Add Skill: langgraph-explainable-agent

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git
Score: 1.0

skills/graph-based-node-orchestration/SKILL.md

@@ -1,108 +0,0 @@
----
-name: graph-based-node-orchestration
-version: 1.0.0
-description: Build a typed node graph that processes data through a sequence of operations
-  and produces a final result
-inputs:
-- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
-- blacknode package with Graph, Node, and cook functionality
-- Python script defining node types with inputs/outputs and connecting them via edges
-steps:
-- Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available
-  via require_nim_api_key()
-- Create a bn.Graph() instance to hold all nodes and their connections
-- Define individual nodes with specific input/output parameters (e.g., Text node with
-  'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-- Connect nodes together using edge definitions (from_port -> to_port) to establish
-  data flow
-- Execute the graph using g.cook() to run the pipeline and process data through the
-  node chain
-- Extract the final result from the output node to complete the workflow
-outputs:
-- A fully constructed graph with typed nodes and defined connections
-- Executed result (e.g., processed text, summary, or other output) from the final
-  node
-- A reusable pattern that can be adapted to different models, hardware, or task types
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# graph-based-node-orchestration
-
-Build a typed node graph that processes data through a sequence of operations and produces a final result
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install blacknode >= 0.3.0 Python >= 3.11 NVIDIA NIM API key (optional but recommended) Anthropic, OpenAI, or other LLM models
-```
-
-**Setup steps:**
-
-1. Clone the repository and install dependencies: pip install -e .
-1. Set NVIDIA_API_KEY or other required API keys in .env
-1. Run the example script: python examples/hello_agent.py
-1. For production, configure hardware pairing and deploy via the blacknode CLI
-
-## Key Files
-
-- `examples/converted_nvidia_nim.py - Full graph with Model, Text, LLMAgent, Output nodes`
-- `examples/hello_agent.py - Minimal agent example connecting Literal → LLMAgent → Print`
-- `blacknode/core - Graph and node implementation (internal)`
-
-## Steps
-
-1. Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()
-2. Create a bn.Graph() instance to hold all nodes and their connections
-3. Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-4. Connect nodes together using edge definitions (from_port -> to_port) to establish data flow
-5. Execute the graph using g.cook() to run the pipeline and process data through the node chain
-6. Extract the final result from the output node to complete the workflow
-
-## Implementation Details
-
-```python
-g = bn.Graph()
-```
-
-```python
-model = g.node('Model', **{'value': 'nim:meta/llama-3.1-8b-instruct'})
-```
-
-```python
-agent = g.node('LLMAgent', **{model})
-```
-
-```python
-result = g.cook(output, 'value')
-```
-
-## Inputs
-
-- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
-- blacknode package with Graph, Node, and cook functionality
-- Python script defining node types with inputs/outputs and connecting them via edges
-
-## Outputs
-
-- A fully constructed graph with typed nodes and defined connections
-- Executed result (e.g., processed text, summary, or other output) from the final node
-- A reusable pattern that can be adapted to different models, hardware, or task types
-
-## Failure Modes
-
-- Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail
-- Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow
-- Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail
-- Graph execution error due to incorrect edge configuration or circular dependencies
-- Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/graph-based-node-orchestration/commands.md

@@ -1,6 +0,0 @@
-# Commands: graph-based-node-orchestration
-
-## Available Commands
-
-- `/skill graph-based-node-orchestration` — Load this skill
-- `/run graph-based-node-orchestration` — Execute workflow

skills/graph-based-node-orchestration/examples.md

@@ -1,10 +0,0 @@
-# Examples: graph-based-node-orchestration
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model, blacknode package with Graph, Node, and cook functionality, Python script defining node types with inputs/outputs and connecting them via edges
-# Process: Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key() → Create a bn.Graph() instance to hold all nodes and their connections → Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-# Outputs: A fully constructed graph with typed nodes and defined connections, Executed result (e.g., processed text, summary, or other output) from the final node, A reusable pattern that can be adapted to different models, hardware, or task types
-```

skills/graph-based-node-orchestration/metadata.json

@@ -1,34 +0,0 @@
-{
-  "name": "graph-based-node-orchestration",
-  "version": "1.0.0",
-  "goal": "Build a typed node graph that processes data through a sequence of operations and produces a final result",
-  "inputs": [
-    "NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model",
-    "blacknode package with Graph, Node, and cook functionality",
-    "Python script defining node types with inputs/outputs and connecting them via edges"
-  ],
-  "steps": [
-    "Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()",
-    "Create a bn.Graph() instance to hold all nodes and their connections",
-    "Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)",
-    "Connect nodes together using edge definitions (from_port -> to_port) to establish data flow",
-    "Execute the graph using g.cook() to run the pipeline and process data through the node chain",
-    "Extract the final result from the output node to complete the workflow"
-  ],
-  "outputs": [
-    "A fully constructed graph with typed nodes and defined connections",
-    "Executed result (e.g., processed text, summary, or other output) from the final node",
-    "A reusable pattern that can be adapted to different models, hardware, or task types"
-  ],
-  "failure_modes": [
-    "Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail",
-    "Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow",
-    "Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail",
-    "Graph execution error due to incorrect edge configuration or circular dependencies",
-    "Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a declarative graph-based orchestration pattern where nodes are connected via explicit ports and data flows through the graph. The pattern is highly reusable across different domains (robotics, research, data processing) because it separates graph structure from execution logic. The same graph construction and cook pattern can be adapted to different models (NIM, Anthropic, OpenAI), hardware targets (CPU, GPU, Jetson), and task types (LLM reasoning, file I/O, sensor processing).",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}

skills/langgraph-explainable-agent/SKILL.md

@@ -0,0 +1,578 @@
+---
+name: langgraph-explainable-agent
+version: 1.0.0
+description: Orchestrate an AI agent workflow that provides explainable answers with
+  citations using knowledge graph retrieval and permission-aware search
+inputs:
+- user_query - text input from the user
+- context_documentation - pre-indexed documents for retrieval
+- knowledge_graph - graph database for entity relationships
+steps:
+- 'Step 1: Create LangGraph chain with agent that processes user query through knowledge
+  graph retrieval and citation generation'
+- 'Step 2: Execute the chain to generate explainable answer with block citations'
+- 'Step 3: Apply permission-aware filtering on retrieved context before final answer'
+outputs:
+- explainable_answer_with_citations - final response with source references
+- actionable_results - structured output for downstream tasks
+tags: []
+metadata:
+  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-explainable-agent
+
+Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langchain langgraph neoelephant pydantic
+```
+
+**Setup steps:**
+
+1. 1
+1. .
+1.  
+1. I
+1. n
+1. s
+1. t
+1. a
+1. l
+1. l
+1.  
+1. l
+1. a
+1. n
+1. g
+1. c
+1. h
+1. a
+1. i
+1. n
+1.  
+1. a
+1. n
+1. d
+1.  
+1. l
+1. a
+1. n
+1. g
+1. g
+1. r
+1. a
+1. p
+1. h
+1.  
+1. p
+1. a
+1. c
+1. k
+1. a
+1. g
+1. e
+1. s
+1.  
+1. 2
+1. .
+1.  
+1. C
+1. o
+1. n
+1. f
+1. i
+1. g
+1. u
+1. r
+1. e
+1.  
+1. k
+1. n
+1. o
+1. w
+1. l
+1. e
+1. d
+1. g
+1. e
+1.  
+1. g
+1. r
+1. a
+1. p
+1. h
+1.  
+1. c
+1. o
+1. n
+1. n
+1. e
+1. c
+1. t
+1. i
+1. o
+1. n
+1.  
+1. 3
+1. .
+1.  
+1. D
+1. e
+1. f
+1. i
+1. n
+1. e
+1.  
+1. a
+1. g
+1. e
+1. n
+1. t
+1.  
+1. c
+1. h
+1. a
+1. i
+1. n
+1.  
+1. 4
+1. .
+1.  
+1. R
+1. u
+1. n
+1.  
+1. t
+1. h
+1. e
+1.  
+1. w
+1. o
+1. r
+1. k
+1. f
+1. l
+1. o
+1. w
+
+## Key Files
+
+- `agent.py - main LangGraph chain definition`
+- `workflow_config.yaml - chain configuration`
+
+## Steps
+
+1. Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation
+2. Step 2: Execute the chain to generate explainable answer with block citations
+3. Step 3: Apply permission-aware filtering on retrieved context before final answer
+
+## Implementation Details
+
+```python
+f
+```
+
+```python
+r
+```
+
+```python
+o
+```
+
+```python
+m
+```
+
+```python
+ 
+```
+
+```python
+l
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+g
+```
+
+```python
+r
+```
+
+```python
+a
+```
+
+```python
+p
+```
+
+```python
+h
+```
+
+```python
+ 
+```
+
+```python
+i
+```
+
+```python
+m
+```
+
+```python
+p
+```
+
+```python
+o
+```
+
+```python
+r
+```
+
+```python
+t
+```
+
+```python
+ 
+```
+
+```python
+L
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+G
+```
+
+```python
+r
+```
+
+```python
+a
+```
+
+```python
+p
+```
+
+```python
+h
+```
+
+```python
+;
+```
+
+```python
+ 
+```
+
+```python
+f
+```
+
+```python
+r
+```
+
+```python
+o
+```
+
+```python
+m
+```
+
+```python
+ 
+```
+
+```python
+l
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+c
+```
+
+```python
+h
+```
+
+```python
+a
+```
+
+```python
+i
+```
+
+```python
+n
+```
+
+```python
+ 
+```
+
+```python
+i
+```
+
+```python
+m
+```
+
+```python
+p
+```
+
+```python
+o
+```
+
+```python
+r
+```
+
+```python
+t
+```
+
+```python
+ 
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+;
+```
+
+```python
+ 
+```
+
+```python
+c
+```
+
+```python
+h
+```
+
+```python
+a
+```
+
+```python
+i
+```
+
+```python
+n
+```
+
+```python
+ 
+```
+
+```python
+=
+```
+
+```python
+ 
+```
+
+```python
+L
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+G
+```
+
+```python
+r
+```
+
+```python
+a
+```
+
+```python
+p
+```
+
+```python
+h
+```
+
+```python
+(
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+)
+```
+
+```python
+;
+```
+
+```python
+ 
+```
+
+```python
+c
+```
+
+```python
+h
+```
+
+```python
+a
+```
+
+```python
+i
+```
+
+```python
+n
+```
+
+```python
+.
+```
+
+```python
+r
+```
+
+```python
+u
+```
+
+```python
+n
+```
+
+```python
+(
+```
+
+```python
+)
+```
+
+## Inputs
+
+- user_query - text input from the user
+- context_documentation - pre-indexed documents for retrieval
+- knowledge_graph - graph database for entity relationships
+
+## Outputs
+
+- explainable_answer_with_citations - final response with source references
+- actionable_results - structured output for downstream tasks
+
+## Failure Modes
+
+- Empty knowledge graph causes missing citations
+- Permission denied on source documents blocks retrieval
+- LangGraph chain execution fails due to missing dependencies
+
+## Source
+
+Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
+Confidence: 0.95

skills/langgraph-explainable-agent/commands.md

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

skills/langgraph-explainable-agent/examples.md

@@ -0,0 +1,10 @@
+# Examples: langgraph-explainable-agent
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: user_query - text input from the user, context_documentation - pre-indexed documents for retrieval, knowledge_graph - graph database for entity relationships
+# Process: Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation → Step 2: Execute the chain to generate explainable answer with block citations → Step 3: Apply permission-aware filtering on retrieved context before final answer
+# Outputs: explainable_answer_with_citations - final response with source references, actionable_results - structured output for downstream tasks
+```

skills/langgraph-explainable-agent/metadata.json

@@ -0,0 +1,28 @@
+{
+  "name": "langgraph-explainable-agent",
+  "version": "1.0.0",
+  "goal": "Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search",
+  "inputs": [
+    "user_query - text input from the user",
+    "context_documentation - pre-indexed documents for retrieval",
+    "knowledge_graph - graph database for entity relationships"
+  ],
+  "steps": [
+    "Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation",
+    "Step 2: Execute the chain to generate explainable answer with block citations",
+    "Step 3: Apply permission-aware filtering on retrieved context before final answer"
+  ],
+  "outputs": [
+    "explainable_answer_with_citations - final response with source references",
+    "actionable_results - structured output for downstream tasks"
+  ],
+  "failure_modes": [
+    "Empty knowledge graph causes missing citations",
+    "Permission denied on source documents blocks retrieval",
+    "LangGraph chain execution fails due to missing dependencies"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow demonstrates a reusable LangGraph-based pattern for building explainable AI agents that integrate knowledge graph retrieval and citation generation. The chain can be adapted to different enterprise contexts by swapping the knowledge graph backend and citation format.",
+  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
+  "score": 1.0
+}

skills/langgraph-explainable-agent/tests.md

@@ -1,4 +1,4 @@
-# Tests: graph-based-node-orchestration
+# Tests: langgraph-explainable-agent
 
 ## Test Checklist