Add Skill: autonomous-web-research-agent

Extracted from: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git
Score: 1.0

skills/agent-builder-workflow/SKILL.md

@@ -1,110 +0,0 @@
----
-name: agent-builder-workflow
-version: 1.0.0
-description: Build no-code AI agents that connect to enterprise knowledge sources,
-  perform unified search and deep research, and generate explainable answers with
-  citations
-inputs:
-- Task description and agent objectives (e.g., answer Q&A, research specific topics,
-  generate reports)
-- Knowledge sources (documents, databases, enterprise systems) to connect to
-- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
-- Output requirements (citation format, response structure, code execution needs)
-steps:
-- 'Step 1: Define agent task and objectives - Specify what the agent should do (e.g.,
-  answer a question, perform deep research on a topic, generate a report with citations)'
-- 'Step 2: Configure knowledge sources - Connect to enterprise documents, databases,
-  or external systems that will serve as the agent''s context'
-- 'Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines
-  retrieval (graph/vector) and LLM response generation with citation capabilities'
-- 'Step 4: Execute agent - Run the LangGraph chain to process the task and generate
-  responses with grounded citations'
-- 'Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute
-  code, deploy it to a safe sandbox environment for verification'
-outputs:
-- Agent execution logs showing retrieval steps and LLM responses
-- Grounded answers with block citations to source documents
-- Generated reports or artifacts (if code execution was performed)
-- Structured task completion status and results
-tags: []
-metadata:
-  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# agent-builder-workflow
-
-Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi pydantic
-```
-
-**Setup steps:**
-
-1. Install dependencies: pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi
-1. Configure knowledge sources in .env (graph DB connection, vector DB, document paths)
-1. Define agent task in agent_builder.py with objectives and retrieval strategy
-1. Run agent chain: python agent_chain.py --task "research_quantum_computing"
-1. For code execution: add sandbox step to agent_chain.py with code generation and safe execution
-
-## Key Files
-
-- `pipeshub-ai/backend/agent_chain.py - LangGraph chain definition for agent workflows`
-- `pipeshub-ai/backend/retrieval_pipeline.py - Knowledge graph and vector search implementation`
-- `pipeshub-ai/workflows/agent_builder.py - No-code agent creation interface`
-- `pipeshub-ai/workflows/citation_generator.py - Block citation generation from retrieved sources`
-
-## Steps
-
-1. Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)
-2. Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context
-3. Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
-4. Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations
-5. Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification
-
-## Implementation Details
-
-```python
-LangGraph chain with retrieval (graph/vector) and LLM response stages
-```
-
-```python
-Knowledge graph construction from enterprise documents
-```
-
-```python
-Citation formatting using block references to source documents
-```
-
-## Inputs
-
-- Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)
-- Knowledge sources (documents, databases, enterprise systems) to connect to
-- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
-- Output requirements (citation format, response structure, code execution needs)
-
-## Outputs
-
-- Agent execution logs showing retrieval steps and LLM responses
-- Grounded answers with block citations to source documents
-- Generated reports or artifacts (if code execution was performed)
-- Structured task completion status and results
-
-## Failure Modes
-
-- Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality
-- Permission errors when accessing enterprise knowledge sources
-- LLM context window overflow when generating long explanations with citations
-- Sandbox execution failures for code generation or execution tasks
-- Timeout errors during multi-step agent chain execution
-
-## Source
-
-Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
-Confidence: 0.95

skills/agent-builder-workflow/commands.md

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

skills/agent-builder-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: agent-builder-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports), Knowledge sources (documents, databases, enterprise systems) to connect to, Retrieval strategy configuration (graph-based knowledge graph vs. vector search), Output requirements (citation format, response structure, code execution needs)
-# Process: Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations) → Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context → Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
-# Outputs: Agent execution logs showing retrieval steps and LLM responses, Grounded answers with block citations to source documents, Generated reports or artifacts (if code execution was performed), Structured task completion status and results
-```

skills/agent-builder-workflow/metadata.json

@@ -1,35 +0,0 @@
-{
-  "name": "agent-builder-workflow",
-  "version": "1.0.0",
-  "goal": "Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations",
-  "inputs": [
-    "Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)",
-    "Knowledge sources (documents, databases, enterprise systems) to connect to",
-    "Retrieval strategy configuration (graph-based knowledge graph vs. vector search)",
-    "Output requirements (citation format, response structure, code execution needs)"
-  ],
-  "steps": [
-    "Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)",
-    "Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context",
-    "Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities",
-    "Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations",
-    "Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification"
-  ],
-  "outputs": [
-    "Agent execution logs showing retrieval steps and LLM responses",
-    "Grounded answers with block citations to source documents",
-    "Generated reports or artifacts (if code execution was performed)",
-    "Structured task completion status and results"
-  ],
-  "failure_modes": [
-    "Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality",
-    "Permission errors when accessing enterprise knowledge sources",
-    "LLM context window overflow when generating long explanations with citations",
-    "Sandbox execution failures for code generation or execution tasks",
-    "Timeout errors during multi-step agent chain execution"
-  ],
-  "confidence": 0.95,
-  "explanation": "PipesHub provides a reusable agent builder workflow that combines LangGraph orchestration with graph-based and vector-based retrieval. This pattern can be adapted to any enterprise context where AI agents need to search across multiple knowledge sources, generate explainable answers with citations, and optionally execute code in a safe sandbox. The workflow is defined by specific configuration files (LangGraph chain definitions) and follows a standard pattern: task definition \u2192 knowledge source connection \u2192 retrieval strategy \u2192 response generation \u2192 optional code sandbox.",
-  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
-  "score": 1.0
-}

skills/autonomous-web-research-agent/SKILL.md

@@ -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

skills/autonomous-web-research-agent/commands.md

@@ -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

skills/autonomous-web-research-agent/examples.md

@@ -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
+```

skills/autonomous-web-research-agent/metadata.json

@@ -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
+}

skills/autonomous-web-research-agent/tests.md

@@ -1,4 +1,4 @@
-# Tests: agent-builder-workflow
+# Tests: autonomous-web-research-agent
 
 ## Test Checklist