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1 Commits
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 Hermes Pipeline
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---
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name: conditional-request-routing-workflow
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version: 1.0.0
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description: Collect a request, classify it via branching logic, and route to an approval
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or rejection handler to produce a final result
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inputs:
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- name: request
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type: string
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description: The input text or request to be evaluated and routed
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steps:
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- node: Start
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agent_type: input
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description: Prompt user and capture the request into state field 'request'
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next: Classify
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- node: Classify
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agent_type: branching
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description: Evaluate the request and set 'decision' field, routing to Approve on
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success or Reject on failure
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input_fields:
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- request
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output_field: decision
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next_node: Approve
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on_failure: Reject
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- node: Approve
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agent_type: default
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description: Format and output an approval message containing the request
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input_fields:
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- request
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output_field: result
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prompt: 'Request approved: {request}'
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- node: Reject
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agent_type: default
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description: Format and output a rejection message containing the request
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input_fields:
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- request
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output_field: result
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prompt: 'Request rejected: {request}'
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outputs:
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- name: result
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type: string
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description: Final formatted message indicating the outcome (approved or rejected)
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- name: decision
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type: string
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description: Routing decision produced by the branching node
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tags: []
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metadata:
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source_repo: https://github.com/jwwelbor/AgentMap.git
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extracted_at: ''
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confidence: 0.92
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---
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# conditional-request-routing-workflow
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Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result
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## Steps
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1. {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'}
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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'}
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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}'}
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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}'}
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## Inputs
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- {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
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## Outputs
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- {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}
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- {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
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## Failure Modes
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- Empty or missing request input prevents meaningful classification
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- Branching node fails to resolve a valid route and defaults to rejection path
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- Prompt template variable missing causes malformed output
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## Source
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Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
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Confidence: 0.92
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@@ -1,6 +0,0 @@
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# Commands: conditional-request-routing-workflow
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## Available Commands
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- `/skill conditional-request-routing-workflow` — Load this skill
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- `/run conditional-request-routing-workflow` — Execute workflow
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@@ -1,10 +0,0 @@
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# Examples: conditional-request-routing-workflow
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## Usage Example
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```python
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# How to use this skill
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# Inputs: {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
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# 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}'}
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# 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'}
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```
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@@ -1,72 +0,0 @@
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{
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"name": "conditional-request-routing-workflow",
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"version": "1.0.0",
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"goal": "Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result",
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"inputs": [
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{
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"name": "request",
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"type": "string",
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"description": "The input text or request to be evaluated and routed"
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}
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],
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"steps": [
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{
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"node": "Start",
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"agent_type": "input",
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"description": "Prompt user and capture the request into state field 'request'",
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"next": "Classify"
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},
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{
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"node": "Classify",
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"agent_type": "branching",
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"description": "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure",
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"input_fields": [
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"request"
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],
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"output_field": "decision",
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"next_node": "Approve",
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"on_failure": "Reject"
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},
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{
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"node": "Approve",
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"agent_type": "default",
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"description": "Format and output an approval message containing the request",
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"input_fields": [
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"request"
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],
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"output_field": "result",
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"prompt": "Request approved: {request}"
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},
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{
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"node": "Reject",
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"agent_type": "default",
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"description": "Format and output a rejection message containing the request",
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"input_fields": [
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"request"
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],
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"output_field": "result",
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"prompt": "Request rejected: {request}"
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}
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],
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"outputs": [
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{
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"name": "result",
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"type": "string",
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"description": "Final formatted message indicating the outcome (approved or rejected)"
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},
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{
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"name": "decision",
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"type": "string",
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"description": "Routing decision produced by the branching node"
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}
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],
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"failure_modes": [
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"Empty or missing request input prevents meaningful classification",
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"Branching node fails to resolve a valid route and defaults to rejection path",
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"Prompt template variable missing causes malformed output"
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],
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"confidence": 0.92,
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"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.",
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"source_repo": "https://github.com/jwwelbor/AgentMap.git",
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"score": 1.0
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}
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@@ -0,0 +1,77 @@
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---
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name: literature-review-with-traceable-ai-evidence
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version: 1.0.0
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description: Enable researchers to ingest documents, asynchronously index them, and
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interact with multi-agent AI to answer questions with verifiable citations to original
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text.
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inputs:
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- Documents in PDF, Office, image, or text formats
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- Research questions or topics of interest
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- 'Optional: user model configuration via .env or settings'
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steps:
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- Upload documents to a project (via desktop app or web UI)
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- System asynchronously converts Office docs to PDF if needed, runs OCR to extract
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text with coordinates, chunks and embeds into vector store
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- User starts a main research session or creates exploration branches without waiting
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for indexing to finish
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- Leader agent receives query and delegates subtasks to researcher, reviewer, writer
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subagents
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- Subagents perform hybrid retrieval and rerank to find relevant chunks with source
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coordinates
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- Agents synthesize answers and return evidence with clickable citations that highlight
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original pages
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- User verifies conclusions by navigating to cited source locations and can save notes
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to research memory or mind map
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outputs:
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- AI-generated answers with traceable evidence (coordinates, page highlights)
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- Research session history with branches
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- Indexed document library for future queries
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- Mind maps or structured notes
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- Persistent run events for resuming sessions
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tags: []
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metadata:
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source_repo: https://github.com/0verL1nk/PaperSage.git
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extracted_at: ''
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confidence: 0.85
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---
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# literature-review-with-traceable-ai-evidence
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Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.
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## Steps
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1. Upload documents to a project (via desktop app or web UI)
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2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
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3. User starts a main research session or creates exploration branches without waiting for indexing to finish
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4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
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5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
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6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
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7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map
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## Inputs
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- Documents in PDF, Office, image, or text formats
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- Research questions or topics of interest
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- Optional: user model configuration via .env or settings
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## Outputs
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- AI-generated answers with traceable evidence (coordinates, page highlights)
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- Research session history with branches
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- Indexed document library for future queries
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- Mind maps or structured notes
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- Persistent run events for resuming sessions
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## Failure Modes
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- Missing local Office/LibreOffice converter causes document conversion failure
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- First-time model download may be slow or require network
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- OCR may have low confidence on poor quality scans
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- Retrieval might miss context if chunking splits semantics
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- Multi-agent coordination could produce conflicting intermediate results
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## Source
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Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
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Confidence: 0.85
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@@ -0,0 +1,6 @@
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# Commands: literature-review-with-traceable-ai-evidence
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## Available Commands
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- `/skill literature-review-with-traceable-ai-evidence` — Load this skill
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- `/run literature-review-with-traceable-ai-evidence` — Execute workflow
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@@ -0,0 +1,10 @@
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# Examples: literature-review-with-traceable-ai-evidence
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## Usage Example
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```python
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# How to use this skill
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# Inputs: Documents in PDF, Office, image, or text formats, Research questions or topics of interest, Optional: user model configuration via .env or settings
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# 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
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# 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
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```
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@@ -0,0 +1,37 @@
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{
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"name": "literature-review-with-traceable-ai-evidence",
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"version": "1.0.0",
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"goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
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"inputs": [
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"Documents in PDF, Office, image, or text formats",
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"Research questions or topics of interest",
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"Optional: user model configuration via .env or settings"
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],
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"steps": [
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"Upload documents to a project (via desktop app or web UI)",
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"System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store",
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"User starts a main research session or creates exploration branches without waiting for indexing to finish",
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"Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents",
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"Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates",
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"Agents synthesize answers and return evidence with clickable citations that highlight original pages",
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"User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map"
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],
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"outputs": [
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"AI-generated answers with traceable evidence (coordinates, page highlights)",
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"Research session history with branches",
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"Indexed document library for future queries",
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"Mind maps or structured notes",
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"Persistent run events for resuming sessions"
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],
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"failure_modes": [
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"Missing local Office/LibreOffice converter causes document conversion failure",
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"First-time model download may be slow or require network",
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"OCR may have low confidence on poor quality scans",
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"Retrieval might miss context if chunking splits semantics",
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"Multi-agent coordination could produce conflicting intermediate results"
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],
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"confidence": 0.85,
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"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.",
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"source_repo": "https://github.com/0verL1nk/PaperSage.git",
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"score": 1.0
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}
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+1
-1
@@ -1,4 +1,4 @@
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# Tests: conditional-request-routing-workflow
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# Tests: literature-review-with-traceable-ai-evidence
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## Test Checklist
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Reference in New Issue
Block a user