Add Skill: mcp-server-setup

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git
Score: 1.0

skills/mcp-server-setup/SKILL.md

@@ -0,0 +1,98 @@
+---
+name: mcp-server-setup
+version: 1.0.0
+description: Set up an MCP server to integrate PipesHub with any MCP-compatible client.
+inputs:
+- MCP server configuration details
+- PipesHub credentials
+steps:
+- 'Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`'
+- 'Step 2: Navigate to the cloned directory with `cd mcp-server`'
+- 'Step 3: Run the interactive installer by executing `./install.sh`'
+- 'Step 4: Follow the prompts in the installer to configure the server, including
+  setting up graph DB, message broker, and KV store'
+- 'Step 5: The installer will generate a `.env` file with necessary environment variables.
+  Ensure these are correctly set'
+- 'Step 6: Start the MCP server by running `docker-compose up -d`'
+outputs:
+- Running MCP server
+- .env file generated
+tags: []
+metadata:
+  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# mcp-server-setup
+
+Set up an MCP server to integrate PipesHub with any MCP-compatible client.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install docker docker-compose
+```
+
+**Setup steps:**
+
+1. Ensure Docker and Docker Compose are installed on your system.
+1. Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
+
+## Key Files
+
+- `path/to/install.sh - Script to run the interactive installer`
+- `path/to/docker-compose.yml - Configuration for Docker services`
+
+## Steps
+
+1. Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
+2. Step 2: Navigate to the cloned directory with `cd mcp-server`
+3. Step 3: Run the interactive installer by executing `./install.sh`
+4. Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store
+5. Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set
+6. Step 6: Start the MCP server by running `docker-compose up -d`
+
+## Implementation Details
+
+```python
+```bash
+./install.sh
+```
+Run this script to start the installation process.
+```
+
+```python
+```yaml
+docker-compose:
+  version: '3.9'
+services:
+  mcp-server:
+    image: pipeshubai/mcp-server:latest
+    environment:
+      - PIPESHUB_API_KEY=your_api_key_here
+```
+This snippet shows how to configure the Docker Compose file.
+```
+
+## Inputs
+
+- MCP server configuration details
+- PipesHub credentials
+
+## Outputs
+
+- Running MCP server
+- .env file generated
+
+## Failure Modes
+
+- Installer fails to run due to missing dependencies or incorrect configuration
+- Docker Compose setup issues preventing server from starting
+
+## Source
+
+Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
+Confidence: 0.95

skills/mcp-server-setup/commands.md

@@ -0,0 +1,6 @@
+# Commands: mcp-server-setup
+
+## Available Commands
+
+- `/skill mcp-server-setup` — Load this skill
+- `/run mcp-server-setup` — Execute workflow

skills/mcp-server-setup/examples.md

@@ -0,0 +1,10 @@
+# Examples: mcp-server-setup
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: MCP server configuration details, PipesHub credentials
+# Process: Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git` → Step 2: Navigate to the cloned directory with `cd mcp-server` → Step 3: Run the interactive installer by executing `./install.sh`
+# Outputs: Running MCP server, .env file generated
+```

skills/mcp-server-setup/metadata.json

@@ -0,0 +1,29 @@
+{
+  "name": "mcp-server-setup",
+  "version": "1.0.0",
+  "goal": "Set up an MCP server to integrate PipesHub with any MCP-compatible client.",
+  "inputs": [
+    "MCP server configuration details",
+    "PipesHub credentials"
+  ],
+  "steps": [
+    "Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`",
+    "Step 2: Navigate to the cloned directory with `cd mcp-server`",
+    "Step 3: Run the interactive installer by executing `./install.sh`",
+    "Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store",
+    "Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set",
+    "Step 6: Start the MCP server by running `docker-compose up -d`"
+  ],
+  "outputs": [
+    "Running MCP server",
+    ".env file generated"
+  ],
+  "failure_modes": [
+    "Installer fails to run due to missing dependencies or incorrect configuration",
+    "Docker Compose setup issues preventing server from starting"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow is specific but can be adapted for different deployment environments and configurations.",
+  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
+  "score": 1.0
+}

skills/mcp-server-setup/tests.md

@@ -1,4 +1,4 @@
-# Tests: unifai-workflow-execution
+# Tests: mcp-server-setup
 
 ## Test Checklist
 

skills/unifai-workflow-execution/SKILL.md

@@ -1,96 +0,0 @@
----
-name: unifai-workflow-execution
-version: 1.0.0
-description: Execute a multi-agent workflow on the UnifAI platform using a specified
-  blueprint and user prompt.
-inputs:
-- blueprint_id or blueprint_name
-- user_shortcut
-- user_question
-steps:
-- 'Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id
-  method)'
-- 'Step 2: Create a new session from the blueprint (create_session method)'
-- 'Step 3: Submit the session for background execution with the user prompt (submit_session
-  method)'
-- 'Step 4: Poll session status until execution completes (poll_session_status method)'
-outputs:
-- session_id
-- workflow_id
-tags: []
-metadata:
-  source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# unifai-workflow-execution
-
-Execute a multi-agent workflow on the UnifAI platform using a specified blueprint and user prompt.
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install requests urllib3
-```
-
-**Setup steps:**
-
-1. Install required dependencies using pip install requests urllib3
-1. Ensure the environment variables are set correctly (BLUEPRINT_ID, BLUEPRINT_NAME, USER_SHORTCUT, POLLING_INTERVAL, UNIFAI_BASE_URL)
-
-## Key Files
-
-- `scripts/execution_workflow.py - Main script for workflow execution`
-
-## Steps
-
-1. Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method)
-2. Step 2: Create a new session from the blueprint (create_session method)
-3. Step 3: Submit the session for background execution with the user prompt (submit_session method)
-4. Step 4: Poll session status until execution completes (poll_session_status method)
-
-## Implementation Details
-
-```python
-resolve_blueprint_id(client: UnifAIClient) -> str
-{...}
-# Resolve the blueprint ID from either direct ID or name lookup.
-```
-
-```python
-create_session(client: UnifAIClient, blueprint_id: str) -> str
-{...}
-# Create a new session from the blueprint.
-```
-
-```python
-submit_session(client: UnifAIClient, session_id: str) -> dict
-{...}
-# Submit the session for background execution with the user prompt.
-```
-
-## Inputs
-
-- blueprint_id or blueprint_name
-- user_shortcut
-- user_question
-
-## Outputs
-
-- session_id
-- workflow_id
-
-## Failure Modes
-
-- Blueprint name not found or not unique - error during blueprint resolution
-- Session creation fails - error from API response
-- Session submission fails - error from API response
-- Polling session status fails - error from API response
-
-## Source
-
-Extracted from: [https://github.com/redhat-community-ai-tools/UnifAI.git](https://github.com/redhat-community-ai-tools/UnifAI.git)
-Confidence: 0.95

skills/unifai-workflow-execution/commands.md

@@ -1,6 +0,0 @@
-# Commands: unifai-workflow-execution
-
-## Available Commands
-
-- `/skill unifai-workflow-execution` — Load this skill
-- `/run unifai-workflow-execution` — Execute workflow

skills/unifai-workflow-execution/examples.md

@@ -1,10 +0,0 @@
-# Examples: unifai-workflow-execution
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: blueprint_id or blueprint_name, user_shortcut, user_question
-# Process: Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method) → Step 2: Create a new session from the blueprint (create_session method) → Step 3: Submit the session for background execution with the user prompt (submit_session method)
-# Outputs: session_id, workflow_id
-```

skills/unifai-workflow-execution/metadata.json

@@ -1,30 +0,0 @@
-{
-  "name": "unifai-workflow-execution",
-  "version": "1.0.0",
-  "goal": "Execute a multi-agent workflow on the UnifAI platform using a specified blueprint and user prompt.",
-  "inputs": [
-    "blueprint_id or blueprint_name",
-    "user_shortcut",
-    "user_question"
-  ],
-  "steps": [
-    "Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method)",
-    "Step 2: Create a new session from the blueprint (create_session method)",
-    "Step 3: Submit the session for background execution with the user prompt (submit_session method)",
-    "Step 4: Poll session status until execution completes (poll_session_status method)"
-  ],
-  "outputs": [
-    "session_id",
-    "workflow_id"
-  ],
-  "failure_modes": [
-    "Blueprint name not found or not unique - error during blueprint resolution",
-    "Session creation fails - error from API response",
-    "Session submission fails - error from API response",
-    "Polling session status fails - error from API response"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow is specific to the UnifAI platform and its multi-agent system, but can be adapted for similar systems with a similar architecture.",
-  "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
-  "score": 1.0
-}