Add Skill: literature-review-with-traceable-ai-evidence

Extracted from: https://github.com/0verL1nk/PaperSage.git Score: 1.0
2026-08-08 23:04:46 +00:00
9 changed files with 131 additions and 148 deletions
@@ -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
@@ -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
@@ -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
 ```
@@ -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
 }
@@ -0,0 +1,77 @@
 ---
 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
@@ -0,0 +1,6 @@
 # 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
@@ -0,0 +1,10 @@
 # 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
 ```
@@ -0,0 +1,37 @@
 {
  "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
 }
@@ -1,4 +1,4 @@
-# Tests: graph-based-node-workflow
+# Tests: literature-review-with-traceable-ai-evidence
 ## Test Checklist
`@@ -1,4 +1,4 @@`
	`# Tests: graph-based-node-workflow`	`# Tests: literature-review-with-traceable-ai-evidence`

	`## Test Checklist`	`## Test Checklist`