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: conditional-request-routing-workflow
+# Tests: autonomous-web-research-agent
 
 ## Test Checklist
 

skills/conditional-request-routing-workflow/SKILL.md

@@ -1,81 +0,0 @@
----
-name: conditional-request-routing-workflow
-version: 1.0.0
-description: Collect a request, classify it via branching logic, and route to an approval
-  or rejection handler to produce a final result
-inputs:
-- name: request
-  type: string
-  description: The input text or request to be evaluated and routed
-steps:
-- node: Start
-  agent_type: input
-  description: Prompt user and capture the request into state field 'request'
-  next: Classify
-- node: Classify
-  agent_type: branching
-  description: Evaluate the request and set 'decision' field, routing to Approve on
-    success or Reject on failure
-  input_fields:
-  - request
-  output_field: decision
-  next_node: Approve
-  on_failure: Reject
-- node: Approve
-  agent_type: default
-  description: Format and output an approval message containing the request
-  input_fields:
-  - request
-  output_field: result
-  prompt: 'Request approved: {request}'
-- node: Reject
-  agent_type: default
-  description: Format and output a rejection message containing the request
-  input_fields:
-  - request
-  output_field: result
-  prompt: 'Request rejected: {request}'
-outputs:
-- name: result
-  type: string
-  description: Final formatted message indicating the outcome (approved or rejected)
-- name: decision
-  type: string
-  description: Routing decision produced by the branching node
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.92
----
-
-# conditional-request-routing-workflow
-
-Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result
-
-## Steps
-
-1. {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'}
-2. {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'}
-3. {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-4. {'node': 'Reject', 'agent_type': 'default', 'description': 'Format and output a rejection message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request rejected: {request}'}
-
-## Inputs
-
-- {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
-
-## Outputs
-
-- {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}
-- {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
-
-## Failure Modes
-
-- Empty or missing request input prevents meaningful classification
-- Branching node fails to resolve a valid route and defaults to rejection path
-- Prompt template variable missing causes malformed output
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.92

skills/conditional-request-routing-workflow/commands.md

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

skills/conditional-request-routing-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-request-routing-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
-# Process: {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'} → {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'} → {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-# Outputs: {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}, {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
-```

skills/conditional-request-routing-workflow/metadata.json

@@ -1,72 +0,0 @@
-{
-  "name": "conditional-request-routing-workflow",
-  "version": "1.0.0",
-  "goal": "Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result",
-  "inputs": [
-    {
-      "name": "request",
-      "type": "string",
-      "description": "The input text or request to be evaluated and routed"
-    }
-  ],
-  "steps": [
-    {
-      "node": "Start",
-      "agent_type": "input",
-      "description": "Prompt user and capture the request into state field 'request'",
-      "next": "Classify"
-    },
-    {
-      "node": "Classify",
-      "agent_type": "branching",
-      "description": "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "decision",
-      "next_node": "Approve",
-      "on_failure": "Reject"
-    },
-    {
-      "node": "Approve",
-      "agent_type": "default",
-      "description": "Format and output an approval message containing the request",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "result",
-      "prompt": "Request approved: {request}"
-    },
-    {
-      "node": "Reject",
-      "agent_type": "default",
-      "description": "Format and output a rejection message containing the request",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "result",
-      "prompt": "Request rejected: {request}"
-    }
-  ],
-  "outputs": [
-    {
-      "name": "result",
-      "type": "string",
-      "description": "Final formatted message indicating the outcome (approved or rejected)"
-    },
-    {
-      "name": "decision",
-      "type": "string",
-      "description": "Routing decision produced by the branching node"
-    }
-  ],
-  "failure_modes": [
-    "Empty or missing request input prevents meaningful classification",
-    "Branching node fails to resolve a valid route and defaults to rejection path",
-    "Prompt template variable missing causes malformed output"
-  ],
-  "confidence": 0.92,
-  "explanation": "Extracted from the ReviewFlow CSV example in the AgentMap README. This is a declarative, CSV-defined LangGraph workflow demonstrating the reusable pattern of input -> branching classification -> conditional handling paths. It can be generalized for ticket triage, content moderation, or any route-by-condition use case.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}