Add Skill: text-concatenation

Extracted from: https://github.com/temiroff/Blacknode.git
Score: 1.0

skills/langgraph-csv-workflow/SKILL.md

@@ -1,89 +0,0 @@
----
-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

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

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

@@ -1,29 +0,0 @@
-{
-  "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/text-concatenation/SKILL.md

@@ -0,0 +1,61 @@
+---
+name: text-concatenation
+version: 1.0.0
+description: Concatenate two text inputs using Blacknode node graph
+inputs:
+- text_a
+- text_b
+steps:
+- Create a Blacknode Graph instance
+- Add a Text node with param value set to text_a
+- Add a second Text node with param value set to text_b
+- Add a Concat node that accepts inputs 'a' and 'b' and outputs 'value'
+- Add an Output node with input port 'value'
+- Connect first Text node 'value' port to Concat 'a' port
+- Connect second Text node 'value' port to Concat 'b' port
+- Connect Concat 'value' port to Output 'value' port
+- Invoke graph cook on Output 'value' to evaluate and return result
+outputs:
+- concatenated_text
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.9
+---
+
+# text-concatenation
+
+Concatenate two text inputs using Blacknode node graph
+
+## Steps
+
+1. Create a Blacknode Graph instance
+2. Add a Text node with param value set to text_a
+3. Add a second Text node with param value set to text_b
+4. Add a Concat node that accepts inputs 'a' and 'b' and outputs 'value'
+5. Add an Output node with input port 'value'
+6. Connect first Text node 'value' port to Concat 'a' port
+7. Connect second Text node 'value' port to Concat 'b' port
+8. Connect Concat 'value' port to Output 'value' port
+9. Invoke graph cook on Output 'value' to evaluate and return result
+
+## Inputs
+
+- text_a
+- text_b
+
+## Outputs
+
+- concatenated_text
+
+## Failure Modes
+
+- Port name mismatch causes connection error
+- Missing runtime or graph not initialized
+- Cook on nonexistent node returns error
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.9

skills/text-concatenation/commands.md

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

skills/text-concatenation/examples.md

@@ -0,0 +1,10 @@
+# Examples: text-concatenation
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: text_a, text_b
+# Process: Create a Blacknode Graph instance → Add a Text node with param value set to text_a → Add a second Text node with param value set to text_b
+# Outputs: concatenated_text
+```

skills/text-concatenation/metadata.json

@@ -0,0 +1,32 @@
+{
+  "name": "text-concatenation",
+  "version": "1.0.0",
+  "goal": "Concatenate two text inputs using Blacknode node graph",
+  "inputs": [
+    "text_a",
+    "text_b"
+  ],
+  "steps": [
+    "Create a Blacknode Graph instance",
+    "Add a Text node with param value set to text_a",
+    "Add a second Text node with param value set to text_b",
+    "Add a Concat node that accepts inputs 'a' and 'b' and outputs 'value'",
+    "Add an Output node with input port 'value'",
+    "Connect first Text node 'value' port to Concat 'a' port",
+    "Connect second Text node 'value' port to Concat 'b' port",
+    "Connect Concat 'value' port to Output 'value' port",
+    "Invoke graph cook on Output 'value' to evaluate and return result"
+  ],
+  "outputs": [
+    "concatenated_text"
+  ],
+  "failure_modes": [
+    "Port name mismatch causes connection error",
+    "Missing runtime or graph not initialized",
+    "Cook on nonexistent node returns error"
+  ],
+  "confidence": 0.9,
+  "explanation": "The repo contains example converted workflows; converted_text_pipeline.py demonstrates a simple reusable pattern of two source nodes feeding a concatenation node into an output, applicable to any string joining task in Blacknode.",
+  "source_repo": "https://github.com/temiroff/Blacknode.git",
+  "score": 1.0
+}

skills/text-concatenation/tests.md

@@ -1,4 +1,4 @@
-# Tests: langgraph-csv-workflow
+# Tests: text-concatenation
 
 ## Test Checklist