Add Skill: langgraph-explainable-agent

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

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

@@ -1,100 +0,0 @@
----
-name: graph-based-node-workflow
-version: 1.0.0
-description: Create and execute typed node graphs for AI/robotics workflows by defining
-  nodes with inputs/outputs and connecting them with edges, then cooking the graph
-  to run the workflow.
-inputs:
-- bn.Graph() - the graph container for the workflow
-- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified
-  inputs and outputs
-- Edge connections mapping from_port to to_port between nodes
-steps:
-- 'Step 1: Initialize a bn.Graph() instance to serve as the workflow container'
-- 'Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal
-  for data, LLMAgent for inference, Concat for combining, Output for final results)'
-- 'Step 3: Create edges connecting nodes by specifying source from_port and destination
-  to_port for each data flow'
-- 'Step 4: Execute the graph by calling g.cook() to process the defined workflow and
-  produce results'
-outputs:
-- Executed workflow results stored in the graph's output nodes
-- Cooked graph ready for inspection, replay, or deployment
-- Potential error states if node dependencies are missing or ports don't match
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# graph-based-node-workflow
-
-Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install blacknode (core Python package) anthropic, openai, docker, petgraph (dependencies) Rust extensions in blacknode-core, blacknode-runtime (optional)
-```
-
-**Setup steps:**
-
-1. Install blacknode with Python 3.11+ and required dependencies
-1. Clone repository and navigate to project directory
-1. Run examples/converted_text_pipeline.py to see basic graph execution
-1. Modify node definitions and edges to create custom workflows
-
-## Key Files
-
-- `examples/converted_text_pipeline.py - basic pipeline pattern`
-- `examples/hello_agent.py - LLM agent workflow pattern`
-- `examples/research_pipeline.py - multi-node research workflow`
-- `blacknode.py - main CLI entry point`
-
-## Steps
-
-1. Step 1: Initialize a bn.Graph() instance to serve as the workflow container
-2. Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)
-3. Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
-4. Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results
-
-## Implementation Details
-
-```python
-g = bn.Graph()
-```
-
-```python
-g._edges = [{"from": "model", "from_port": "value", "to": "agent", "to_port": "model"}]
-```
-
-```python
-result = g.cook(output, "value")
-```
-
-## Inputs
-
-- bn.Graph() - the graph container for the workflow
-- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs
-- Edge connections mapping from_port to to_port between nodes
-
-## Outputs
-
-- Executed workflow results stored in the graph's output nodes
-- Cooked graph ready for inspection, replay, or deployment
-- Potential error states if node dependencies are missing or ports don't match
-
-## Failure Modes
-
-- Missing node dependencies causing undefined variable errors
-- Port mismatch in edge connections leading to no data flow
-- Incomplete graph definition causing cook() to fail
-- Model API key missing or invalid for LLMAgent nodes
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

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

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

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

@@ -1,10 +0,0 @@
-# Examples: graph-based-node-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: bn.Graph() - the graph container for the workflow, Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs, Edge connections mapping from_port to to_port between nodes
-# Process: Step 1: Initialize a bn.Graph() instance to serve as the workflow container → Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results) → Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
-# Outputs: Executed workflow results stored in the graph's output nodes, Cooked graph ready for inspection, replay, or deployment, Potential error states if node dependencies are missing or ports don't match
-```

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

@@ -1,31 +0,0 @@
-{
-  "name": "graph-based-node-workflow",
-  "version": "1.0.0",
-  "goal": "Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.",
-  "inputs": [
-    "bn.Graph() - the graph container for the workflow",
-    "Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs",
-    "Edge connections mapping from_port to to_port between nodes"
-  ],
-  "steps": [
-    "Step 1: Initialize a bn.Graph() instance to serve as the workflow container",
-    "Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)",
-    "Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow",
-    "Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results"
-  ],
-  "outputs": [
-    "Executed workflow results stored in the graph's output nodes",
-    "Cooked graph ready for inspection, replay, or deployment",
-    "Potential error states if node dependencies are missing or ports don't match"
-  ],
-  "failure_modes": [
-    "Missing node dependencies causing undefined variable errors",
-    "Port mismatch in edge connections leading to no data flow",
-    "Incomplete graph definition causing cook() to fail",
-    "Model API key missing or invalid for LLMAgent nodes"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow pattern is reusable across different AI/robotics applications because it provides a standardized way to compose complex pipelines from typed nodes. The pattern can be adapted to various use cases like research pipelines, agent workflows, or robotics control graphs by simply adding/removing nodes and edges while maintaining the same graph-cooking execution model.",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}

skills/langgraph-explainable-agent/SKILL.md

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+---
+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
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+1. a
+1. n
+1. d
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+1. l
+1. a
+1. n
+1. g
+1. g
+1. r
+1. a
+1. p
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+1.  
+1. p
+1. a
+1. c
+1. k
+1. a
+1. g
+1. e
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+1. 2
+1. .
+1.  
+1. C
+1. o
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+1. i
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+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
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+1. p
+1. h
+1.  
+1. c
+1. o
+1. n
+1. n
+1. e
+1. c
+1. t
+1. i
+1. o
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+1. .
+1.  
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+1. g
+1. e
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+1. n
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+1. 4
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+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
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+```
+
+```python
+r
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+```python
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+```python
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+```python
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+```
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+```python
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+```python
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+
+```python
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+```
+
+```python
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+```
+
+```python
+ 
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+
+```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
+.
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+
+```python
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+
+```python
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+
+```python
+;
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+
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+
+```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
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+```
+
+```python
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+
+```python
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+
+```python
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+
+```python
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+```
+
+```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-workflow
+# Tests: langgraph-explainable-agent
 
 ## Test Checklist