Add Skill: autonomous-web-research-agent

Extracted from: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git Score: 1.0
2026-08-10 17:05:01 +00:00
9 changed files with 130 additions and 149 deletions
@@ -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
+}
@@ -1,4 +1,4 @@
-# Tests: branching-agent-pattern
+# Tests: autonomous-web-research-agent

 ## Test Checklist

@@ -1,101 +0,0 @@
---
-name: branching-agent-pattern
-version: 1.0.0
-description: Define and execute AI agent workflows using CSV-based declarative definitions
-  with configurable branching logic
-inputs:
- 'CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type,
-  next_node, on_failure, prompt, input_fields, output_field'
- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
- Storage backend configuration in agentmap_config_storage.yaml
-steps:
- Define workflow graph in CSV with nodes representing agent steps and their connections
-  (next_node, on_failure)
- Configure BranchingAgent with customizable success/failure values and fallback fields
-  in the context dictionary
- Initialize the agent runtime with ensure_initialized() and configure execution tracking
-  and state adapter services
- Execute the workflow using agentmap run with appropriate inputs and monitor the
-  execution trace
-outputs:
- Executed workflow with results stored in the specified output_field
- Detailed execution trace showing success/failure decisions at each branching point
- Updated workflow state persisted in the configured storage backend
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.95
---
-
-# branching-agent-pattern
-
-Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml>=6.0.0 fastapi>=0.111.0 uvicorn>=0.34.3
-```
-
-**Setup steps:**
-
-1. Install AgentMap: pip install agentmap[all]
-1. Configure llm providers in agentmap_config.yaml (OpenAI, Anthropic, Google models)
-1. Create CSV workflow files with graph definitions
-1. Initialize runtime with ensure_initialized()
-1. Run workflow with agentmap run <csv_file> --pretty
-
-## Key Files
-
- `agentmap_config.yaml - Main configuration with LLM and storage settings`
- `agentmap_config_storage.yaml - Storage backend configuration`
- `hello_world.csv - Sample workflow demonstrating basic agent chain`
- `examples/host_integration/custom_agents.py - Custom agent implementations with host service integration`
-
-## Steps
-
-1. Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)
-2. Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary
-3. Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
-4. Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace
-
-## Implementation Details
-
-```python
-CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field
-```
-
-```python
-BranchingAgent context example: {'input_fields': ['success'], 'output_field': 'result', 'success_values': ['PASSED', 'COMPLETED']}
-```
-
-```python
-Execution command: agentmap run hello_world.csv --pretty
-```
-
-## Inputs
-
- CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field
- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
- Storage backend configuration in agentmap_config_storage.yaml
-
-## Outputs
-
- Executed workflow with results stored in the specified output_field
- Detailed execution trace showing success/failure decisions at each branching point
- Updated workflow state persisted in the configured storage backend
-
-## Failure Modes
-
- Invalid CSV format causing parsing errors during workflow loading
- Missing or misconfigured LLM provider settings leading to execution failures
- Storage backend unavailable or misconfigured preventing workflow persistence
- Agent execution timeout due to long-running operations or infinite loops
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.95
@@ -1,6 +0,0 @@
-# Commands: branching-agent-pattern
-
-## Available Commands
-
- `/skill branching-agent-pattern` — Load this skill
- `/run branching-agent-pattern` — Execute workflow
@@ -1,10 +0,0 @@
-# Examples: branching-agent-pattern
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field, LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml, Storage backend configuration in agentmap_config_storage.yaml
-# Process: Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure) → Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary → Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
-# Outputs: Executed workflow with results stored in the specified output_field, Detailed execution trace showing success/failure decisions at each branching point, Updated workflow state persisted in the configured storage backend
-```
@@ -1,31 +0,0 @@
-{
-  "name": "branching-agent-pattern",
-  "version": "1.0.0",
-  "goal": "Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic",
-  "inputs": [
-    "CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field",
-    "LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml",
-    "Storage backend configuration in agentmap_config_storage.yaml"
-  ],
-  "steps": [
-    "Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)",
-    "Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary",
-    "Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services",
-    "Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace"
-  ],
-  "outputs": [
-    "Executed workflow with results stored in the specified output_field",
-    "Detailed execution trace showing success/failure decisions at each branching point",
-    "Updated workflow state persisted in the configured storage backend"
-  ],
-  "failure_modes": [
-    "Invalid CSV format causing parsing errors during workflow loading",
-    "Missing or misconfigured LLM provider settings leading to execution failures",
-    "Storage backend unavailable or misconfigured preventing workflow persistence",
-    "Agent execution timeout due to long-running operations or infinite loops"
-  ],
-  "confidence": 0.95,
-  "explanation": "The BranchingAgent pattern provides a reusable framework for creating conditional AI workflows. The CSV-based workflow definition allows defining complex agent graphs declaratively, while the BranchingAgent handles dynamic branching based on success/failure conditions with customizable value sets. This pattern can be adapted to various use cases including task routing, error handling, and conditional execution paths across different domains.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}