Add Skill: conditional-review-workflow

Extracted from: https://github.com/jwwelbor/AgentMap.git
Score: 1.0

skills/conditional-review-workflow/SKILL.md

@@ -0,0 +1,56 @@
+---
+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

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+# 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

@@ -0,0 +1,10 @@
+# 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

@@ -0,0 +1,26 @@
+{
+  "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/conditional-review-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: literature-review-with-traceable-ai-evidence
+# Tests: conditional-review-workflow
 
 ## 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
-}