Add Skill: conditional-request-routing

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

skills/conditional-request-routing/SKILL.md

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+---
+name: conditional-request-routing
+version: 1.0.0
+description: Classify an input request and route it to either an approval or rejection
+  path, producing a corresponding result
+inputs:
+- 'request: the input request or content to be reviewed and routed'
+steps:
+- 'Collect input: Use an input agent to capture the user''s request into state field
+  ''request'''
+- 'Branch: Use a branching agent to evaluate ''request'' and route to ''Approve''
+  node on success or ''Reject'' node on failure'
+- 'Approve path: Use a default agent to format and output an approval message with
+  the request'
+- 'Reject path: Use a default agent to format and output a rejection message with
+  the request'
+outputs:
+- 'result: the approval or rejection message containing the original request'
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.85
+---
+
+# conditional-request-routing
+
+Classify an input request and route it to either an approval or rejection path, producing a corresponding result
+
+## Steps
+
+1. Collect input: Use an input agent to capture the user's request into state field 'request'
+2. Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure
+3. Approve path: Use a default agent to format and output an approval message with the request
+4. Reject path: Use a default agent to format and output a rejection message with the request
+
+## Inputs
+
+- request: the input request or content to be reviewed and routed
+
+## Outputs
+
+- result: the approval or rejection message containing the original request
+
+## Failure Modes
+
+- Branching agent cannot evaluate request and neither path is taken
+- Missing or empty input request
+- Prompt misconfiguration causing incorrect routing
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.85

skills/conditional-request-routing/commands.md

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+# Commands: conditional-request-routing
+
+## Available Commands
+
+- `/skill conditional-request-routing` — Load this skill
+- `/run conditional-request-routing` — Execute workflow

skills/conditional-request-routing/examples.md

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+# Examples: conditional-request-routing
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: request: the input request or content to be reviewed and routed
+# Process: Collect input: Use an input agent to capture the user's request into state field 'request' → Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure → Approve path: Use a default agent to format and output an approval message with the request
+# Outputs: result: the approval or rejection message containing the original request
+```

skills/conditional-request-routing/metadata.json

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+{
+  "name": "conditional-request-routing",
+  "version": "1.0.0",
+  "goal": "Classify an input request and route it to either an approval or rejection path, producing a corresponding result",
+  "inputs": [
+    "request: the input request or content to be reviewed and routed"
+  ],
+  "steps": [
+    "Collect input: Use an input agent to capture the user's request into state field 'request'",
+    "Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure",
+    "Approve path: Use a default agent to format and output an approval message with the request",
+    "Reject path: Use a default agent to format and output a rejection message with the request"
+  ],
+  "outputs": [
+    "result: the approval or rejection message containing the original request"
+  ],
+  "failure_modes": [
+    "Branching agent cannot evaluate request and neither path is taken",
+    "Missing or empty input request",
+    "Prompt misconfiguration causing incorrect routing"
+  ],
+  "confidence": 0.85,
+  "explanation": "Extracted from AgentMap's README example 'ReviewFlow' CSV workflow. This is a declarative LangGraph pattern using AgentMap's CSV format that implements conditional branching - a universally reusable pattern for request triage, content moderation, approval gates, or any two-path decision flow. It can be adapted by changing agent types (e.g., using LLM agents instead of default) and prompts.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/conditional-request-routing/tests.md

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-# Tests: langgraph-explainable-agent
+# Tests: conditional-request-routing
 
 ## Test Checklist
 

skills/langgraph-explainable-agent/SKILL.md

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----
-name: langgraph-explainable-agent
-version: 1.0.0
-description: Orchestrate an AI agent workflow that provides explainable answers with
-  citations using knowledge graph retrieval and permission-aware search
-inputs:
-- user_query - text input from the user
-- context_documentation - pre-indexed documents for retrieval
-- knowledge_graph - graph database for entity relationships
-steps:
-- 'Step 1: Create LangGraph chain with agent that processes user query through knowledge
-  graph retrieval and citation generation'
-- 'Step 2: Execute the chain to generate explainable answer with block citations'
-- 'Step 3: Apply permission-aware filtering on retrieved context before final answer'
-outputs:
-- explainable_answer_with_citations - final response with source references
-- actionable_results - structured output for downstream tasks
-tags: []
-metadata:
-  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-explainable-agent
-
-Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph neoelephant pydantic
-```
-
-**Setup steps:**
-
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-
-## Key Files
-
-- `agent.py - main LangGraph chain definition`
-- `workflow_config.yaml - chain configuration`
-
-## Steps
-
-1. Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation
-2. Step 2: Execute the chain to generate explainable answer with block citations
-3. Step 3: Apply permission-aware filtering on retrieved context before final answer
-
-## Implementation Details
-
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-
-## Inputs
-
-- user_query - text input from the user
-- context_documentation - pre-indexed documents for retrieval
-- knowledge_graph - graph database for entity relationships
-
-## Outputs
-
-- explainable_answer_with_citations - final response with source references
-- actionable_results - structured output for downstream tasks
-
-## Failure Modes
-
-- Empty knowledge graph causes missing citations
-- Permission denied on source documents blocks retrieval
-- LangGraph chain execution fails due to missing dependencies
-
-## Source
-
-Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
-Confidence: 0.95

skills/langgraph-explainable-agent/commands.md

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-# Commands: langgraph-explainable-agent
-
-## Available Commands
-
-- `/skill langgraph-explainable-agent` — Load this skill
-- `/run langgraph-explainable-agent` — Execute workflow

skills/langgraph-explainable-agent/examples.md

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-# Examples: langgraph-explainable-agent
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: user_query - text input from the user, context_documentation - pre-indexed documents for retrieval, knowledge_graph - graph database for entity relationships
-# Process: Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation → Step 2: Execute the chain to generate explainable answer with block citations → Step 3: Apply permission-aware filtering on retrieved context before final answer
-# Outputs: explainable_answer_with_citations - final response with source references, actionable_results - structured output for downstream tasks
-```

skills/langgraph-explainable-agent/metadata.json

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-{
-  "name": "langgraph-explainable-agent",
-  "version": "1.0.0",
-  "goal": "Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search",
-  "inputs": [
-    "user_query - text input from the user",
-    "context_documentation - pre-indexed documents for retrieval",
-    "knowledge_graph - graph database for entity relationships"
-  ],
-  "steps": [
-    "Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation",
-    "Step 2: Execute the chain to generate explainable answer with block citations",
-    "Step 3: Apply permission-aware filtering on retrieved context before final answer"
-  ],
-  "outputs": [
-    "explainable_answer_with_citations - final response with source references",
-    "actionable_results - structured output for downstream tasks"
-  ],
-  "failure_modes": [
-    "Empty knowledge graph causes missing citations",
-    "Permission denied on source documents blocks retrieval",
-    "LangGraph chain execution fails due to missing dependencies"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a reusable LangGraph-based pattern for building explainable AI agents that integrate knowledge graph retrieval and citation generation. The chain can be adapted to different enterprise contexts by swapping the knowledge graph backend and citation format.",
-  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
-  "score": 1.0
-}