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

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

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

@@ -1,108 +0,0 @@
----
-name: graph-based-node-orchestration
-version: 1.0.0
-description: Build a typed node graph that processes data through a sequence of operations
-  and produces a final result
-inputs:
-- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
-- blacknode package with Graph, Node, and cook functionality
-- Python script defining node types with inputs/outputs and connecting them via edges
-steps:
-- Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available
-  via require_nim_api_key()
-- Create a bn.Graph() instance to hold all nodes and their connections
-- Define individual nodes with specific input/output parameters (e.g., Text node with
-  'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-- Connect nodes together using edge definitions (from_port -> to_port) to establish
-  data flow
-- Execute the graph using g.cook() to run the pipeline and process data through the
-  node chain
-- Extract the final result from the output node to complete the workflow
-outputs:
-- A fully constructed graph with typed nodes and defined connections
-- Executed result (e.g., processed text, summary, or other output) from the final
-  node
-- A reusable pattern that can be adapted to different models, hardware, or task types
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# graph-based-node-orchestration
-
-Build a typed node graph that processes data through a sequence of operations and produces a final result
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install blacknode >= 0.3.0 Python >= 3.11 NVIDIA NIM API key (optional but recommended) Anthropic, OpenAI, or other LLM models
-```
-
-**Setup steps:**
-
-1. Clone the repository and install dependencies: pip install -e .
-1. Set NVIDIA_API_KEY or other required API keys in .env
-1. Run the example script: python examples/hello_agent.py
-1. For production, configure hardware pairing and deploy via the blacknode CLI
-
-## Key Files
-
-- `examples/converted_nvidia_nim.py - Full graph with Model, Text, LLMAgent, Output nodes`
-- `examples/hello_agent.py - Minimal agent example connecting Literal → LLMAgent → Print`
-- `blacknode/core - Graph and node implementation (internal)`
-
-## Steps
-
-1. Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()
-2. Create a bn.Graph() instance to hold all nodes and their connections
-3. Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-4. Connect nodes together using edge definitions (from_port -> to_port) to establish data flow
-5. Execute the graph using g.cook() to run the pipeline and process data through the node chain
-6. Extract the final result from the output node to complete the workflow
-
-## Implementation Details
-
-```python
-g = bn.Graph()
-```
-
-```python
-model = g.node('Model', **{'value': 'nim:meta/llama-3.1-8b-instruct'})
-```
-
-```python
-agent = g.node('LLMAgent', **{model})
-```
-
-```python
-result = g.cook(output, 'value')
-```
-
-## Inputs
-
-- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
-- blacknode package with Graph, Node, and cook functionality
-- Python script defining node types with inputs/outputs and connecting them via edges
-
-## Outputs
-
-- A fully constructed graph with typed nodes and defined connections
-- Executed result (e.g., processed text, summary, or other output) from the final node
-- A reusable pattern that can be adapted to different models, hardware, or task types
-
-## Failure Modes
-
-- Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail
-- Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow
-- Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail
-- Graph execution error due to incorrect edge configuration or circular dependencies
-- Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

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

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

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

@@ -1,10 +0,0 @@
-# Examples: graph-based-node-orchestration
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model, blacknode package with Graph, Node, and cook functionality, Python script defining node types with inputs/outputs and connecting them via edges
-# Process: Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key() → Create a bn.Graph() instance to hold all nodes and their connections → Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-# Outputs: A fully constructed graph with typed nodes and defined connections, Executed result (e.g., processed text, summary, or other output) from the final node, A reusable pattern that can be adapted to different models, hardware, or task types
-```

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

@@ -1,34 +0,0 @@
-{
-  "name": "graph-based-node-orchestration",
-  "version": "1.0.0",
-  "goal": "Build a typed node graph that processes data through a sequence of operations and produces a final result",
-  "inputs": [
-    "NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model",
-    "blacknode package with Graph, Node, and cook functionality",
-    "Python script defining node types with inputs/outputs and connecting them via edges"
-  ],
-  "steps": [
-    "Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()",
-    "Create a bn.Graph() instance to hold all nodes and their connections",
-    "Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)",
-    "Connect nodes together using edge definitions (from_port -> to_port) to establish data flow",
-    "Execute the graph using g.cook() to run the pipeline and process data through the node chain",
-    "Extract the final result from the output node to complete the workflow"
-  ],
-  "outputs": [
-    "A fully constructed graph with typed nodes and defined connections",
-    "Executed result (e.g., processed text, summary, or other output) from the final node",
-    "A reusable pattern that can be adapted to different models, hardware, or task types"
-  ],
-  "failure_modes": [
-    "Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail",
-    "Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow",
-    "Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail",
-    "Graph execution error due to incorrect edge configuration or circular dependencies",
-    "Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a declarative graph-based orchestration pattern where nodes are connected via explicit ports and data flows through the graph. The pattern is highly reusable across different domains (robotics, research, data processing) because it separates graph structure from execution logic. The same graph construction and cook pattern can be adapted to different models (NIM, Anthropic, OpenAI), hardware targets (CPU, GPU, Jetson), and task types (LLM reasoning, file I/O, sensor processing).",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}