Add Skill: langgraph-csv-workflow

Extracted from: https://github.com/jwwelbor/AgentMap.git
Score: 1.0

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

@@ -1,77 +0,0 @@
----
-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

@@ -1,6 +0,0 @@
-# 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

@@ -1,10 +0,0 @@
-# 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

@@ -1,36 +0,0 @@
-{
-  "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/langgraph-csv-workflow/SKILL.md

@@ -0,0 +1,89 @@
+---
+name: langgraph-csv-workflow
+version: 1.0.0
+description: Transform simple CSV files into powerful AI agent workflows using LangGraph
+  orchestration
+inputs:
+- 'CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
+steps:
+- Define workflow in CSV format specifying graph nodes, agent types, and data flow
+  between them
+- Configure LLM providers and storage backends in the agentmap configuration files
+- Execute the workflow using the agentmap CLI or Python API
+outputs:
+- Executed workflow with agent decisions and state transitions
+- Traced execution path through the graph nodes
+- Logged agent interactions and output fields populated
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-csv-workflow
+
+Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml fastapi uvicorn
+```
+
+**Setup steps:**
+
+1. Install agentmap: pip install agentmap[all]
+1. Initialize configuration: agentmap init-config
+1. Configure LLM providers in agentmap_config.yaml
+1. Run workflow: agentmap run workflow.csv
+
+## Key Files
+
+- `agentmap_config.yaml - Main configuration for LLM providers and paths`
+- `agentmap_config_storage.yaml - Storage configuration for CSV/JSON/Vector DBs`
+- `hello_world.csv - Sample workflow definition`
+
+## Steps
+
+1. Define workflow in CSV format specifying graph nodes, agent types, and data flow between them
+2. Configure LLM providers and storage backends in the agentmap configuration files
+3. Execute the workflow using the agentmap CLI or Python API
+
+## Implementation Details
+
+```python
+CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field
+```
+
+```python
+CLI command: agentmap run hello_world.csv --pretty
+```
+
+## Inputs
+
+- CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
+
+## Outputs
+
+- Executed workflow with agent decisions and state transitions
+- Traced execution path through the graph nodes
+- Logged agent interactions and output fields populated
+
+## Failure Modes
+
+- Invalid CSV format causing parse errors during workflow loading
+- Missing or misconfigured LLM provider credentials leading to runtime failures
+- Incorrect agent configuration (e.g., missing input_fields) causing processing errors
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.95

skills/langgraph-csv-workflow/commands.md

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

skills/langgraph-csv-workflow/examples.md

@@ -0,0 +1,10 @@
+# Examples: langgraph-csv-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
+# Process: Define workflow in CSV format specifying graph nodes, agent types, and data flow between them → Configure LLM providers and storage backends in the agentmap configuration files → Execute the workflow using the agentmap CLI or Python API
+# Outputs: Executed workflow with agent decisions and state transitions, Traced execution path through the graph nodes, Logged agent interactions and output fields populated
+```

skills/langgraph-csv-workflow/metadata.json

@@ -0,0 +1,29 @@
+{
+  "name": "langgraph-csv-workflow",
+  "version": "1.0.0",
+  "goal": "Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration",
+  "inputs": [
+    "CSV workflow files 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 configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml"
+  ],
+  "steps": [
+    "Define workflow in CSV format specifying graph nodes, agent types, and data flow between them",
+    "Configure LLM providers and storage backends in the agentmap configuration files",
+    "Execute the workflow using the agentmap CLI or Python API"
+  ],
+  "outputs": [
+    "Executed workflow with agent decisions and state transitions",
+    "Traced execution path through the graph nodes",
+    "Logged agent interactions and output fields populated"
+  ],
+  "failure_modes": [
+    "Invalid CSV format causing parse errors during workflow loading",
+    "Missing or misconfigured LLM provider credentials leading to runtime failures",
+    "Incorrect agent configuration (e.g., missing input_fields) causing processing errors"
+  ],
+  "confidence": 0.95,
+  "explanation": "AgentMap provides a declarative pattern where workflows are defined in CSV files with specific columns describing graph nodes, agent types, and data flow. This pattern can be adapted to create multi-agent systems with LangGraph, supporting various LLM providers and storage backends. The workflow is reusable across different use cases by simply modifying the CSV definition.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/langgraph-csv-workflow/tests.md

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