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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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@@ -0,0 +1,6 @@
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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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@@ -0,0 +1,10 @@
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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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@@ -0,0 +1,72 @@
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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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+1
-1
@@ -1,4 +1,4 @@
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# Tests: graph-based-node-workflow
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# Tests: conditional-request-routing-workflow
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## Test Checklist
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@@ -1,100 +0,0 @@
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---
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name: graph-based-node-workflow
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version: 1.0.0
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description: Create and execute typed node graphs for AI/robotics workflows by defining
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nodes with inputs/outputs and connecting them with edges, then cooking the graph
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to run the workflow.
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inputs:
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- bn.Graph() - the graph container for the workflow
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- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified
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inputs and outputs
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- Edge connections mapping from_port to to_port between nodes
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steps:
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- 'Step 1: Initialize a bn.Graph() instance to serve as the workflow container'
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- 'Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal
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for data, LLMAgent for inference, Concat for combining, Output for final results)'
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- 'Step 3: Create edges connecting nodes by specifying source from_port and destination
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to_port for each data flow'
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- 'Step 4: Execute the graph by calling g.cook() to process the defined workflow and
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produce results'
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outputs:
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- Executed workflow results stored in the graph's output nodes
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- Cooked graph ready for inspection, replay, or deployment
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- Potential error states if node dependencies are missing or ports don't match
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tags: []
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metadata:
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source_repo: https://github.com/temiroff/Blacknode.git
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extracted_at: ''
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confidence: 0.95
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---
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# graph-based-node-workflow
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Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.
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## Setup
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**Dependencies:**
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```text
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pip install blacknode (core Python package) anthropic, openai, docker, petgraph (dependencies) Rust extensions in blacknode-core, blacknode-runtime (optional)
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```
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**Setup steps:**
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1. Install blacknode with Python 3.11+ and required dependencies
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1. Clone repository and navigate to project directory
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1. Run examples/converted_text_pipeline.py to see basic graph execution
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1. Modify node definitions and edges to create custom workflows
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## Key Files
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- `examples/converted_text_pipeline.py - basic pipeline pattern`
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- `examples/hello_agent.py - LLM agent workflow pattern`
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- `examples/research_pipeline.py - multi-node research workflow`
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- `blacknode.py - main CLI entry point`
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## Steps
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1. Step 1: Initialize a bn.Graph() instance to serve as the workflow container
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2. Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)
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3. Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
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4. Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results
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## Implementation Details
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```python
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g = bn.Graph()
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```
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```python
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g._edges = [{"from": "model", "from_port": "value", "to": "agent", "to_port": "model"}]
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```
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```python
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result = g.cook(output, "value")
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```
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## Inputs
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- bn.Graph() - the graph container for the workflow
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- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs
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- Edge connections mapping from_port to to_port between nodes
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## Outputs
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- Executed workflow results stored in the graph's output nodes
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- Cooked graph ready for inspection, replay, or deployment
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- Potential error states if node dependencies are missing or ports don't match
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## Failure Modes
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- Missing node dependencies causing undefined variable errors
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- Port mismatch in edge connections leading to no data flow
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- Incomplete graph definition causing cook() to fail
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- Model API key missing or invalid for LLMAgent nodes
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## Source
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Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
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Confidence: 0.95
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@@ -1,6 +0,0 @@
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# Commands: graph-based-node-workflow
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## Available Commands
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- `/skill graph-based-node-workflow` — Load this skill
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- `/run graph-based-node-workflow` — Execute workflow
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@@ -1,10 +0,0 @@
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# Examples: graph-based-node-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: bn.Graph() - the graph container for the workflow, Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs, Edge connections mapping from_port to to_port between nodes
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# Process: Step 1: Initialize a bn.Graph() instance to serve as the workflow container → Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results) → Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
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# Outputs: Executed workflow results stored in the graph's output nodes, Cooked graph ready for inspection, replay, or deployment, Potential error states if node dependencies are missing or ports don't match
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```
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@@ -1,31 +0,0 @@
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{
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"name": "graph-based-node-workflow",
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"version": "1.0.0",
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"goal": "Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.",
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"inputs": [
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"bn.Graph() - the graph container for the workflow",
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"Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs",
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"Edge connections mapping from_port to to_port between nodes"
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],
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"steps": [
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"Step 1: Initialize a bn.Graph() instance to serve as the workflow container",
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"Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)",
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"Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow",
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"Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results"
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],
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"outputs": [
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"Executed workflow results stored in the graph's output nodes",
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"Cooked graph ready for inspection, replay, or deployment",
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"Potential error states if node dependencies are missing or ports don't match"
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],
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"failure_modes": [
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"Missing node dependencies causing undefined variable errors",
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"Port mismatch in edge connections leading to no data flow",
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"Incomplete graph definition causing cook() to fail",
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"Model API key missing or invalid for LLMAgent nodes"
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],
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"confidence": 0.95,
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"explanation": "This workflow pattern is reusable across different AI/robotics applications because it provides a standardized way to compose complex pipelines from typed nodes. The pattern can be adapted to various use cases like research pipelines, agent workflows, or robotics control graphs by simply adding/removing nodes and edges while maintaining the same graph-cooking execution model.",
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"source_repo": "https://github.com/temiroff/Blacknode.git",
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"score": 1.0
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}
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Reference in New Issue
Block a user