Add Publisher v2 + 5 extracted skills
Publisher fixes: - Checkout new branch before push (was pushing main ref) - Verify files staged before commit - Handle duplicate files gracefully - Clean error reporting per stage Skills merged to main: - mcp-server-setup (from pipeshub-ai) - research-pipeline (from Blacknode) - multi-agent-sequential-workflow (from Fast-LLM-Agent-MCP) - unifai-workflow-execution (from UnifAI) - code-review-agent-workflow (from AgentKit)
This commit is contained in:
Epictetus
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---
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name: code-review-agent-workflow
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version: 1.0.0
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description: Automate the code review process using a multi-step workflow with human-in-the-loop
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approval.
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inputs:
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- Sample diff of code changes (str)
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- Repo context (dict)
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steps:
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- 'Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`'
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- 'Step 2: Invoke the graph with initial parameters including sample diff, repo context,
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user ID, and other metadata'
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- 'Step 3: The graph processes the input through a series of steps, generating messages
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and issues as it progresses'
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outputs:
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- Final result containing processed messages and issues (dict)
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tags: []
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metadata:
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source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
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extracted_at: ''
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confidence: 0.95
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---
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# code-review-agent-workflow
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Automate the code review process using a multi-step workflow with human-in-the-loop approval.
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## Setup
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**Dependencies:**
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```text
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pip install langgraph>=0.3 langchain-core>=0.3 langchain-anthropic>=0.3 langfuse>=2.0 mcp[server]>=1.24,<2.0 langchain-mcp-adapters>=0.1 tenacity>=9.0 fastapi>=0.115 uvicorn[standard]>=0.32 psycopg[binary]>=3.1 langgraph-checkpoint-postgres>=2.0 httpx>=0.27 python-dotenv>=1.0 redis>=5.0
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```
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**Setup steps:**
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1. cp .env.example .env
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1. docker compose up -d
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1. pip install -e '.[dev]'
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## Key Files
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- `agentkit/workflow/code_review/graph.py - Contains the `build_graph` function and graph invocation logic.`
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- `examples/run_code_review.py - Example script demonstrating how to run the code review agent.`
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## Steps
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1. Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`
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2. Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata
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3. Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses
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## Implementation Details
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```python
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graph = build_graph()
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thread_id = str(uuid.uuid4())
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result = graph.invoke(...)
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```
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## Inputs
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- Sample diff of code changes (str)
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- Repo context (dict)
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## Outputs
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- Final result containing processed messages and issues (dict)
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## Failure Modes
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- Specific failure scenario with mitigation: If the `build_graph()` function fails to initialize properly, ensure all required dependencies are correctly installed.
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## Source
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Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
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Confidence: 0.95
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# Commands: code-review-agent-workflow
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## Available Commands
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- `/skill code-review-agent-workflow` — Load this skill
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- `/run code-review-agent-workflow` — Execute workflow
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# Examples: code-review-agent-workflow
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## Usage Example
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```python
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# How to use this skill
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# Inputs: Sample diff of code changes (str), Repo context (dict)
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# Process: Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph` → Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata → Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses
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# Outputs: Final result containing processed messages and issues (dict)
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```
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{
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"name": "code-review-agent-workflow",
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"version": "1.0.0",
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"goal": "Automate the code review process using a multi-step workflow with human-in-the-loop approval.",
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"inputs": [
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"Sample diff of code changes (str)",
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"Repo context (dict)"
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],
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"steps": [
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"Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`",
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"Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata",
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"Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses"
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],
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"outputs": [
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"Final result containing processed messages and issues (dict)"
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],
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"failure_modes": [
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"Specific failure scenario with mitigation: If the `build_graph()` function fails to initialize properly, ensure all required dependencies are correctly installed."
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],
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"confidence": 0.95,
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"explanation": "This workflow is reusable for any code review process that requires a multi-step analysis and human approval.",
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"source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
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"score": 1.0
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}
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# Tests: code-review-agent-workflow
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## Test Checklist
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- [ ] Workflow has at least 3 steps
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- [ ] All inputs are defined
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- [ ] All outputs are defined
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- [ ] Failure modes are documented
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- [ ] Skill can be loaded without errors
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---
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name: mcp-server-setup
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version: 1.0.0
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description: Set up an MCP server to integrate PipesHub with any MCP-compatible client.
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inputs:
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- MCP server configuration details
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- PipesHub credentials
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steps:
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- 'Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`'
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- 'Step 2: Navigate to the cloned directory with `cd mcp-server`'
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- 'Step 3: Run the interactive installer by executing `./install.sh`'
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- 'Step 4: Follow the prompts in the installer to configure the server, including
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setting up graph DB, message broker, and KV store'
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- 'Step 5: The installer will generate a `.env` file with necessary environment variables.
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Ensure these are correctly set'
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- 'Step 6: Start the MCP server by running `docker-compose up -d`'
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outputs:
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- Running MCP server
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- .env file generated
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tags: []
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metadata:
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source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
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extracted_at: ''
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confidence: 0.95
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---
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# mcp-server-setup
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Set up an MCP server to integrate PipesHub with any MCP-compatible client.
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## Setup
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**Dependencies:**
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```text
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pip install docker docker-compose
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```
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**Setup steps:**
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1. Ensure Docker and Docker Compose are installed on your system.
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1. Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
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## Key Files
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- `path/to/install.sh - Script to run the interactive installer`
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- `path/to/docker-compose.yml - Configuration for Docker services`
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## Steps
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1. Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
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2. Step 2: Navigate to the cloned directory with `cd mcp-server`
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3. Step 3: Run the interactive installer by executing `./install.sh`
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4. Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store
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5. Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set
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6. Step 6: Start the MCP server by running `docker-compose up -d`
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## Implementation Details
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```python
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```bash
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./install.sh
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```
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Run this script to start the installation process.
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```
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```python
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```yaml
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docker-compose:
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version: '3.9'
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services:
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mcp-server:
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image: pipeshubai/mcp-server:latest
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environment:
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- PIPESHUB_API_KEY=your_api_key_here
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```
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This snippet shows how to configure the Docker Compose file.
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```
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## Inputs
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- MCP server configuration details
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- PipesHub credentials
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## Outputs
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- Running MCP server
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- .env file generated
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## Failure Modes
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- Installer fails to run due to missing dependencies or incorrect configuration
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- Docker Compose setup issues preventing server from starting
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## Source
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Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
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Confidence: 0.95
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# Commands: mcp-server-setup
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## Available Commands
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- `/skill mcp-server-setup` — Load this skill
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- `/run mcp-server-setup` — Execute workflow
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# Examples: mcp-server-setup
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## Usage Example
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```python
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# How to use this skill
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# Inputs: MCP server configuration details, PipesHub credentials
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# 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`
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# Outputs: Running MCP server, .env file generated
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```
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{
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"name": "mcp-server-setup",
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"version": "1.0.0",
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"goal": "Set up an MCP server to integrate PipesHub with any MCP-compatible client.",
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"inputs": [
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"MCP server configuration details",
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"PipesHub credentials"
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],
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"steps": [
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"Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`",
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"Step 2: Navigate to the cloned directory with `cd mcp-server`",
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"Step 3: Run the interactive installer by executing `./install.sh`",
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"Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store",
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"Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set",
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"Step 6: Start the MCP server by running `docker-compose up -d`"
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],
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"outputs": [
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"Running MCP server",
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".env file generated"
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],
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"failure_modes": [
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"Installer fails to run due to missing dependencies or incorrect configuration",
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"Docker Compose setup issues preventing server from starting"
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],
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"confidence": 0.95,
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"explanation": "This workflow is specific but can be adapted for different deployment environments and configurations.",
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"source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
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"score": 1.0
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}
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# Tests: mcp-server-setup
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## Test Checklist
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- [ ] Workflow has at least 3 steps
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- [ ] All inputs are defined
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- [ ] All outputs are defined
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- [ ] Failure modes are documented
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- [ ] Skill can be loaded without errors
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---
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name: multi-agent-sequential-workflow
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version: 1.0.0
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description: Gather and process information from multiple agents to generate a comprehensive
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travel guide.
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inputs:
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- User query with location
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steps:
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- 'Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel
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to gather raw facts about the destination.'
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- 'Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into
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a structured travel guide based on the user''s request and raw information provided
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by the researcher.'
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- 'Step 3: Writer agent (agent.py) formats the final response, including the structured
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guide content and prominently features the ''Suggested Web Pages'' section.'
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outputs:
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- Structured travel guide with key sections
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- Final client response
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tags: []
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metadata:
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source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
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extracted_at: ''
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confidence: 0.95
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---
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# multi-agent-sequential-workflow
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Gather and process information from multiple agents to generate a comprehensive travel guide.
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## Setup
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**Dependencies:**
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```text
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pip install python3 fastapi uvicorn strands bedrock-model
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```
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**Setup steps:**
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1. Install required dependencies using pip
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1. Set up environment variables for API keys and model IDs
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## Key Files
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- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the multi-agent workflow logic.`
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- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - FastAPI app to handle user queries.`
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## Steps
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1. Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.
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2. Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.
|
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3. Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
|
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## Implementation Details
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```python
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research_output = researcher_agent(query, stream=False)
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```
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```python
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guide_output = travel_guide_agent(planner_prompt, stream=False)
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```
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```python
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final_response = writer_agent(writer_prompt, stream=False)
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```
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## Inputs
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- User query with location
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## Outputs
|
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|
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- Structured travel guide with key sections
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- Final client response
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|
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## Failure Modes
|
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|
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- Network issues during API calls could lead to incomplete data collection or processing failures
|
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|
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## Source
|
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|
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Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
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Confidence: 0.95
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@@ -0,0 +1,6 @@
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# Commands: multi-agent-sequential-workflow
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## Available Commands
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- `/skill multi-agent-sequential-workflow` — Load this skill
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- `/run multi-agent-sequential-workflow` — Execute workflow
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# Examples: multi-agent-sequential-workflow
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|
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## Usage Example
|
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|
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```python
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# How to use this skill
|
||||
# Inputs: User query with location
|
||||
# Process: Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination. → Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher. → Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
|
||||
# Outputs: Structured travel guide with key sections, Final client response
|
||||
```
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||||
@@ -0,0 +1,24 @@
|
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{
|
||||
"name": "multi-agent-sequential-workflow",
|
||||
"version": "1.0.0",
|
||||
"goal": "Gather and process information from multiple agents to generate a comprehensive travel guide.",
|
||||
"inputs": [
|
||||
"User query with location"
|
||||
],
|
||||
"steps": [
|
||||
"Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.",
|
||||
"Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.",
|
||||
"Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section."
|
||||
],
|
||||
"outputs": [
|
||||
"Structured travel guide with key sections",
|
||||
"Final client response"
|
||||
],
|
||||
"failure_modes": [
|
||||
"Network issues during API calls could lead to incomplete data collection or processing failures"
|
||||
],
|
||||
"confidence": 0.95,
|
||||
"explanation": "This workflow is specific but can be adapted for other types of guides or information gathering tasks.",
|
||||
"source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
|
||||
"score": 1.0
|
||||
}
|
||||
@@ -0,0 +1,9 @@
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# Tests: multi-agent-sequential-workflow
|
||||
|
||||
## Test Checklist
|
||||
|
||||
- [ ] Workflow has at least 3 steps
|
||||
- [ ] All inputs are defined
|
||||
- [ ] All outputs are defined
|
||||
- [ ] Failure modes are documented
|
||||
- [ ] Skill can be loaded without errors
|
||||
@@ -0,0 +1,90 @@
|
||||
---
|
||||
name: research-pipeline
|
||||
version: 1.0.0
|
||||
description: Fetch a Wikipedia page, summarise its content using an AI agent, and
|
||||
write the summary to a file.
|
||||
inputs:
|
||||
- URL of the Wikipedia page
|
||||
steps:
|
||||
- 'Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap
|
||||
import NIM_MODEL, require_nim_api_key; import blacknode as bn`'
|
||||
- 'Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the
|
||||
API key is set.'
|
||||
- 'Step 3: Create a graph instance: Initialize `g = bn.Graph()`.'
|
||||
- 'Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node(''Literal'',
|
||||
value=''URL of the Wikipedia page''); fetcher = g.node(''HTTPGet''); summarise =
|
||||
g.node(''LLMAgent'', system=''You are a technical writer. Summarise the text in
|
||||
3 bullet points.'', model=NIM_MODEL); writer = g.node(''FileWrite'', path=''summary.txt'')`'
|
||||
- 'Step 5: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
|
||||
fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
|
||||
- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
|
||||
print(f''Summary written to: {result}'')`'
|
||||
outputs:
|
||||
- Path of the summary file
|
||||
tags: []
|
||||
metadata:
|
||||
source_repo: https://github.com/temiroff/Blacknode.git
|
||||
extracted_at: ''
|
||||
confidence: 0.95
|
||||
---
|
||||
|
||||
# research-pipeline
|
||||
|
||||
Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.
|
||||
|
||||
## Setup
|
||||
|
||||
**Dependencies:**
|
||||
|
||||
```text
|
||||
pip install anthropic>=0.25 docker>=7.1 openai>=1.0
|
||||
```
|
||||
|
||||
**Setup steps:**
|
||||
|
||||
1. Ensure NVIDIA NIM API key is set in the environment or editor
|
||||
1. Install required dependencies: `pip install -r requirements.txt`
|
||||
|
||||
## Key Files
|
||||
|
||||
- `examples/research_pipeline.py - Contains the research pipeline workflow`
|
||||
|
||||
## Steps
|
||||
|
||||
1. Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`
|
||||
2. Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.
|
||||
3. Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
|
||||
4. Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')`
|
||||
5. Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`
|
||||
6. Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`
|
||||
|
||||
## Implementation Details
|
||||
|
||||
```python
|
||||
from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn
|
||||
```
|
||||
|
||||
```python
|
||||
url = g.node('Literal', value='https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro=1&explaintext=1&titles=Houdini_(software)&format=json&formatversion=2&origin=*'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')
|
||||
```
|
||||
|
||||
```python
|
||||
url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')
|
||||
```
|
||||
|
||||
## Inputs
|
||||
|
||||
- URL of the Wikipedia page
|
||||
|
||||
## Outputs
|
||||
|
||||
- Path of the summary file
|
||||
|
||||
## Failure Modes
|
||||
|
||||
- If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty
|
||||
|
||||
## Source
|
||||
|
||||
Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
|
||||
Confidence: 0.95
|
||||
@@ -0,0 +1,6 @@
|
||||
# Commands: research-pipeline
|
||||
|
||||
## Available Commands
|
||||
|
||||
- `/skill research-pipeline` — Load this skill
|
||||
- `/run research-pipeline` — Execute workflow
|
||||
@@ -0,0 +1,10 @@
|
||||
# Examples: research-pipeline
|
||||
|
||||
## Usage Example
|
||||
|
||||
```python
|
||||
# How to use this skill
|
||||
# Inputs: URL of the Wikipedia page
|
||||
# Process: Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn` → Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set. → Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
|
||||
# Outputs: Path of the summary file
|
||||
```
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"name": "research-pipeline",
|
||||
"version": "1.0.0",
|
||||
"goal": "Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.",
|
||||
"inputs": [
|
||||
"URL of the Wikipedia page"
|
||||
],
|
||||
"steps": [
|
||||
"Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`",
|
||||
"Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.",
|
||||
"Step 3: Create a graph instance: Initialize `g = bn.Graph()`.",
|
||||
"Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')`",
|
||||
"Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`",
|
||||
"Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`"
|
||||
],
|
||||
"outputs": [
|
||||
"Path of the summary file"
|
||||
],
|
||||
"failure_modes": [
|
||||
"If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty"
|
||||
],
|
||||
"confidence": 0.95,
|
||||
"explanation": "This workflow can be adapted to fetch and summarise any Wikipedia page or similar content source.",
|
||||
"source_repo": "https://github.com/temiroff/Blacknode.git",
|
||||
"score": 1.0
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
# Tests: research-pipeline
|
||||
|
||||
## Test Checklist
|
||||
|
||||
- [ ] Workflow has at least 3 steps
|
||||
- [ ] All inputs are defined
|
||||
- [ ] All outputs are defined
|
||||
- [ ] Failure modes are documented
|
||||
- [ ] Skill can be loaded without errors
|
||||
@@ -0,0 +1,96 @@
|
||||
---
|
||||
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
|
||||
@@ -0,0 +1,6 @@
|
||||
# Commands: unifai-workflow-execution
|
||||
|
||||
## Available Commands
|
||||
|
||||
- `/skill unifai-workflow-execution` — Load this skill
|
||||
- `/run unifai-workflow-execution` — Execute workflow
|
||||
@@ -0,0 +1,10 @@
|
||||
# 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
|
||||
```
|
||||
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
# Tests: unifai-workflow-execution
|
||||
|
||||
## Test Checklist
|
||||
|
||||
- [ ] Workflow has at least 3 steps
|
||||
- [ ] All inputs are defined
|
||||
- [ ] All outputs are defined
|
||||
- [ ] Failure modes are documented
|
||||
- [ ] Skill can be loaded without errors
|
||||
Reference in New Issue
Block a user