Comprehensive MCP orchestration skill integrating PAL MCP (reasoning, consensus, debugging) and Rube MCP (500+ app automations). Central hub for all MCP-powered workflows.
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.
# 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
| 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 |
Use consensus for:
- Architectural decisions (2-3 models)
- Security validations (security-focused models)
- Technology choices (diverse perspectives)
- Complex trade-off analysis
Recommended model combinations:
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
Use codereview for:
- Pre-merge validation
- Security audits
- Performance reviews
- Architecture compliance
Review types: full, security, performance, quick
| 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 |
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)
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
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
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
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
1. PAL debug - Root cause analysis
2. Implement fix locally
3. PAL codereview - Validate fix
4. Rube MULTI_EXECUTE - Update tickets, notify team
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
| 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 |
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)
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"]}
)
/sc:mcp analyze --pal consensus --models "gpt-5.2,gemini-3-pro,deepseek" \
--question "Is event sourcing appropriate for this use case?"
/sc:mcp automate --rube --apps github,slack \
--workflow "On PR merge, post summary to #releases"
--save-recipe --schedule "0 9 * * 1-5"
/sc:mcp orchestrate --pal debug --rube \
--issue "Memory leak in production" \
--notify slack,jira --full-validation
| 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 |
mcp-pal-enabled - Apply PAL to any agentmcp-rube-enabled - Apply Rube to any agentSearch for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Start voice calls via the OpenClaw voice-call plugin.
Notion API for creating and managing pages, databases, and blocks.
Gemini CLI for one-shot Q&A, summaries, and generation.
Category:developer