Add Skill: langgraph-explainable-agent

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.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
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+
+## 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

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+# 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: literature-review-with-traceable-ai-evidence
+# Tests: langgraph-explainable-agent
 
 ## Test Checklist
 

skills/literature-review-with-traceable-ai-evidence/SKILL.md

@@ -1,77 +0,0 @@
----
-name: literature-review-with-traceable-ai-evidence
-version: 1.0.0
-description: Enable researchers to ingest documents, asynchronously index them, and
-  interact with multi-agent AI to answer questions with verifiable citations to original
-  text.
-inputs:
-- Documents in PDF, Office, image, or text formats
-- Research questions or topics of interest
-- 'Optional: user model configuration via .env or settings'
-steps:
-- Upload documents to a project (via desktop app or web UI)
-- System asynchronously converts Office docs to PDF if needed, runs OCR to extract
-  text with coordinates, chunks and embeds into vector store
-- User starts a main research session or creates exploration branches without waiting
-  for indexing to finish
-- Leader agent receives query and delegates subtasks to researcher, reviewer, writer
-  subagents
-- Subagents perform hybrid retrieval and rerank to find relevant chunks with source
-  coordinates
-- Agents synthesize answers and return evidence with clickable citations that highlight
-  original pages
-- User verifies conclusions by navigating to cited source locations and can save notes
-  to research memory or mind map
-outputs:
-- AI-generated answers with traceable evidence (coordinates, page highlights)
-- Research session history with branches
-- Indexed document library for future queries
-- Mind maps or structured notes
-- Persistent run events for resuming sessions
-tags: []
-metadata:
-  source_repo: https://github.com/0verL1nk/PaperSage.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# literature-review-with-traceable-ai-evidence
-
-Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.
-
-## Steps
-
-1. Upload documents to a project (via desktop app or web UI)
-2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
-3. User starts a main research session or creates exploration branches without waiting for indexing to finish
-4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
-5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
-6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
-7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map
-
-## Inputs
-
-- Documents in PDF, Office, image, or text formats
-- Research questions or topics of interest
-- Optional: user model configuration via .env or settings
-
-## Outputs
-
-- AI-generated answers with traceable evidence (coordinates, page highlights)
-- Research session history with branches
-- Indexed document library for future queries
-- Mind maps or structured notes
-- Persistent run events for resuming sessions
-
-## Failure Modes
-
-- Missing local Office/LibreOffice converter causes document conversion failure
-- First-time model download may be slow or require network
-- OCR may have low confidence on poor quality scans
-- Retrieval might miss context if chunking splits semantics
-- Multi-agent coordination could produce conflicting intermediate results
-
-## Source
-
-Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
-Confidence: 0.85

skills/literature-review-with-traceable-ai-evidence/commands.md

@@ -1,6 +0,0 @@
-# Commands: literature-review-with-traceable-ai-evidence
-
-## Available Commands
-
-- `/skill literature-review-with-traceable-ai-evidence` — Load this skill
-- `/run literature-review-with-traceable-ai-evidence` — Execute workflow

skills/literature-review-with-traceable-ai-evidence/examples.md

@@ -1,10 +0,0 @@
-# Examples: literature-review-with-traceable-ai-evidence
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Documents in PDF, Office, image, or text formats, Research questions or topics of interest, Optional: user model configuration via .env or settings
-# Process: Upload documents to a project (via desktop app or web UI) → System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store → User starts a main research session or creates exploration branches without waiting for indexing to finish
-# Outputs: AI-generated answers with traceable evidence (coordinates, page highlights), Research session history with branches, Indexed document library for future queries, Mind maps or structured notes, Persistent run events for resuming sessions
-```

skills/literature-review-with-traceable-ai-evidence/metadata.json

@@ -1,37 +0,0 @@
-{
-  "name": "literature-review-with-traceable-ai-evidence",
-  "version": "1.0.0",
-  "goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
-  "inputs": [
-    "Documents in PDF, Office, image, or text formats",
-    "Research questions or topics of interest",
-    "Optional: user model configuration via .env or settings"
-  ],
-  "steps": [
-    "Upload documents to a project (via desktop app or web UI)",
-    "System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store",
-    "User starts a main research session or creates exploration branches without waiting for indexing to finish",
-    "Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents",
-    "Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates",
-    "Agents synthesize answers and return evidence with clickable citations that highlight original pages",
-    "User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map"
-  ],
-  "outputs": [
-    "AI-generated answers with traceable evidence (coordinates, page highlights)",
-    "Research session history with branches",
-    "Indexed document library for future queries",
-    "Mind maps or structured notes",
-    "Persistent run events for resuming sessions"
-  ],
-  "failure_modes": [
-    "Missing local Office/LibreOffice converter causes document conversion failure",
-    "First-time model download may be slow or require network",
-    "OCR may have low confidence on poor quality scans",
-    "Retrieval might miss context if chunking splits semantics",
-    "Multi-agent coordination could produce conflicting intermediate results"
-  ],
-  "confidence": 0.85,
-  "explanation": "The README describes PaperSage's core workflow: asynchronous document ingestion with OCR/indexing, followed by multi-agent question answering with cited evidence. This process is not tied to the specific codebase and can be reused as a general literature review methodology for any document-centric research using RAG and agent collaboration.",
-  "source_repo": "https://github.com/0verL1nk/PaperSage.git",
-  "score": 1.0
-}