From 2f2c7cf5fbc7fcfe363148a78a0985e792931736 Mon Sep 17 00:00:00 2001 From: Hermes Pipeline Date: Thu, 6 Aug 2026 14:41:00 +0000 Subject: [PATCH] Add Skill: langgraph-csv-workflow Extracted from: https://github.com/jwwelbor/AgentMap.git Score: 1.0 --- skills/langgraph-csv-workflow/SKILL.md | 89 +++++++++++++++++++++ skills/langgraph-csv-workflow/commands.md | 6 ++ skills/langgraph-csv-workflow/examples.md | 10 +++ skills/langgraph-csv-workflow/metadata.json | 29 +++++++ skills/langgraph-csv-workflow/tests.md | 9 +++ 5 files changed, 143 insertions(+) create mode 100644 skills/langgraph-csv-workflow/SKILL.md create mode 100644 skills/langgraph-csv-workflow/commands.md create mode 100644 skills/langgraph-csv-workflow/examples.md create mode 100644 skills/langgraph-csv-workflow/metadata.json create mode 100644 skills/langgraph-csv-workflow/tests.md diff --git a/skills/langgraph-csv-workflow/SKILL.md b/skills/langgraph-csv-workflow/SKILL.md new file mode 100644 index 0000000..01caa80 --- /dev/null +++ b/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 diff --git a/skills/langgraph-csv-workflow/commands.md b/skills/langgraph-csv-workflow/commands.md new file mode 100644 index 0000000..07b40fd --- /dev/null +++ b/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 diff --git a/skills/langgraph-csv-workflow/examples.md b/skills/langgraph-csv-workflow/examples.md new file mode 100644 index 0000000..a1a7fce --- /dev/null +++ b/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 +``` diff --git a/skills/langgraph-csv-workflow/metadata.json b/skills/langgraph-csv-workflow/metadata.json new file mode 100644 index 0000000..1e9bdc3 --- /dev/null +++ b/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 +} \ No newline at end of file diff --git a/skills/langgraph-csv-workflow/tests.md b/skills/langgraph-csv-workflow/tests.md new file mode 100644 index 0000000..4cb9f26 --- /dev/null +++ b/skills/langgraph-csv-workflow/tests.md @@ -0,0 +1,9 @@ +# Tests: langgraph-csv-workflow + +## Test Checklist + +- [ ] Workflow has at least 3 steps +- [ ] All inputs are defined +- [ ] All outputs are defined +- [ ] Failure modes are documented +- [ ] Skill can be loaded without errors