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

@@ -0,0 +1,6 @@
+# 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: graph-based-node-workflow
+# Tests: conditional-input-routing
 
 ## Test Checklist
 

skills/graph-based-node-workflow/SKILL.md

@@ -1,100 +0,0 @@
----
-name: graph-based-node-workflow
-version: 1.0.0
-description: 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.
-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
-steps:
-- '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'
-- 'Step 4: Execute the graph by calling g.cook() to process the defined workflow and
-  produce results'
-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
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# graph-based-node-workflow
-
-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.
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install blacknode (core Python package) anthropic, openai, docker, petgraph (dependencies) Rust extensions in blacknode-core, blacknode-runtime (optional)
-```
-
-**Setup steps:**
-
-1. Install blacknode with Python 3.11+ and required dependencies
-1. Clone repository and navigate to project directory
-1. Run examples/converted_text_pipeline.py to see basic graph execution
-1. Modify node definitions and edges to create custom workflows
-
-## Key Files
-
-- `examples/converted_text_pipeline.py - basic pipeline pattern`
-- `examples/hello_agent.py - LLM agent workflow pattern`
-- `examples/research_pipeline.py - multi-node research workflow`
-- `blacknode.py - main CLI entry point`
-
-## Steps
-
-1. Step 1: Initialize a bn.Graph() instance to serve as the workflow container
-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)
-3. Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
-4. Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results
-
-## Implementation Details
-
-```python
-g = bn.Graph()
-```
-
-```python
-g._edges = [{"from": "model", "from_port": "value", "to": "agent", "to_port": "model"}]
-```
-
-```python
-result = g.cook(output, "value")
-```
-
-## 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
-
-## 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
-
-## Failure Modes
-
-- Missing node dependencies causing undefined variable errors
-- Port mismatch in edge connections leading to no data flow
-- Incomplete graph definition causing cook() to fail
-- Model API key missing or invalid for LLMAgent nodes
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/graph-based-node-workflow/commands.md

@@ -1,6 +0,0 @@
-# Commands: graph-based-node-workflow
-
-## Available Commands
-
-- `/skill graph-based-node-workflow` — Load this skill
-- `/run graph-based-node-workflow` — Execute workflow

skills/graph-based-node-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: graph-based-node-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# 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
-# 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
-# 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
-```

skills/graph-based-node-workflow/metadata.json

@@ -1,31 +0,0 @@
-{
-  "name": "graph-based-node-workflow",
-  "version": "1.0.0",
-  "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.",
-  "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"
-  ],
-  "steps": [
-    "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",
-    "Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results"
-  ],
-  "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"
-  ],
-  "failure_modes": [
-    "Missing node dependencies causing undefined variable errors",
-    "Port mismatch in edge connections leading to no data flow",
-    "Incomplete graph definition causing cook() to fail",
-    "Model API key missing or invalid for LLMAgent nodes"
-  ],
-  "confidence": 0.95,
-  "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.",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}