Add Skill: autonomous-web-research-agent

Extracted from: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git Score: 1.0
Add 5 skills from LFM + 12 skills total
2026-08-10 17:05:01 +00:00 · 2026-08-05 17:05:21 +00:00
26 changed files with 763 additions and 3 deletions
@@ -16,10 +16,10 @@ llm:
  api_key: ""
  max_tokens: 8000

-# Secondary LLM for pipeline tasks — uses Ollama on 3060 (non-reasoning model)
+# Secondary LLM for pipeline tasks — uses LFM on 3060 (llama.cpp)
 llm_pipeline:
-  base_url: http://100.64.0.4:11434
-  model: qwen2.5:7b
+  base_url: http://100.64.0.4:8080
+  model: C:\models\LFM2.5-2.6B-Q4_K_M.gguf
  api_key: ""
  max_tokens: 6000

@@ -0,0 +1,77 @@
+---
+name: autonomous-web-research-agent
+version: 1.0.0
+description: Autonomously research a given query on the web using multiple search
+  tools and generate a structured report with summary, detailed sections, source tracking,
+  and bias analysis.
+inputs:
+- 'query (string): the research question or topic to investigate'
+- 'tools (list, optional): selected web search/tools to use (e.g., Tavily, Google,
+  NewsAPI, DuckDuckGo)'
+- 'api_keys (dict, optional): credentials for LLM and external search APIs'
+- 'model_config (dict, optional): LLM provider and parameters'
+steps:
+- 1. Accept user query and optional tool selections.
+- '2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search
+  (Tavily, Google, DuckDuckGo), news API, web scraping.'
+- 3. Decompose query into sub-questions if needed and iteratively call tools to gather
+  relevant information.
+- 4. Extract and deduplicate content from retrieved sources, tracking source metadata
+  (URL, tool used).
+- 5. Use a large language model to synthesize findings into an executive summary and
+  detailed sections.
+- 6. Analyze potential biases or limitations of gathered sources.
+- 7. Compile a structured report object (ResearchReport) containing query, summary,
+  sections, sources, biases.
+- 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
+outputs:
+- 'ResearchReport (JSON/dict) with fields: query (string), summary (string), sections
+  (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases
+  (string)'
+- Optional UI rendering of report with source badges and expandable sections
+tags: []
+metadata:
+  source_repo: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git
+  extracted_at: ''
+  confidence: 0.85
+---
+
+# autonomous-web-research-agent
+
+Autonomously research a given query on the web using multiple search tools and generate a structured report with summary, detailed sections, source tracking, and bias analysis.
+
+## Steps
+
+1. 1. Accept user query and optional tool selections.
+2. 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.
+3. 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
+4. 4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).
+5. 5. Use a large language model to synthesize findings into an executive summary and detailed sections.
+6. 6. Analyze potential biases or limitations of gathered sources.
+7. 7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.
+8. 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
+
+## Inputs
+
+- query (string): the research question or topic to investigate
+- tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)
+- api_keys (dict, optional): credentials for LLM and external search APIs
+- model_config (dict, optional): LLM provider and parameters
+
+## Outputs
+
+- ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)
+- Optional UI rendering of report with source badges and expandable sections
+
+## Failure Modes
+
+- Missing or invalid API keys causing tool authentication failures
+- Rate limits or network errors from search APIs
+- Insufficient or low-quality search results leading to incomplete report
+- LLM hallucination or mis-summarization despite source tracking
+- Parsing errors in HTML/scraped content
+
+## Source
+
+Extracted from: [https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git](https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git)
+Confidence: 0.85
@@ -0,0 +1,6 @@
+# Commands: autonomous-web-research-agent
+
+## Available Commands
+
+- `/skill autonomous-web-research-agent` — Load this skill
+- `/run autonomous-web-research-agent` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: autonomous-web-research-agent
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: query (string): the research question or topic to investigate, tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo), api_keys (dict, optional): credentials for LLM and external search APIs, model_config (dict, optional): LLM provider and parameters
+# Process: 1. Accept user query and optional tool selections. → 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping. → 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
+# Outputs: ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string), Optional UI rendering of report with source badges and expandable sections
+```
@@ -0,0 +1,36 @@
+{
+  "name": "autonomous-web-research-agent",
+  "version": "1.0.0",
+  "goal": "Autonomously research a given query on the web using multiple search tools and generate a structured report with summary, detailed sections, source tracking, and bias analysis.",
+  "inputs": [
+    "query (string): the research question or topic to investigate",
+    "tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)",
+    "api_keys (dict, optional): credentials for LLM and external search APIs",
+    "model_config (dict, optional): LLM provider and parameters"
+  ],
+  "steps": [
+    "1. Accept user query and optional tool selections.",
+    "2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.",
+    "3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.",
+    "4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).",
+    "5. Use a large language model to synthesize findings into an executive summary and detailed sections.",
+    "6. Analyze potential biases or limitations of gathered sources.",
+    "7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.",
+    "8. Optionally present report via a UI (e.g., Streamlit) or return as JSON."
+  ],
+  "outputs": [
+    "ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)",
+    "Optional UI rendering of report with source badges and expandable sections"
+  ],
+  "failure_modes": [
+    "Missing or invalid API keys causing tool authentication failures",
+    "Rate limits or network errors from search APIs",
+    "Insufficient or low-quality search results leading to incomplete report",
+    "LLM hallucination or mis-summarization despite source tracking",
+    "Parsing errors in HTML/scraped content"
+  ],
+  "confidence": 0.85,
+  "explanation": "The repository implements a generic autonomous web research agent that can be reused for any topical query. The workflow of querying, multi-tool retrieval, synthesis, and structured reporting is not domain-specific and can be extracted as a reusable skill.",
+  "source_repo": "https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git",
+  "score": 1.0
+}
@@ -0,0 +1,9 @@
+# Tests: autonomous-web-research-agent
+
+## 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: blacknode-graph-workflow
+version: 1.0.0
+description: Build and execute node-based AI workflows with LLM agents and processing
+  nodes
+inputs:
+- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
+- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite,
+  etc.)
+- Data sources (URLs, text content, or other inputs for the workflow)
+steps:
+- Initialize a blacknode.Graph instance to create the workflow structure
+- Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent,
+  FileWrite)
+- Define edges connecting nodes to establish data flow between them
+- Execute the graph using cook() to run the workflow and generate outputs
+outputs:
+- Processed results from the final node (e.g., printed text, written files, or generated
+  data)
+- Graph execution status and any errors encountered during execution
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# blacknode-graph-workflow
+
+Build and execute node-based AI workflows with LLM agents and processing nodes
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install blacknode (core package) anthropic>=0.25 openai>=1.0 petgraph (for graph operations)
+```
+
+**Setup steps:**
+
+1. Install blacknode package: pip install blacknode
+1. Configure model API keys (NIM_API_KEY, OPENAI_API_KEY, etc.) in .env or editor
+1. Create a Graph instance and add nodes with inputs/outputs
+1. Define node connections in g._edges list
+1. Execute with g.cook() to run the workflow and capture results
+
+## Key Files
+
+- `blacknode/blacknode.py (Graph class implementation)`
+- `examples/hello_agent.py (simple LLM agent workflow)`
+- `examples/converted_nvidia_nim.py (NIM model workflow)`
+
+## Steps
+
+1. Initialize a blacknode.Graph instance to create the workflow structure
+2. Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)
+3. Define edges connecting nodes to establish data flow between them
+4. Execute the graph using cook() to run the workflow and generate outputs
+
+## Implementation Details
+
+```python
+g = bn.Graph()
+```
+
+```python
+g._edges = [{'from': 'model', 'from_port': 'value', 'to': 'agent', 'to_port': 'model'}]
+```
+
+```python
+result = g.cook(output_node, 'value')
+```
+
+## Inputs
+
+- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
+- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)
+- Data sources (URLs, text content, or other inputs for the workflow)
+
+## Outputs
+
+- Processed results from the final node (e.g., printed text, written files, or generated data)
+- Graph execution status and any errors encountered during execution
+
+## Failure Modes
+
+- Missing or invalid model API key causing graph initialization failure
+- Incorrect node connections or missing edge definitions leading to runtime errors
+- Model not found or unavailable in the specified environment causing execution failure
+- Graph edges not properly defined or mismatched causing cook() to fail
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95
@@ -0,0 +1,6 @@
+# Commands: blacknode-graph-workflow
+
+## Available Commands
+
+- `/skill blacknode-graph-workflow` — Load this skill
+- `/run blacknode-graph-workflow` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: blacknode-graph-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic), Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.), Data sources (URLs, text content, or other inputs for the workflow)
+# Process: Initialize a blacknode.Graph instance to create the workflow structure → Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite) → Define edges connecting nodes to establish data flow between them
+# Outputs: Processed results from the final node (e.g., printed text, written files, or generated data), Graph execution status and any errors encountered during execution
+```
@@ -0,0 +1,30 @@
+{
+  "name": "blacknode-graph-workflow",
+  "version": "1.0.0",
+  "goal": "Build and execute node-based AI workflows with LLM agents and processing nodes",
+  "inputs": [
+    "Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)",
+    "Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)",
+    "Data sources (URLs, text content, or other inputs for the workflow)"
+  ],
+  "steps": [
+    "Initialize a blacknode.Graph instance to create the workflow structure",
+    "Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)",
+    "Define edges connecting nodes to establish data flow between them",
+    "Execute the graph using cook() to run the workflow and generate outputs"
+  ],
+  "outputs": [
+    "Processed results from the final node (e.g., printed text, written files, or generated data)",
+    "Graph execution status and any errors encountered during execution"
+  ],
+  "failure_modes": [
+    "Missing or invalid model API key causing graph initialization failure",
+    "Incorrect node connections or missing edge definitions leading to runtime errors",
+    "Model not found or unavailable in the specified environment causing execution failure",
+    "Graph edges not properly defined or mismatched causing cook() to fail"
+  ],
+  "confidence": 0.95,
+  "explanation": "Blacknode provides a standardized Graph-based workflow pattern where users create node graphs using the blacknode.Graph class. This pattern is reusable across projects as it follows a consistent structure: initialize a graph, add nodes with defined inputs/outputs, connect them with edges, and execute with cook(). The examples demonstrate this pattern with LLM agents and text processing pipelines, making it adaptable to various robotics and AI workflows.",
+  "source_repo": "https://github.com/temiroff/Blacknode.git",
+  "score": 1.0
+}
@@ -0,0 +1,9 @@
+# Tests: blacknode-graph-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 +1,96 @@
+---
+name: langgraph-multi-agent-router
+version: 1.0.0
+description: Orchestrate a multi-agent workflow where specialized agents collaborate
+  sequentially to gather information, structure it, and generate a final response
+inputs:
+- User query string (e.g., destination location)
+- BedrockModel configuration (model_id, temperature, top_p)
+- Pre-configured agents with specific system prompts and tool sets
+steps:
+- Researcher agent executes with system prompt to gather raw destination facts (places,
+  history, accommodations, food, web pages) using BedrockModel and available tools
+  (calculator, current_time)
+- Travel guide agent receives raw research output and structures it into labeled sections
+  (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights,
+  Suggested Web Pages)
+- Writer agent receives the structured guide and synthesizes it into a professional
+  client-facing response with clear formatting and emphasis on the suggested web pages
+outputs:
+- Raw research data (JSON string containing gathered facts)
+- Structured guide content (markdown-formatted travel guide with labeled sections)
+- Final client response (professional formatted response ready for delivery)
+tags: []
+metadata:
+  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-multi-agent-router
+
+Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langchain langgraph bedrock-model pydantic
+```
+
+**Setup steps:**
+
+1. Install langchain and langgraph packages
+1. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
+1. Create three Agent instances with specific system prompts and tool sets
+1. Initialize LangGraph with the agent chain and run the workflow
+
+## Key Files
+
+- `agents/langchain_langgraph/00-basic-agent/agent.py`
+- `agents/langchain_langgraph/02-agent-with-tools-structured-output/agent.py`
+- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
+
+## Steps
+
+1. Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)
+2. Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)
+3. Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
+
+## Implementation Details
+
+```python
+Researcher agent uses BedrockModel with temperature=0.7, top_p=0.9 to gather destination facts
+```
+
+```python
+Travel guide agent receives raw output and formats into 5 labeled sections
+```
+
+```python
+Writer agent takes structured guide and writes professional client response
+```
+
+## Inputs
+
+- User query string (e.g., destination location)
+- BedrockModel configuration (model_id, temperature, top_p)
+- Pre-configured agents with specific system prompts and tool sets
+
+## Outputs
+
+- Raw research data (JSON string containing gathered facts)
+- Structured guide content (markdown-formatted travel guide with labeled sections)
+- Final client response (professional formatted response ready for delivery)
+
+## Failure Modes
+
+- Researcher agent fails to gather sufficient data or returns incomplete results
+- Travel guide agent fails to structure information correctly or produces unreadable output
+- Writer agent fails to format the final response properly or loses key information from the guide
+
+## Source
+
+Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
+Confidence: 0.95
@@ -0,0 +1,6 @@
+# Commands: langgraph-multi-agent-router
+
+## Available Commands
+
+- `/skill langgraph-multi-agent-router` — Load this skill
+- `/run langgraph-multi-agent-router` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: langgraph-multi-agent-router
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: User query string (e.g., destination location), BedrockModel configuration (model_id, temperature, top_p), Pre-configured agents with specific system prompts and tool sets
+# Process: Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time) → Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages) → Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
+# Outputs: Raw research data (JSON string containing gathered facts), Structured guide content (markdown-formatted travel guide with labeled sections), Final client response (professional formatted response ready for delivery)
+```
@@ -0,0 +1,29 @@
+{
+  "name": "langgraph-multi-agent-router",
+  "version": "1.0.0",
+  "goal": "Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response",
+  "inputs": [
+    "User query string (e.g., destination location)",
+    "BedrockModel configuration (model_id, temperature, top_p)",
+    "Pre-configured agents with specific system prompts and tool sets"
+  ],
+  "steps": [
+    "Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)",
+    "Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)",
+    "Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages"
+  ],
+  "outputs": [
+    "Raw research data (JSON string containing gathered facts)",
+    "Structured guide content (markdown-formatted travel guide with labeled sections)",
+    "Final client response (professional formatted response ready for delivery)"
+  ],
+  "failure_modes": [
+    "Researcher agent fails to gather sufficient data or returns incomplete results",
+    "Travel guide agent fails to structure information correctly or produces unreadable output",
+    "Writer agent fails to format the final response properly or loses key information from the guide"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow demonstrates a reusable multi-stage agent pattern where specialized agents collaborate in sequence. The Researcher agent gathers raw information using a domain-specific model, the Travel Guide agent structures that information into a consistent format, and the Writer agent synthesizes the final output. This pattern can be adapted to other domains (e.g., code generation, data analysis, research workflows) by swapping the agent types and system prompts while maintaining the same three-step structure.",
+  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
+  "score": 1.0
+}
@@ -0,0 +1,9 @@
+# Tests: langgraph-multi-agent-router
+
+## 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 +1,113 @@
+---
+name: multi-agent-workflow-execution
+version: 1.0.0
+description: Execute multi-agent AI workflows defined in YAML blueprints by creating
+  sessions, submitting user prompts, and polling for completion until final answers
+  are returned.
+inputs:
+- Blueprint ID or name (to identify the workflow to execute)
+- User shortcut (authentication identifier for the user)
+- User question or prompt (input to the workflow)
+- Base URL of the UnifAI API (endpoint for session management)
+- Polling interval (seconds between status checks during execution)
+steps:
+- Resolve the blueprint ID from either direct ID or name lookup via the API, handling
+  cases where the blueprint is not found or not unique
+- Create a new session from the resolved blueprint using the session creation endpoint
+- Submit the session with the user's prompt to start the multi-agent workflow execution
+- Poll the session status at regular intervals until the session completes, fails,
+  or is cancelled
+- Retrieve and return the final answer from the completed workflow
+outputs:
+- Final workflow result or answer (text or structured data)
+- Session status (completed, failed, or cancelled)
+- Error details if the workflow execution fails or times out
+tags: []
+metadata:
+  source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# multi-agent-workflow-execution
+
+Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install requests urllib3 python-langgraph temporalio
+```
+
+**Setup steps:**
+
+1. Install Python 3.11+ and required packages (requests, langgraph, temporalio)
+1. Configure API base URL and user credentials in environment variables or config
+1. Define or select a blueprint from the available workflows in the system
+1. Run the execution_workflow.py script with blueprint ID/name and user prompt
+
+## Key Files
+
+- `scripts/execution_workflow.py - Main workflow execution script`
+- `multi-agent/lib/mas/engine/ - LangGraph-based orchestration modules`
+- `multi-agent/lib/mas/elements/ - Node definitions (custom_agent_node, merger_node, etc.)`
+- `multi-agent/lib/mas/blueprints/ - Blueprint resolution and validation logic`
+
+## Steps
+
+1. Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique
+2. Create a new session from the resolved blueprint using the session creation endpoint
+3. Submit the session with the user's prompt to start the multi-agent workflow execution
+4. Poll the session status at regular intervals until the session completes, fails, or is cancelled
+5. Retrieve and return the final answer from the completed workflow
+
+## Implementation Details
+
+```python
+resolve_blueprint_id() - Resolves blueprint by ID or name lookup with error handling
+```
+
+```python
+create_session() - Creates a new session from a blueprint via POST /user.session.create
+```
+
+```python
+submit_session() - Submits user prompt to start workflow via POST /user.session.submit
+```
+
+```python
+poll_session_status() - Polls session.stream.status at configurable intervals
+```
+
+```python
+get_final_answer() - Retrieves final output via GET /session.chat.get
+```
+
+## Inputs
+
+- Blueprint ID or name (to identify the workflow to execute)
+- User shortcut (authentication identifier for the user)
+- User question or prompt (input to the workflow)
+- Base URL of the UnifAI API (endpoint for session management)
+- Polling interval (seconds between status checks during execution)
+
+## Outputs
+
+- Final workflow result or answer (text or structured data)
+- Session status (completed, failed, or cancelled)
+- Error details if the workflow execution fails or times out
+
+## Failure Modes
+
+- Blueprint not found or not unique - script exits with an error listing available blueprints
+- Session creation fails - may be due to invalid blueprint ID, authentication issues, or rate limiting
+- Session submission fails - could be due to network issues, invalid parameters, or API rate limits
+- Polling loop times out - session may be stuck in a long-running state without progress
+- Final answer retrieval fails - could be due to session cleanup or network issues after completion
+
+## 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: multi-agent-workflow-execution
+
+## Available Commands
+
+- `/skill multi-agent-workflow-execution` — Load this skill
+- `/run multi-agent-workflow-execution` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: multi-agent-workflow-execution
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: Blueprint ID or name (to identify the workflow to execute), User shortcut (authentication identifier for the user), User question or prompt (input to the workflow), Base URL of the UnifAI API (endpoint for session management), Polling interval (seconds between status checks during execution)
+# Process: Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique → Create a new session from the resolved blueprint using the session creation endpoint → Submit the session with the user's prompt to start the multi-agent workflow execution
+# Outputs: Final workflow result or answer (text or structured data), Session status (completed, failed, or cancelled), Error details if the workflow execution fails or times out
+```
@@ -0,0 +1,35 @@
+{
+  "name": "multi-agent-workflow-execution",
+  "version": "1.0.0",
+  "goal": "Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.",
+  "inputs": [
+    "Blueprint ID or name (to identify the workflow to execute)",
+    "User shortcut (authentication identifier for the user)",
+    "User question or prompt (input to the workflow)",
+    "Base URL of the UnifAI API (endpoint for session management)",
+    "Polling interval (seconds between status checks during execution)"
+  ],
+  "steps": [
+    "Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique",
+    "Create a new session from the resolved blueprint using the session creation endpoint",
+    "Submit the session with the user's prompt to start the multi-agent workflow execution",
+    "Poll the session status at regular intervals until the session completes, fails, or is cancelled",
+    "Retrieve and return the final answer from the completed workflow"
+  ],
+  "outputs": [
+    "Final workflow result or answer (text or structured data)",
+    "Session status (completed, failed, or cancelled)",
+    "Error details if the workflow execution fails or times out"
+  ],
+  "failure_modes": [
+    "Blueprint not found or not unique - script exits with an error listing available blueprints",
+    "Session creation fails - may be due to invalid blueprint ID, authentication issues, or rate limiting",
+    "Session submission fails - could be due to network issues, invalid parameters, or API rate limits",
+    "Polling loop times out - session may be stuck in a long-running state without progress",
+    "Final answer retrieval fails - could be due to session cleanup or network issues after completion"
+  ],
+  "confidence": 0.95,
+  "explanation": "The UnifAI repository contains a concrete, reusable workflow pattern for executing multi-agent AI workflows. The scripts/execution_workflow.py script demonstrates a complete pipeline: resolving blueprints by ID or name, creating sessions from blueprints, submitting user prompts to start workflows, polling session status until completion, and retrieving final answers. This pattern can be adapted to any multi-agent workflow defined in the YAML blueprint system, making it reusable across different use cases and teams.",
+  "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
+  "score": 1.0
+}
@@ -0,0 +1,9 @@
+# Tests: multi-agent-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
@@ -0,0 +1,94 @@
+---
+name: three-tier-evaluation-pipeline
+version: 1.0.0
+description: Run tasks through three evaluation tiers (Run, Trace, Thread) to produce
+  comprehensive reports with human-in-the-loop validation
+inputs:
+- query/input text for the task
+- search results (for trace tier evaluation)
+- evaluation criteria and thresholds
+steps:
+- 'Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph
+  engine) to generate initial outputs and results'
+- 'Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined
+  criteria, generating detailed analysis and scoring'
+- 'Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion,
+  approval, and iterative refinement of the output'
+outputs:
+- Final consolidated report combining results from all three tiers
+- Detailed scores and metrics per tier
+- Threaded discussion logs for human review and approval
+tags: []
+metadata:
+  source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# three-tier-evaluation-pipeline
+
+Run tasks through three evaluation tiers (Run, Trace, Thread) to produce comprehensive reports with human-in-the-loop validation
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=0.3 langchain-core>=0.3 langchain-anthropic>=0.3 langfuse>=2.0 mcp[server]>=1.24 tenacity>=9.0 fastapi>=0.115 psycopg[binary]>=3.1
+```
+
+**Setup steps:**
+
+1. Install dependencies with pip install -e .[dev]
+1. Start infrastructure: docker compose up -d (PostgreSQL, Langfuse, MCP server)
+1. Configure environment variables (DATABASE_URL, MCP_API_KEY, etc.)
+1. Run the pipeline: python -m eval.runner --tiers run,thread,trace
+
+## Key Files
+
+- `eval/ - contains the three-tier evaluation logic`
+- `scripts/ci_gate.py - threshold update and benchmark validation`
+- `agentkit/runtime/ - LangGraph engine for state management and graph execution`
+
+## Steps
+
+1. Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph engine) to generate initial outputs and results
+2. Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined criteria, generating detailed analysis and scoring
+3. Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion, approval, and iterative refinement of the output
+
+## Implementation Details
+
+```python
+The eval/ directory implements Run, Trace, and Thread stages with configurable tiers
+```
+
+```python
+Benchmark suite (40 test cases) validates the pipeline's reliability
+```
+
+```python
+CI/CD workflows (ci.yml, eval-fast.yml, eval-trace.yml) orchestrate the evaluation pipeline
+```
+
+## Inputs
+
+- query/input text for the task
+- search results (for trace tier evaluation)
+- evaluation criteria and thresholds
+
+## Outputs
+
+- Final consolidated report combining results from all three tiers
+- Detailed scores and metrics per tier
+- Threaded discussion logs for human review and approval
+
+## Failure Modes
+
+- If the Run tier fails (e.g., code execution error), the pipeline can retry but may produce incomplete outputs
+- If the Trace tier LLM-as-Judge produces low-quality evaluations, the Thread tier may need additional human intervention
+- Threshold mismatches between tiers could cause the pipeline to exit early or require manual adjustment
+
+## Source
+
+Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
+Confidence: 0.95
@@ -0,0 +1,6 @@
+# Commands: three-tier-evaluation-pipeline
+
+## Available Commands
+
+- `/skill three-tier-evaluation-pipeline` — Load this skill
+- `/run three-tier-evaluation-pipeline` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: three-tier-evaluation-pipeline
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: query/input text for the task, search results (for trace tier evaluation), evaluation criteria and thresholds
+# Process: Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph engine) to generate initial outputs and results → Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined criteria, generating detailed analysis and scoring → Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion, approval, and iterative refinement of the output
+# Outputs: Final consolidated report combining results from all three tiers, Detailed scores and metrics per tier, Threaded discussion logs for human review and approval
+```
@@ -0,0 +1,29 @@
+{
+  "name": "three-tier-evaluation-pipeline",
+  "version": "1.0.0",
+  "goal": "Run tasks through three evaluation tiers (Run, Trace, Thread) to produce comprehensive reports with human-in-the-loop validation",
+  "inputs": [
+    "query/input text for the task",
+    "search results (for trace tier evaluation)",
+    "evaluation criteria and thresholds"
+  ],
+  "steps": [
+    "Step 1: Execute the main task using the Run tier of the evaluation pipeline (agentkit/runtime/LangGraph engine) to generate initial outputs and results",
+    "Step 2: Run the Trace tier where an LLM-as-Judge evaluates the output against defined criteria, generating detailed analysis and scoring",
+    "Step 3: Execute the Thread tier which facilitates human-in-the-loop discussion, approval, and iterative refinement of the output"
+  ],
+  "outputs": [
+    "Final consolidated report combining results from all three tiers",
+    "Detailed scores and metrics per tier",
+    "Threaded discussion logs for human review and approval"
+  ],
+  "failure_modes": [
+    "If the Run tier fails (e.g., code execution error), the pipeline can retry but may produce incomplete outputs",
+    "If the Trace tier LLM-as-Judge produces low-quality evaluations, the Thread tier may need additional human intervention",
+    "Threshold mismatches between tiers could cause the pipeline to exit early or require manual adjustment"
+  ],
+  "confidence": 0.95,
+  "explanation": "The AgentKit repository contains a production-ready three-tier evaluation pipeline (Run \u2192 Trace \u2192 Thread) that can be adapted to any task requiring multi-stage validation. This workflow uses LangGraph for orchestration and LangChain for tool integration, making it portable across different agent engineering scenarios. The pattern is reusable because it separates concerns into distinct stages with clear inputs/outputs, allowing teams to plug in different evaluation criteria or human reviewers as needed.",
+  "source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
+  "score": 1.0
+}
@@ -0,0 +1,9 @@
+# Tests: three-tier-evaluation-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