Add Skill: conditional-input-routing

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

skills/conditional-input-routing/SKILL.md

@@ -0,0 +1,73 @@
+---
+name: conditional-input-routing
+version: 1.0.0
+description: Collect user input, classify or branch on its content, and route to appropriate
+  success or failure handling paths to produce a final result
+inputs:
+- name: request
+  type: string
+  description: User-provided input or request to be evaluated
+steps:
+- name: Start
+  action: input agent captures initial request into state
+  agent_type: input
+  output_field: request
+- name: Classify
+  action: branching agent evaluates request and routes to next_node or on_failure
+  agent_type: branching
+  input_fields: request
+  output_field: decision
+  next_node: Approve
+  on_failure: Reject
+- name: Approve
+  action: default agent processes approved request and sets result
+  agent_type: default
+  input_fields: request
+  output_field: result
+  prompt: 'Request approved: {request}'
+- name: Reject
+  action: default agent processes rejected request and sets result
+  agent_type: default
+  input_fields: request
+  output_field: result
+  prompt: 'Request rejected: {request}'
+outputs:
+- name: result
+  type: string
+  description: Final output from either the approve or reject branch
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.9
+---
+
+# conditional-input-routing
+
+Collect user input, classify or branch on its content, and route to appropriate success or failure handling paths to produce a final result
+
+## Steps
+
+1. {'name': 'Start', 'action': 'input agent captures initial request into state', 'agent_type': 'input', 'output_field': 'request'}
+2. {'name': 'Classify', 'action': 'branching agent evaluates request and routes to next_node or on_failure', 'agent_type': 'branching', 'input_fields': 'request', 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'}
+3. {'name': 'Approve', 'action': 'default agent processes approved request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request approved: {request}'}
+4. {'name': 'Reject', 'action': 'default agent processes rejected request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request rejected: {request}'}
+
+## Inputs
+
+- {'name': 'request', 'type': 'string', 'description': 'User-provided input or request to be evaluated'}
+
+## Outputs
+
+- {'name': 'result', 'type': 'string', 'description': 'Final output from either the approve or reject branch'}
+
+## Failure Modes
+
+- Input node fails to capture request (handled by on_failure if defined)
+- Branching condition not met and no on_failure path defined
+- Missing input_fields in state causing agent execution error
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.9

skills/conditional-input-routing/commands.md

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+# Commands: conditional-input-routing
+
+## Available Commands
+
+- `/skill conditional-input-routing` — Load this skill
+- `/run conditional-input-routing` — Execute workflow

skills/conditional-input-routing/examples.md

@@ -0,0 +1,10 @@
+# Examples: conditional-input-routing
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: {'name': 'request', 'type': 'string', 'description': 'User-provided input or request to be evaluated'}
+# Process: {'name': 'Start', 'action': 'input agent captures initial request into state', 'agent_type': 'input', 'output_field': 'request'} → {'name': 'Classify', 'action': 'branching agent evaluates request and routes to next_node or on_failure', 'agent_type': 'branching', 'input_fields': 'request', 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'} → {'name': 'Approve', 'action': 'default agent processes approved request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request approved: {request}'}
+# Outputs: {'name': 'result', 'type': 'string', 'description': 'Final output from either the approve or reject branch'}
+```

skills/conditional-input-routing/metadata.json

@@ -0,0 +1,61 @@
+{
+  "name": "conditional-input-routing",
+  "version": "1.0.0",
+  "goal": "Collect user input, classify or branch on its content, and route to appropriate success or failure handling paths to produce a final result",
+  "inputs": [
+    {
+      "name": "request",
+      "type": "string",
+      "description": "User-provided input or request to be evaluated"
+    }
+  ],
+  "steps": [
+    {
+      "name": "Start",
+      "action": "input agent captures initial request into state",
+      "agent_type": "input",
+      "output_field": "request"
+    },
+    {
+      "name": "Classify",
+      "action": "branching agent evaluates request and routes to next_node or on_failure",
+      "agent_type": "branching",
+      "input_fields": "request",
+      "output_field": "decision",
+      "next_node": "Approve",
+      "on_failure": "Reject"
+    },
+    {
+      "name": "Approve",
+      "action": "default agent processes approved request and sets result",
+      "agent_type": "default",
+      "input_fields": "request",
+      "output_field": "result",
+      "prompt": "Request approved: {request}"
+    },
+    {
+      "name": "Reject",
+      "action": "default agent processes rejected request and sets result",
+      "agent_type": "default",
+      "input_fields": "request",
+      "output_field": "result",
+      "prompt": "Request rejected: {request}"
+    }
+  ],
+  "outputs": [
+    {
+      "name": "result",
+      "type": "string",
+      "description": "Final output from either the approve or reject branch"
+    }
+  ],
+  "failure_modes": [
+    "Input node fails to capture request (handled by on_failure if defined)",
+    "Branching condition not met and no on_failure path defined",
+    "Missing input_fields in state causing agent execution error"
+  ],
+  "confidence": 0.9,
+  "explanation": "Extracted from AgentMap's documented conditional workflow example (ReviewFlow). This CSV-declared pattern of input to branching to dual-path handling is reusable for any approval, triage, or routing scenario without writing orchestration code.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/conditional-input-routing/tests.md

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