sc-implement

Q: How do I install sc-implement?

Run npx skills add https://github.com/tony363/superclaude --skill sc-implement in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sc-implement/ (personal) or .claude/skills/sc-implement/ (project).

by tony363AI & Agents

Feature implementation with intelligent persona activation, task orchestration, and MCP integration. Use when implementing features, APIs, components, services, or coordinating multi-agent development. Triggers on requests for code implementation, feature development, or complex task orchestration.

Installs: 94
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/tony363/superclaude --skill sc-implement

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads sc-implement automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

FAQ

What does the sc-implement skill do?

How do I install sc-implement?

Run npx skills add https://github.com/tony363/superclaude --skill sc-implement in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sc-implement/ (personal) or .claude/skills/sc-implement/ (project).

Which AI agents can use sc-implement?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is sc-implement safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains sc-implement?

It is published by tony363 in tony363/superclaude. It has 94 recorded installs.

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sc-implement

Install

Install

Describe the task

Review the result

SKILL.md

Implementation Skill

Quick Start

Behavioral Flow

Flags

Personas Activated

MCP Integration

PAL MCP (Always Use for Quality)

PAL Usage Patterns

Rube MCP (Automation & Integration)

Rube Usage Patterns

MCP-Powered Loop Mode

Guardrails

Evidence Requirements

Examples

React Component

API with Tests

Complex Orchestration

Loop Mode & Learning

How Learning Works

Loop Flags

Example with Learning

Learned Skills Location

Resources

FAQ

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