Add Skill: langgraph-csv-workflow

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

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

@@ -1,53 +0,0 @@
----
-name: conditional-request-review-workflow
-version: 1.0.0
-description: Classify an input request and route it to an approval or rejection path,
-  producing a final result message.
-inputs:
-- request
-steps:
-- Start node (agent_type=input) collects the user request and stores it in state field
-  'request'.
-- Classify node (agent_type=branching) reads 'request', makes a branching decision,
-  and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'.
-- Approve node (agent_type=default) formats an approval message using 'request' and
-  writes to 'result'.
-- Reject node (agent_type=default) formats a rejection message using 'request' and
-  writes to 'result' (also used if Classify fails).
-outputs:
-- result
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-request-review-workflow
-
-Classify an input request and route it to an approval or rejection path, producing a final result message.
-
-## Steps
-
-1. Start node (agent_type=input) collects the user request and stores it in state field 'request'.
-2. Classify node (agent_type=branching) reads 'request', makes a branching decision, and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'.
-3. Approve node (agent_type=default) formats an approval message using 'request' and writes to 'result'.
-4. Reject node (agent_type=default) formats a rejection message using 'request' and writes to 'result' (also used if Classify fails).
-
-## Inputs
-
-- request
-
-## Outputs
-
-- result
-
-## Failure Modes
-
-- If branching classification fails, workflow defaults to Reject node via on_failure.
-- Input node has no defined on_failure, so workflow may terminate unexpectedly if input collection fails.
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

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

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

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

@@ -1,10 +0,0 @@
-# Examples: conditional-request-review-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request
-# Process: Start node (agent_type=input) collects the user request and stores it in state field 'request'. → Classify node (agent_type=branching) reads 'request', makes a branching decision, and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'. → Approve node (agent_type=default) formats an approval message using 'request' and writes to 'result'.
-# Outputs: result
-```

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

@@ -1,25 +0,0 @@
-{
-  "name": "conditional-request-review-workflow",
-  "version": "1.0.0",
-  "goal": "Classify an input request and route it to an approval or rejection path, producing a final result message.",
-  "inputs": [
-    "request"
-  ],
-  "steps": [
-    "Start node (agent_type=input) collects the user request and stores it in state field 'request'.",
-    "Classify node (agent_type=branching) reads 'request', makes a branching decision, and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'.",
-    "Approve node (agent_type=default) formats an approval message using 'request' and writes to 'result'.",
-    "Reject node (agent_type=default) formats a rejection message using 'request' and writes to 'result' (also used if Classify fails)."
-  ],
-  "outputs": [
-    "result"
-  ],
-  "failure_modes": [
-    "If branching classification fails, workflow defaults to Reject node via on_failure.",
-    "Input node has no defined on_failure, so workflow may terminate unexpectedly if input collection fails."
-  ],
-  "confidence": 0.85,
-  "explanation": "This workflow is directly taken from the AgentMap README 'ReviewFlow' CSV example. It represents a reusable declarative pattern for conditional routing based on input content, adaptable to many binary decision tasks.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.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: conditional-request-review-workflow
+# Tests: langgraph-csv-workflow
 
 ## Test Checklist