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

Extracted from: https://github.com/0verL1nk/PaperSage.git
Score: 1.0

skills/conditional-request-routing-workflow/SKILL.md

@@ -1,81 +0,0 @@
----
-name: conditional-request-routing-workflow
-version: 1.0.0
-description: Collect a request, classify it via branching logic, and route to an approval
-  or rejection handler to produce a final result
-inputs:
-- name: request
-  type: string
-  description: The input text or request to be evaluated and routed
-steps:
-- node: Start
-  agent_type: input
-  description: Prompt user and capture the request into state field 'request'
-  next: Classify
-- node: Classify
-  agent_type: branching
-  description: Evaluate the request and set 'decision' field, routing to Approve on
-    success or Reject on failure
-  input_fields:
-  - request
-  output_field: decision
-  next_node: Approve
-  on_failure: Reject
-- node: Approve
-  agent_type: default
-  description: Format and output an approval message containing the request
-  input_fields:
-  - request
-  output_field: result
-  prompt: 'Request approved: {request}'
-- node: Reject
-  agent_type: default
-  description: Format and output a rejection message containing the request
-  input_fields:
-  - request
-  output_field: result
-  prompt: 'Request rejected: {request}'
-outputs:
-- name: result
-  type: string
-  description: Final formatted message indicating the outcome (approved or rejected)
-- name: decision
-  type: string
-  description: Routing decision produced by the branching node
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.92
----
-
-# conditional-request-routing-workflow
-
-Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result
-
-## Steps
-
-1. {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'}
-2. {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'}
-3. {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-4. {'node': 'Reject', 'agent_type': 'default', 'description': 'Format and output a rejection message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request rejected: {request}'}
-
-## Inputs
-
-- {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
-
-## Outputs
-
-- {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}
-- {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
-
-## Failure Modes
-
-- Empty or missing request input prevents meaningful classification
-- Branching node fails to resolve a valid route and defaults to rejection path
-- Prompt template variable missing causes malformed output
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.92

skills/conditional-request-routing-workflow/commands.md

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

skills/conditional-request-routing-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-request-routing-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
-# Process: {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'} → {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'} → {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
-# Outputs: {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}, {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
-```

skills/conditional-request-routing-workflow/metadata.json

@@ -1,72 +0,0 @@
-{
-  "name": "conditional-request-routing-workflow",
-  "version": "1.0.0",
-  "goal": "Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result",
-  "inputs": [
-    {
-      "name": "request",
-      "type": "string",
-      "description": "The input text or request to be evaluated and routed"
-    }
-  ],
-  "steps": [
-    {
-      "node": "Start",
-      "agent_type": "input",
-      "description": "Prompt user and capture the request into state field 'request'",
-      "next": "Classify"
-    },
-    {
-      "node": "Classify",
-      "agent_type": "branching",
-      "description": "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "decision",
-      "next_node": "Approve",
-      "on_failure": "Reject"
-    },
-    {
-      "node": "Approve",
-      "agent_type": "default",
-      "description": "Format and output an approval message containing the request",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "result",
-      "prompt": "Request approved: {request}"
-    },
-    {
-      "node": "Reject",
-      "agent_type": "default",
-      "description": "Format and output a rejection message containing the request",
-      "input_fields": [
-        "request"
-      ],
-      "output_field": "result",
-      "prompt": "Request rejected: {request}"
-    }
-  ],
-  "outputs": [
-    {
-      "name": "result",
-      "type": "string",
-      "description": "Final formatted message indicating the outcome (approved or rejected)"
-    },
-    {
-      "name": "decision",
-      "type": "string",
-      "description": "Routing decision produced by the branching node"
-    }
-  ],
-  "failure_modes": [
-    "Empty or missing request input prevents meaningful classification",
-    "Branching node fails to resolve a valid route and defaults to rejection path",
-    "Prompt template variable missing causes malformed output"
-  ],
-  "confidence": 0.92,
-  "explanation": "Extracted from the ReviewFlow CSV example in the AgentMap README. This is a declarative, CSV-defined LangGraph workflow demonstrating the reusable pattern of input -> branching classification -> conditional handling paths. It can be generalized for ticket triage, content moderation, or any route-by-condition use case.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}

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

@@ -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

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

@@ -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

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

@@ -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
+```

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

@@ -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
+}

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

@@ -1,4 +1,4 @@
-# Tests: conditional-request-routing-workflow
+# Tests: literature-review-with-traceable-ai-evidence
 
 ## Test Checklist