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

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+# 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-workflow
+# Tests: conditional-review-workflow
 
 ## 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
-}