

• scripts/select_agent.py - Agent selection logic

• scripts/evidence_gate.py - Evidence validation

• scripts/skill_learn.py - Skill learning management