Add Skill: branching-agent-pattern

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

skills/branching-agent-pattern/SKILL.md

@@ -0,0 +1,101 @@
+---
+name: branching-agent-pattern
+version: 1.0.0
+description: Define and execute AI agent workflows using CSV-based declarative definitions
+  with configurable branching logic
+inputs:
+- 'CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type,
+  next_node, on_failure, prompt, input_fields, output_field'
+- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
+- Storage backend configuration in agentmap_config_storage.yaml
+steps:
+- Define workflow graph in CSV with nodes representing agent steps and their connections
+  (next_node, on_failure)
+- Configure BranchingAgent with customizable success/failure values and fallback fields
+  in the context dictionary
+- Initialize the agent runtime with ensure_initialized() and configure execution tracking
+  and state adapter services
+- Execute the workflow using agentmap run with appropriate inputs and monitor the
+  execution trace
+outputs:
+- Executed workflow with results stored in the specified output_field
+- Detailed execution trace showing success/failure decisions at each branching point
+- Updated workflow state persisted in the configured storage backend
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# branching-agent-pattern
+
+Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml>=6.0.0 fastapi>=0.111.0 uvicorn>=0.34.3
+```
+
+**Setup steps:**
+
+1. Install AgentMap: pip install agentmap[all]
+1. Configure llm providers in agentmap_config.yaml (OpenAI, Anthropic, Google models)
+1. Create CSV workflow files with graph definitions
+1. Initialize runtime with ensure_initialized()
+1. Run workflow with agentmap run <csv_file> --pretty
+
+## Key Files
+
+- `agentmap_config.yaml - Main configuration with LLM and storage settings`
+- `agentmap_config_storage.yaml - Storage backend configuration`
+- `hello_world.csv - Sample workflow demonstrating basic agent chain`
+- `examples/host_integration/custom_agents.py - Custom agent implementations with host service integration`
+
+## Steps
+
+1. Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)
+2. Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary
+3. Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
+4. Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace
+
+## Implementation Details
+
+```python
+CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field
+```
+
+```python
+BranchingAgent context example: {'input_fields': ['success'], 'output_field': 'result', 'success_values': ['PASSED', 'COMPLETED']}
+```
+
+```python
+Execution command: agentmap run hello_world.csv --pretty
+```
+
+## Inputs
+
+- CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field
+- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
+- Storage backend configuration in agentmap_config_storage.yaml
+
+## Outputs
+
+- Executed workflow with results stored in the specified output_field
+- Detailed execution trace showing success/failure decisions at each branching point
+- Updated workflow state persisted in the configured storage backend
+
+## Failure Modes
+
+- Invalid CSV format causing parsing errors during workflow loading
+- Missing or misconfigured LLM provider settings leading to execution failures
+- Storage backend unavailable or misconfigured preventing workflow persistence
+- Agent execution timeout due to long-running operations or infinite loops
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.95

skills/branching-agent-pattern/commands.md

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

skills/branching-agent-pattern/examples.md

@@ -0,0 +1,10 @@
+# Examples: branching-agent-pattern
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field, LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml, Storage backend configuration in agentmap_config_storage.yaml
+# Process: Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure) → Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary → Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
+# Outputs: Executed workflow with results stored in the specified output_field, Detailed execution trace showing success/failure decisions at each branching point, Updated workflow state persisted in the configured storage backend
+```

skills/branching-agent-pattern/metadata.json

@@ -0,0 +1,31 @@
+{
+  "name": "branching-agent-pattern",
+  "version": "1.0.0",
+  "goal": "Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic",
+  "inputs": [
+    "CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field",
+    "LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml",
+    "Storage backend configuration in agentmap_config_storage.yaml"
+  ],
+  "steps": [
+    "Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)",
+    "Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary",
+    "Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services",
+    "Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace"
+  ],
+  "outputs": [
+    "Executed workflow with results stored in the specified output_field",
+    "Detailed execution trace showing success/failure decisions at each branching point",
+    "Updated workflow state persisted in the configured storage backend"
+  ],
+  "failure_modes": [
+    "Invalid CSV format causing parsing errors during workflow loading",
+    "Missing or misconfigured LLM provider settings leading to execution failures",
+    "Storage backend unavailable or misconfigured preventing workflow persistence",
+    "Agent execution timeout due to long-running operations or infinite loops"
+  ],
+  "confidence": 0.95,
+  "explanation": "The BranchingAgent pattern provides a reusable framework for creating conditional AI workflows. The CSV-based workflow definition allows defining complex agent graphs declaratively, while the BranchingAgent handles dynamic branching based on success/failure conditions with customizable value sets. This pattern can be adapted to various use cases including task routing, error handling, and conditional execution paths across different domains.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/branching-agent-pattern/tests.md

@@ -1,4 +1,4 @@
-# Tests: literature-review-with-traceable-ai-evidence
+# Tests: branching-agent-pattern
 
 ## Test Checklist
 

skills/literature-review-with-traceable-ai-evidence/SKILL.md

@@ -1,77 +0,0 @@
----
-name: literature-review-with-traceable-ai-evidence
-version: 1.0.0
-description: Enable researchers to ingest documents, asynchronously index them, and
-  interact with multi-agent AI to answer questions with verifiable citations to original
-  text.
-inputs:
-- Documents in PDF, Office, image, or text formats
-- Research questions or topics of interest
-- 'Optional: user model configuration via .env or settings'
-steps:
-- Upload documents to a project (via desktop app or web UI)
-- System asynchronously converts Office docs to PDF if needed, runs OCR to extract
-  text with coordinates, chunks and embeds into vector store
-- User starts a main research session or creates exploration branches without waiting
-  for indexing to finish
-- Leader agent receives query and delegates subtasks to researcher, reviewer, writer
-  subagents
-- Subagents perform hybrid retrieval and rerank to find relevant chunks with source
-  coordinates
-- Agents synthesize answers and return evidence with clickable citations that highlight
-  original pages
-- User verifies conclusions by navigating to cited source locations and can save notes
-  to research memory or mind map
-outputs:
-- AI-generated answers with traceable evidence (coordinates, page highlights)
-- Research session history with branches
-- Indexed document library for future queries
-- Mind maps or structured notes
-- Persistent run events for resuming sessions
-tags: []
-metadata:
-  source_repo: https://github.com/0verL1nk/PaperSage.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# literature-review-with-traceable-ai-evidence
-
-Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.
-
-## Steps
-
-1. Upload documents to a project (via desktop app or web UI)
-2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
-3. User starts a main research session or creates exploration branches without waiting for indexing to finish
-4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
-5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
-6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
-7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map
-
-## Inputs
-
-- Documents in PDF, Office, image, or text formats
-- Research questions or topics of interest
-- Optional: user model configuration via .env or settings
-
-## Outputs
-
-- AI-generated answers with traceable evidence (coordinates, page highlights)
-- Research session history with branches
-- Indexed document library for future queries
-- Mind maps or structured notes
-- Persistent run events for resuming sessions
-
-## Failure Modes
-
-- Missing local Office/LibreOffice converter causes document conversion failure
-- First-time model download may be slow or require network
-- OCR may have low confidence on poor quality scans
-- Retrieval might miss context if chunking splits semantics
-- Multi-agent coordination could produce conflicting intermediate results
-
-## Source
-
-Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
-Confidence: 0.85

skills/literature-review-with-traceable-ai-evidence/commands.md

@@ -1,6 +0,0 @@
-# Commands: literature-review-with-traceable-ai-evidence
-
-## Available Commands
-
-- `/skill literature-review-with-traceable-ai-evidence` — Load this skill
-- `/run literature-review-with-traceable-ai-evidence` — Execute workflow

skills/literature-review-with-traceable-ai-evidence/examples.md

@@ -1,10 +0,0 @@
-# Examples: literature-review-with-traceable-ai-evidence
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Documents in PDF, Office, image, or text formats, Research questions or topics of interest, Optional: user model configuration via .env or settings
-# Process: Upload documents to a project (via desktop app or web UI) → System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store → User starts a main research session or creates exploration branches without waiting for indexing to finish
-# Outputs: AI-generated answers with traceable evidence (coordinates, page highlights), Research session history with branches, Indexed document library for future queries, Mind maps or structured notes, Persistent run events for resuming sessions
-```

skills/literature-review-with-traceable-ai-evidence/metadata.json

@@ -1,37 +0,0 @@
-{
-  "name": "literature-review-with-traceable-ai-evidence",
-  "version": "1.0.0",
-  "goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
-  "inputs": [
-    "Documents in PDF, Office, image, or text formats",
-    "Research questions or topics of interest",
-    "Optional: user model configuration via .env or settings"
-  ],
-  "steps": [
-    "Upload documents to a project (via desktop app or web UI)",
-    "System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store",
-    "User starts a main research session or creates exploration branches without waiting for indexing to finish",
-    "Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents",
-    "Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates",
-    "Agents synthesize answers and return evidence with clickable citations that highlight original pages",
-    "User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map"
-  ],
-  "outputs": [
-    "AI-generated answers with traceable evidence (coordinates, page highlights)",
-    "Research session history with branches",
-    "Indexed document library for future queries",
-    "Mind maps or structured notes",
-    "Persistent run events for resuming sessions"
-  ],
-  "failure_modes": [
-    "Missing local Office/LibreOffice converter causes document conversion failure",
-    "First-time model download may be slow or require network",
-    "OCR may have low confidence on poor quality scans",
-    "Retrieval might miss context if chunking splits semantics",
-    "Multi-agent coordination could produce conflicting intermediate results"
-  ],
-  "confidence": 0.85,
-  "explanation": "The README describes PaperSage's core workflow: asynchronous document ingestion with OCR/indexing, followed by multi-agent question answering with cited evidence. This process is not tied to the specific codebase and can be reused as a general literature review methodology for any document-centric research using RAG and agent collaboration.",
-  "source_repo": "https://github.com/0verL1nk/PaperSage.git",
-  "score": 1.0
-}