Add Skill: conditional-request-review-workflow

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

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

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+---
+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

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+# 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

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+# 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

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+{
+  "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

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-# Tests: langgraph-explainable-agent
+# Tests: conditional-request-review-workflow
 
 ## 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
-}