Add Skill: conditional-request-review-workflow

Extracted from: https://github.com/jwwelbor/AgentMap.git
Score: 1.0

skills/conditional-request-review-workflow/SKILL.md

@@ -0,0 +1,53 @@
+---
+name: conditional-request-review-workflow
+version: 1.0.0
+description: Classify an input request and route it to an approval or rejection path,
+  producing a final result message.
+inputs:
+- request
+steps:
+- Start node (agent_type=input) collects the user request and stores it in state field
+  'request'.
+- Classify node (agent_type=branching) reads 'request', makes a branching decision,
+  and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'.
+- Approve node (agent_type=default) formats an approval message using 'request' and
+  writes to 'result'.
+- Reject node (agent_type=default) formats a rejection message using 'request' and
+  writes to 'result' (also used if Classify fails).
+outputs:
+- result
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.85
+---
+
+# conditional-request-review-workflow
+
+Classify an input request and route it to an approval or rejection path, producing a final result message.
+
+## Steps
+
+1. Start node (agent_type=input) collects the user request and stores it in state field 'request'.
+2. Classify node (agent_type=branching) reads 'request', makes a branching decision, and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'.
+3. Approve node (agent_type=default) formats an approval message using 'request' and writes to 'result'.
+4. Reject node (agent_type=default) formats a rejection message using 'request' and writes to 'result' (also used if Classify fails).
+
+## Inputs
+
+- request
+
+## Outputs
+
+- result
+
+## Failure Modes
+
+- If branching classification fails, workflow defaults to Reject node via on_failure.
+- Input node has no defined on_failure, so workflow may terminate unexpectedly if input collection fails.
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.85

skills/conditional-request-review-workflow/commands.md

@@ -0,0 +1,6 @@
+# Commands: conditional-request-review-workflow
+
+## Available Commands
+
+- `/skill conditional-request-review-workflow` — Load this skill
+- `/run conditional-request-review-workflow` — Execute workflow

skills/conditional-request-review-workflow/examples.md

@@ -0,0 +1,10 @@
+# Examples: conditional-request-review-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: request
+# Process: Start node (agent_type=input) collects the user request and stores it in state field 'request'. → Classify node (agent_type=branching) reads 'request', makes a branching decision, and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'. → Approve node (agent_type=default) formats an approval message using 'request' and writes to 'result'.
+# Outputs: result
+```

skills/conditional-request-review-workflow/metadata.json

@@ -0,0 +1,25 @@
+{
+  "name": "conditional-request-review-workflow",
+  "version": "1.0.0",
+  "goal": "Classify an input request and route it to an approval or rejection path, producing a final result message.",
+  "inputs": [
+    "request"
+  ],
+  "steps": [
+    "Start node (agent_type=input) collects the user request and stores it in state field 'request'.",
+    "Classify node (agent_type=branching) reads 'request', makes a branching decision, and on success goes to Approve, on failure goes to Reject; stores decision in 'decision'.",
+    "Approve node (agent_type=default) formats an approval message using 'request' and writes to 'result'.",
+    "Reject node (agent_type=default) formats a rejection message using 'request' and writes to 'result' (also used if Classify fails)."
+  ],
+  "outputs": [
+    "result"
+  ],
+  "failure_modes": [
+    "If branching classification fails, workflow defaults to Reject node via on_failure.",
+    "Input node has no defined on_failure, so workflow may terminate unexpectedly if input collection fails."
+  ],
+  "confidence": 0.85,
+  "explanation": "This workflow is directly taken from the AgentMap README 'ReviewFlow' CSV example. It represents a reusable declarative pattern for conditional routing based on input content, adaptable to many binary decision tasks.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/conditional-request-review-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: langgraph-multi-agent-sequential
+# Tests: conditional-request-review-workflow
 
 ## Test Checklist
 

skills/langgraph-multi-agent-sequential/SKILL.md

@@ -1,99 +0,0 @@
----
-name: langgraph-multi-agent-sequential
-version: 1.0.0
-description: Orchestrate a sequence of specialized agents to perform multi-step tasks
-  like research, data processing, and final output generation
-inputs:
-- BedrockModel with temperature=0.3, top_p=0.8
-- Researcher agent with system prompt for destination research (places, history, accommodations,
-  food, web pages)
-- Travel Guide Generator agent with system prompt for structuring travel guides into
-  labeled sections
-- Writer agent with system prompt for formatting professional client responses
-steps:
-- Researcher agent gathers raw destination facts (top 5 attractions, historical facts,
-  best areas, local foods, suggested web pages) using BedrockModel
-- Travel Guide Generator agent structures the raw facts into a comprehensive travel
-  guide with clearly labeled sections
-- Writer agent formats the structured guide into a professional client-facing response
-  with the full guide and highlighted web pages
-outputs:
-- Raw research data (JSON string containing destination facts and categories)
-- Structured travel guide content (markdown with sections for attractions, history,
-  accommodations, cuisine, and web pages)
-- Final client response (formatted travel guide ready for delivery)
-tags: []
-metadata:
-  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-multi-agent-sequential
-
-Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph bedrock-model pydantic
-```
-
-**Setup steps:**
-
-1. Install langchain and langgraph packages
-1. Configure BedrockModel with temperature=0.3 and top_p=0.8
-1. Create three Agent instances with appropriate system prompts and tools
-1. Deploy the FastAPI server with the LangGraph application
-
-## Key Files
-
-- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
-- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/app.py`
-
-## Steps
-
-1. Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel
-2. Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections
-3. Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
-
-## Implementation Details
-
-```python
-Researcher agent with system_prompt for destination research and tools=[calculator, current_time]
-```
-
-```python
-Travel Guide Generator agent with system_prompt requiring structured sections (attractions, history, accommodations, cuisine, web pages)
-```
-
-```python
-Writer agent with system_prompt for client-facing response formatting
-```
-
-## Inputs
-
-- BedrockModel with temperature=0.3, top_p=0.8
-- Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)
-- Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections
-- Writer agent with system prompt for formatting professional client responses
-
-## Outputs
-
-- Raw research data (JSON string containing destination facts and categories)
-- Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)
-- Final client response (formatted travel guide ready for delivery)
-
-## Failure Modes
-
-- Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data
-- Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output
-- Writer agent fails to format the final response correctly, producing garbled or incomplete output
-- Model timeouts or errors in any agent step causing the entire pipeline to fail
-
-## Source
-
-Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
-Confidence: 0.95

skills/langgraph-multi-agent-sequential/commands.md

@@ -1,6 +0,0 @@
-# Commands: langgraph-multi-agent-sequential
-
-## Available Commands
-
-- `/skill langgraph-multi-agent-sequential` — Load this skill
-- `/run langgraph-multi-agent-sequential` — Execute workflow

skills/langgraph-multi-agent-sequential/examples.md

@@ -1,10 +0,0 @@
-# Examples: langgraph-multi-agent-sequential
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: BedrockModel with temperature=0.3, top_p=0.8, Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages), Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections, Writer agent with system prompt for formatting professional client responses
-# Process: Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel → Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections → Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages
-# Outputs: Raw research data (JSON string containing destination facts and categories), Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages), Final client response (formatted travel guide ready for delivery)
-```

skills/langgraph-multi-agent-sequential/metadata.json

@@ -1,31 +0,0 @@
-{
-  "name": "langgraph-multi-agent-sequential",
-  "version": "1.0.0",
-  "goal": "Orchestrate a sequence of specialized agents to perform multi-step tasks like research, data processing, and final output generation",
-  "inputs": [
-    "BedrockModel with temperature=0.3, top_p=0.8",
-    "Researcher agent with system prompt for destination research (places, history, accommodations, food, web pages)",
-    "Travel Guide Generator agent with system prompt for structuring travel guides into labeled sections",
-    "Writer agent with system prompt for formatting professional client responses"
-  ],
-  "steps": [
-    "Researcher agent gathers raw destination facts (top 5 attractions, historical facts, best areas, local foods, suggested web pages) using BedrockModel",
-    "Travel Guide Generator agent structures the raw facts into a comprehensive travel guide with clearly labeled sections",
-    "Writer agent formats the structured guide into a professional client-facing response with the full guide and highlighted web pages"
-  ],
-  "outputs": [
-    "Raw research data (JSON string containing destination facts and categories)",
-    "Structured travel guide content (markdown with sections for attractions, history, accommodations, cuisine, and web pages)",
-    "Final client response (formatted travel guide ready for delivery)"
-  ],
-  "failure_modes": [
-    "Researcher agent fails to retrieve sufficient information, causing downstream agents to receive incomplete or empty data",
-    "Travel Guide Generator fails to structure data properly, resulting in unorganized or malformed output",
-    "Writer agent fails to format the final response correctly, producing garbled or incomplete output",
-    "Model timeouts or errors in any agent step causing the entire pipeline to fail"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a reusable LangGraph pattern where three specialized agents work sequentially: a Researcher agent gathers raw destination facts, a Travel Guide Generator agent structures those facts into a travel guide, and a Writer agent formats the final output for clients. The pattern is modular and can be adapted to other multi-step tasks by swapping agent roles and prompts while maintaining the same pipeline structure.",
-  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
-  "score": 1.0
-}