sc-mcp
Comprehensive MCP orchestration skill integrating PAL MCP (reasoning, consensus, debugging) and Rube MCP (500+ app automations). Central hub for all MCP-powered workflows.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/tony363/superclaude --skill sc-mcp- 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-mcp 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.
MCP Orchestration Skill
Central orchestration hub for PAL MCP and Rube MCP capabilities. Use this skill for complex workflows requiring multi-model reasoning, external service integration, or both.
Quick Start
# PAL-powered analysis
/sc:mcp analyze --pal consensus --question "Should we use microservices?"
# Rube-powered automation
/sc:mcp automate --rube --apps slack,github --workflow "notify on PR"
# Combined orchestration
/sc:mcp orchestrate --pal thinkdeep --rube --full-validation
PAL MCP Integration
Available Tools
| Tool | Invocation | Purpose |
|---|---|---|
chat | mcp__pal__chat | Collaborative thinking, brainstorming |
thinkdeep | mcp__pal__thinkdeep | Multi-stage investigation, complex analysis |
planner | mcp__pal__planner | Sequential planning with branching |
consensus | mcp__pal__consensus | Multi-model voting on decisions |
codereview | mcp__pal__codereview | Systematic code quality analysis |
precommit | mcp__pal__precommit | Git change validation |
debug | mcp__pal__debug | Root cause analysis |
challenge | mcp__pal__challenge | Force critical thinking |
apilookup | mcp__pal__apilookup | Current API/SDK documentation |
listmodels | mcp__pal__listmodels | Available AI models |
clink | mcp__pal__clink | External CLI integration |
PAL Workflows
Consensus Decision Making
Use consensus for:
- Architectural decisions (2-3 models)
- Security validations (security-focused models)
- Technology choices (diverse perspectives)
- Complex trade-off analysis
Recommended model combinations:
- Architectural: gpt-5.2 (for), gemini-3-pro (against), deepseek (neutral)
- Security: gpt-5.2 (security focus), gemini-3-pro (attack surface)
- Performance: gpt-5.2 (optimization), deepseek (efficiency)
Debug Investigation
Use debug for:
- Complex bugs with unclear causes
- Performance issues
- Race conditions
- Memory leaks
- Integration problems
Debug confidence levels: exploring -> low -> medium -> high -> very_high -> almost_certain -> certain
Code Review
Use codereview for:
- Pre-merge validation
- Security audits
- Performance reviews
- Architecture compliance
Review types: full, security, performance, quick
Rube MCP Integration
Available Tools
| Tool | Invocation | Purpose |
|---|---|---|
SEARCH_TOOLS | mcp__rube__RUBE_SEARCH_TOOLS | Discover available integrations |
GET_RECIPE_DETAILS | mcp__rube__RUBE_GET_RECIPE_DETAILS | Get details of saved recipes |
MULTI_EXECUTE | mcp__rube__RUBE_MULTI_EXECUTE_TOOL | Parallel tool execution |
REMOTE_BASH | mcp__rube__RUBE_REMOTE_BASH_TOOL | Remote shell commands |
REMOTE_WORKBENCH | mcp__rube__RUBE_REMOTE_WORKBENCH | Python sandbox execution |
CREATE_RECIPE | mcp__rube__RUBE_CREATE_UPDATE_RECIPE | Save reusable workflows |
EXECUTE_RECIPE | mcp__rube__RUBE_EXECUTE_RECIPE | Run saved recipes |
FIND_RECIPE | mcp__rube__RUBE_FIND_RECIPE | Search existing recipes |
MANAGE_CONNECTIONS | mcp__rube__RUBE_MANAGE_CONNECTIONS | App authentication |
GET_SCHEMAS | mcp__rube__RUBE_GET_TOOL_SCHEMAS | Tool input schemas |
MANAGE_SCHEDULE | mcp__rube__RUBE_MANAGE_RECIPE_SCHEDULE | Recipe scheduling |
Rube Workflows
External Integration Flow
1. SEARCH_TOOLS - Find relevant tools for use case
2. GET_SCHEMAS - Get input requirements (if schemaRef returned)
3. MANAGE_CONNECTIONS - Verify/create auth
4. MULTI_EXECUTE - Execute tools
5. CREATE_RECIPE - Save for reuse (optional)
Bulk Processing Flow
1. SEARCH_TOOLS - Find data source/destination tools
2. REMOTE_WORKBENCH - Process with Python helpers:
- run_composio_tool() - Execute Composio tools
- invoke_llm() - AI processing
- upload_local_file() - Export results
- proxy_execute() - Direct API calls
Supported Apps (500+)
Communication: Slack, Discord, Teams, Gmail, Outlook, WhatsApp, Telegram Development: GitHub, GitLab, Jira, Linear, Asana, Vercel Productivity: Google Workspace, Notion, Airtable, Trello Data: Snowflake, BigQuery, Datadog, Amplitude AI: OpenAI, Anthropic, Replicate
Combined Orchestration Patterns
Pattern 1: Research + Decide + Execute
1. PAL thinkdeep - Investigate problem deeply
2. PAL consensus - Get multi-model decision
3. Rube SEARCH_TOOLS - Find execution tools
4. Rube MULTI_EXECUTE - Implement decision
Pattern 2: Review + Validate + Notify
1. PAL codereview - Review code changes
2. PAL precommit - Validate git changes
3. Rube MULTI_EXECUTE - Send notifications (Slack, email)
4. Rube CREATE_RECIPE - Save for CI/CD
Pattern 3: Debug + Fix + Verify
1. PAL debug - Root cause analysis
2. Implement fix locally
3. PAL codereview - Validate fix
4. Rube MULTI_EXECUTE - Update tickets, notify team
Pattern 4: Plan + Consensus + Automate
1. PAL planner - Create implementation plan
2. PAL consensus - Validate approach with multiple models
3. Rube MULTI_EXECUTE - Execute across apps
4. Rube MULTI_EXECUTE - Execute across apps
5. Rube CREATE_RECIPE - Save as reusable workflow
Flags
| Flag | Type | Default | Description |
|---|---|---|---|
--pal | string | - | PAL tool: chat, thinkdeep, planner, consensus, codereview, precommit, debug |
--rube | bool | false | Enable Rube MCP integration |
--apps | string | - | Comma-separated apps for Rube |
--models | string | auto | Models for consensus (comma-separated) |
--full-validation | bool | false | Run all PAL validators |
--save-recipe | bool | false | Save workflow as Rube recipe |
--schedule | string | - | Cron expression for recipe scheduling |
Behavioral Flow
- Analyze - Understand what MCP capabilities are needed
- Discover - Use RUBE_SEARCH_TOOLS for external needs, listmodels for PAL
- Plan - Create execution plan (PAL planner or RUBE_CREATE_PLAN)
- Validate - Use consensus for critical decisions
- Execute - Run PAL analysis and/or Rube tools
- Persist - Save recipes, store memory for continuity
- Report - Present findings with tool attribution
Memory & State Management
PAL Continuation
Use continuation_id to maintain context across PAL tool calls:
# First call returns continuation_id
result = mcp__pal__thinkdeep(...)
continuation_id = result["continuation_id"]
# Subsequent calls reuse it
result = mcp__pal__thinkdeep(..., continuation_id=continuation_id)
Rube Session & Memory
Use session_id and memory for Rube continuity:
# First search generates session_id
result = mcp__rube__RUBE_SEARCH_TOOLS(..., session={"generate_id": True})
session_id = result["session_id"]
# Subsequent calls reuse session and build memory
result = mcp__rube__RUBE_MULTI_EXECUTE_TOOL(
...,
session_id=session_id,
memory={"slack": ["Channel general is C123"]}
)
Examples
Multi-Model Architecture Review
/sc:mcp analyze --pal consensus --models "gpt-5.2,gemini-3-pro,deepseek" \
--question "Is event sourcing appropriate for this use case?"
Automated PR Workflow
/sc:mcp automate --rube --apps github,slack \
--workflow "On PR merge, post summary to #releases"
--save-recipe --schedule "0 9 * * 1-5"
Full Investigation Pipeline
/sc:mcp orchestrate --pal debug --rube \
--issue "Memory leak in production" \
--notify slack,jira --full-validation
Guardrails
- Always search tools before executing unknown integrations
- Use consensus for decisions with >$1000 impact
- Validate schemas before multi-execute
- Store memory for frequently used IDs
- Check connection status before automation
- Use thinking_mode=high for complex PAL analysis
Error Handling
| Error | Recovery |
|---|---|
| PAL model unavailable | Fall back to different model |
| Rube connection missing | Prompt MANAGE_CONNECTIONS |
| Tool schema unknown | Call GET_SCHEMAS first |
| Rate limited | Use backoff in REMOTE_WORKBENCH |
| Recipe not found | Search or create new |
Resources
- PAL MCP: codereview, debug, consensus, thinkdeep, precommit, planner, chat, challenge, apilookup
- Rube MCP: 500+ app integrations via Composio
- Trait:
mcp-pal-enabled- Apply PAL to any agent - Trait:
mcp-rube-enabled- Apply Rube to any agent
FAQ
What does the sc-mcp skill do?
Comprehensive MCP orchestration skill integrating PAL MCP (reasoning, consensus, debugging) and Rube MCP (500+ app automations). Central hub for all MCP-powered workflows.
How do I install sc-mcp?
Run npx skills add https://github.com/tony363/superclaude --skill sc-mcp 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-mcp/ (personal) or .claude/skills/sc-mcp/ (project).
Which AI agents can use sc-mcp?
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-mcp 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-mcp?
It is published by tony363 in tony363/superclaude. It has 94 recorded installs.
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