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

1. Analyze - Examine requirements, detect technology context
2. Plan - Choose approach, activate relevant personas
3. Generate - Create implementation with framework best practices
4. Validate - Apply security, quality, and principles validation

- Run KISS validation: python .claude/skills/sc-principles/scripts/validatekiss.py --scope-root . --json - Run Purity validation: python .claude/skills/sc-principles/scripts/validatepurity.py --scope-root . --json - If blocked: Refactor code to comply before proceeding

1. Integrate - Update docs, provide testing recommendations

Flags

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)

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)

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:

1. Iteration N - Implement feature
2. PAL codereview - Assess quality (target: 70+ score)
3. PAL debug - Investigate any issues found
4. Iteration N+1 - Apply improvements
5. 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

1. Feedback Recording - Each iteration's quality scores and improvements are persisted
2. Skill Extraction - Successful patterns are extracted when quality threshold is met
3. Skill Retrieval - Relevant learned skills are injected into subsequent tasks
4. Effectiveness Tracking - Applied skills are tracked for success rate

Loop Flags

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/selectagent.py](scripts/selectagent.py) - Agent selection logic
• [scripts/evidencegate.py](scripts/evidencegate.py) - Evidence validation
• [scripts/skilllearn.py](scripts/skilllearn.py) - Skill learning management