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

Extracted from: https://github.com/0verL1nk/PaperSage.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/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-review-workflow
+# Tests: literature-review-with-traceable-ai-evidence
 
 ## Test Checklist