Add Skill: autonomous-web-research-agent

Extracted from: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.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: literature-review-with-traceable-ai-evidence
+# Tests: autonomous-web-research-agent
 
 ## Test Checklist
 

skills/literature-review-with-traceable-ai-evidence/SKILL.md

@@ -1,77 +0,0 @@
----
-name: literature-review-with-traceable-ai-evidence
-version: 1.0.0
-description: Enable researchers to ingest documents, asynchronously index them, and
-  interact with multi-agent AI to answer questions with verifiable citations to original
-  text.
-inputs:
-- Documents in PDF, Office, image, or text formats
-- Research questions or topics of interest
-- 'Optional: user model configuration via .env or settings'
-steps:
-- Upload documents to a project (via desktop app or web UI)
-- System asynchronously converts Office docs to PDF if needed, runs OCR to extract
-  text with coordinates, chunks and embeds into vector store
-- User starts a main research session or creates exploration branches without waiting
-  for indexing to finish
-- Leader agent receives query and delegates subtasks to researcher, reviewer, writer
-  subagents
-- Subagents perform hybrid retrieval and rerank to find relevant chunks with source
-  coordinates
-- Agents synthesize answers and return evidence with clickable citations that highlight
-  original pages
-- User verifies conclusions by navigating to cited source locations and can save notes
-  to research memory or mind map
-outputs:
-- AI-generated answers with traceable evidence (coordinates, page highlights)
-- Research session history with branches
-- Indexed document library for future queries
-- Mind maps or structured notes
-- Persistent run events for resuming sessions
-tags: []
-metadata:
-  source_repo: https://github.com/0verL1nk/PaperSage.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# literature-review-with-traceable-ai-evidence
-
-Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.
-
-## Steps
-
-1. Upload documents to a project (via desktop app or web UI)
-2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
-3. User starts a main research session or creates exploration branches without waiting for indexing to finish
-4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
-5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
-6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
-7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map
-
-## Inputs
-
-- Documents in PDF, Office, image, or text formats
-- Research questions or topics of interest
-- Optional: user model configuration via .env or settings
-
-## Outputs
-
-- AI-generated answers with traceable evidence (coordinates, page highlights)
-- Research session history with branches
-- Indexed document library for future queries
-- Mind maps or structured notes
-- Persistent run events for resuming sessions
-
-## Failure Modes
-
-- Missing local Office/LibreOffice converter causes document conversion failure
-- First-time model download may be slow or require network
-- OCR may have low confidence on poor quality scans
-- Retrieval might miss context if chunking splits semantics
-- Multi-agent coordination could produce conflicting intermediate results
-
-## Source
-
-Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
-Confidence: 0.85

skills/literature-review-with-traceable-ai-evidence/commands.md

@@ -1,6 +0,0 @@
-# Commands: literature-review-with-traceable-ai-evidence
-
-## Available Commands
-
-- `/skill literature-review-with-traceable-ai-evidence` — Load this skill
-- `/run literature-review-with-traceable-ai-evidence` — Execute workflow

skills/literature-review-with-traceable-ai-evidence/examples.md

@@ -1,10 +0,0 @@
-# Examples: literature-review-with-traceable-ai-evidence
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Documents in PDF, Office, image, or text formats, Research questions or topics of interest, Optional: user model configuration via .env or settings
-# Process: Upload documents to a project (via desktop app or web UI) → System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store → User starts a main research session or creates exploration branches without waiting for indexing to finish
-# Outputs: AI-generated answers with traceable evidence (coordinates, page highlights), Research session history with branches, Indexed document library for future queries, Mind maps or structured notes, Persistent run events for resuming sessions
-```

skills/literature-review-with-traceable-ai-evidence/metadata.json

@@ -1,37 +0,0 @@
-{
-  "name": "literature-review-with-traceable-ai-evidence",
-  "version": "1.0.0",
-  "goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
-  "inputs": [
-    "Documents in PDF, Office, image, or text formats",
-    "Research questions or topics of interest",
-    "Optional: user model configuration via .env or settings"
-  ],
-  "steps": [
-    "Upload documents to a project (via desktop app or web UI)",
-    "System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store",
-    "User starts a main research session or creates exploration branches without waiting for indexing to finish",
-    "Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents",
-    "Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates",
-    "Agents synthesize answers and return evidence with clickable citations that highlight original pages",
-    "User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map"
-  ],
-  "outputs": [
-    "AI-generated answers with traceable evidence (coordinates, page highlights)",
-    "Research session history with branches",
-    "Indexed document library for future queries",
-    "Mind maps or structured notes",
-    "Persistent run events for resuming sessions"
-  ],
-  "failure_modes": [
-    "Missing local Office/LibreOffice converter causes document conversion failure",
-    "First-time model download may be slow or require network",
-    "OCR may have low confidence on poor quality scans",
-    "Retrieval might miss context if chunking splits semantics",
-    "Multi-agent coordination could produce conflicting intermediate results"
-  ],
-  "confidence": 0.85,
-  "explanation": "The README describes PaperSage's core workflow: asynchronous document ingestion with OCR/indexing, followed by multi-agent question answering with cited evidence. This process is not tied to the specific codebase and can be reused as a general literature review methodology for any document-centric research using RAG and agent collaboration.",
-  "source_repo": "https://github.com/0verL1nk/PaperSage.git",
-  "score": 1.0
-}