Add Skill: langgraph-explainable-agent

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

skills/conditional-review-workflow/SKILL.md

@@ -1,56 +0,0 @@
----
-name: conditional-review-workflow
-version: 1.0.0
-description: Process a user request through branching logic to approve or reject it,
-  producing a routed decision result
-inputs:
-- 'request: string - The user''s request or input collected at runtime via the input
-  agent'
-steps:
-- 'Start: Input agent collects the user request and stores it in the ''request'' state
-  field, then routes to Classify node'
-- 'Classify: Branching agent evaluates the ''request'' field and routes to Approve
-  node on success or Reject node on failure (on_failure)'
-- 'Approve: Default agent formats an approval message using the request and stores
-  it in the ''result'' output field'
-- 'Reject: Default agent formats a rejection message using the request and stores
-  it in the ''result'' output field'
-outputs:
-- 'result: string - Final message indicating whether the request was approved or rejected,
-  containing the original request'
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-review-workflow
-
-Process a user request through branching logic to approve or reject it, producing a routed decision result
-
-## Steps
-
-1. Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node
-2. Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)
-3. Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-4. Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field
-
-## Inputs
-
-- request: string - The user's request or input collected at runtime via the input agent
-
-## Outputs
-
-- result: string - Final message indicating whether the request was approved or rejected, containing the original request
-
-## Failure Modes
-
-- Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)
-- Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration
-- LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

skills/conditional-review-workflow/commands.md

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

skills/conditional-review-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-review-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request: string - The user's request or input collected at runtime via the input agent
-# Process: Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node → Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure) → Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-# Outputs: result: string - Final message indicating whether the request was approved or rejected, containing the original request
-```

skills/conditional-review-workflow/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "conditional-review-workflow",
-  "version": "1.0.0",
-  "goal": "Process a user request through branching logic to approve or reject it, producing a routed decision result",
-  "inputs": [
-    "request: string - The user's request or input collected at runtime via the input agent"
-  ],
-  "steps": [
-    "Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node",
-    "Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)",
-    "Approve: Default agent formats an approval message using the request and stores it in the 'result' output field",
-    "Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field"
-  ],
-  "outputs": [
-    "result: string - Final message indicating whether the request was approved or rejected, containing the original request"
-  ],
-  "failure_modes": [
-    "Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)",
-    "Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration",
-    "LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline"
-  ],
-  "confidence": 0.85,
-  "explanation": "Extracted from AgentMap's documented CSV workflow example (ReviewFlow). This is a reusable conditional routing pattern that can be adapted for any approval/rejection, triage, or binary-decision scenario by modifying the branching prompt and agent types. The CSV-based declarative format makes it portable across the AgentMap framework.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.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
+1.  
+1. l
+1. a
+1. n
+1. g
+1. g
+1. r
+1. a
+1. p
+1. h
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+1. a
+1. c
+1. k
+1. a
+1. g
+1. e
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+1.  
+1. 2
+1. .
+1.  
+1. C
+1. o
+1. n
+1. f
+1. i
+1. g
+1. u
+1. r
+1. e
+1.  
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+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
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+1. i
+1. n
+1. e
+1.  
+1. a
+1. g
+1. e
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+1. t
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+1. h
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+1.  
+1. 4
+1. .
+1.  
+1. R
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+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: conditional-review-workflow
+# Tests: langgraph-explainable-agent
 
 ## Test Checklist