Add Skill: agent-builder-workflow

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git
Score: 1.0

skills/agent-builder-workflow/SKILL.md

@@ -0,0 +1,110 @@
+---
+name: agent-builder-workflow
+version: 1.0.0
+description: Build no-code AI agents that connect to enterprise knowledge sources,
+  perform unified search and deep research, and generate explainable answers with
+  citations
+inputs:
+- Task description and agent objectives (e.g., answer Q&A, research specific topics,
+  generate reports)
+- Knowledge sources (documents, databases, enterprise systems) to connect to
+- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
+- Output requirements (citation format, response structure, code execution needs)
+steps:
+- 'Step 1: Define agent task and objectives - Specify what the agent should do (e.g.,
+  answer a question, perform deep research on a topic, generate a report with citations)'
+- 'Step 2: Configure knowledge sources - Connect to enterprise documents, databases,
+  or external systems that will serve as the agent''s context'
+- 'Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines
+  retrieval (graph/vector) and LLM response generation with citation capabilities'
+- 'Step 4: Execute agent - Run the LangGraph chain to process the task and generate
+  responses with grounded citations'
+- 'Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute
+  code, deploy it to a safe sandbox environment for verification'
+outputs:
+- Agent execution logs showing retrieval steps and LLM responses
+- Grounded answers with block citations to source documents
+- Generated reports or artifacts (if code execution was performed)
+- Structured task completion status and results
+tags: []
+metadata:
+  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# agent-builder-workflow
+
+Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi pydantic
+```
+
+**Setup steps:**
+
+1. Install dependencies: pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi
+1. Configure knowledge sources in .env (graph DB connection, vector DB, document paths)
+1. Define agent task in agent_builder.py with objectives and retrieval strategy
+1. Run agent chain: python agent_chain.py --task "research_quantum_computing"
+1. For code execution: add sandbox step to agent_chain.py with code generation and safe execution
+
+## Key Files
+
+- `pipeshub-ai/backend/agent_chain.py - LangGraph chain definition for agent workflows`
+- `pipeshub-ai/backend/retrieval_pipeline.py - Knowledge graph and vector search implementation`
+- `pipeshub-ai/workflows/agent_builder.py - No-code agent creation interface`
+- `pipeshub-ai/workflows/citation_generator.py - Block citation generation from retrieved sources`
+
+## Steps
+
+1. Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)
+2. Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context
+3. Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
+4. Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations
+5. Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification
+
+## Implementation Details
+
+```python
+LangGraph chain with retrieval (graph/vector) and LLM response stages
+```
+
+```python
+Knowledge graph construction from enterprise documents
+```
+
+```python
+Citation formatting using block references to source documents
+```
+
+## Inputs
+
+- Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)
+- Knowledge sources (documents, databases, enterprise systems) to connect to
+- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
+- Output requirements (citation format, response structure, code execution needs)
+
+## Outputs
+
+- Agent execution logs showing retrieval steps and LLM responses
+- Grounded answers with block citations to source documents
+- Generated reports or artifacts (if code execution was performed)
+- Structured task completion status and results
+
+## Failure Modes
+
+- Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality
+- Permission errors when accessing enterprise knowledge sources
+- LLM context window overflow when generating long explanations with citations
+- Sandbox execution failures for code generation or execution tasks
+- Timeout errors during multi-step agent chain execution
+
+## Source
+
+Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
+Confidence: 0.95

skills/agent-builder-workflow/commands.md

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

skills/agent-builder-workflow/examples.md

@@ -0,0 +1,10 @@
+# Examples: agent-builder-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports), Knowledge sources (documents, databases, enterprise systems) to connect to, Retrieval strategy configuration (graph-based knowledge graph vs. vector search), Output requirements (citation format, response structure, code execution needs)
+# Process: Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations) → Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context → Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
+# Outputs: Agent execution logs showing retrieval steps and LLM responses, Grounded answers with block citations to source documents, Generated reports or artifacts (if code execution was performed), Structured task completion status and results
+```

skills/agent-builder-workflow/metadata.json

@@ -0,0 +1,35 @@
+{
+  "name": "agent-builder-workflow",
+  "version": "1.0.0",
+  "goal": "Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations",
+  "inputs": [
+    "Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)",
+    "Knowledge sources (documents, databases, enterprise systems) to connect to",
+    "Retrieval strategy configuration (graph-based knowledge graph vs. vector search)",
+    "Output requirements (citation format, response structure, code execution needs)"
+  ],
+  "steps": [
+    "Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)",
+    "Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context",
+    "Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities",
+    "Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations",
+    "Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification"
+  ],
+  "outputs": [
+    "Agent execution logs showing retrieval steps and LLM responses",
+    "Grounded answers with block citations to source documents",
+    "Generated reports or artifacts (if code execution was performed)",
+    "Structured task completion status and results"
+  ],
+  "failure_modes": [
+    "Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality",
+    "Permission errors when accessing enterprise knowledge sources",
+    "LLM context window overflow when generating long explanations with citations",
+    "Sandbox execution failures for code generation or execution tasks",
+    "Timeout errors during multi-step agent chain execution"
+  ],
+  "confidence": 0.95,
+  "explanation": "PipesHub provides a reusable agent builder workflow that combines LangGraph orchestration with graph-based and vector-based retrieval. This pattern can be adapted to any enterprise context where AI agents need to search across multiple knowledge sources, generate explainable answers with citations, and optionally execute code in a safe sandbox. The workflow is defined by specific configuration files (LangGraph chain definitions) and follows a standard pattern: task definition \u2192 knowledge source connection \u2192 retrieval strategy \u2192 response generation \u2192 optional code sandbox.",
+  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
+  "score": 1.0
+}

skills/agent-builder-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: graph-based-node-orchestration
+# Tests: agent-builder-workflow
 
 ## Test Checklist
 

skills/graph-based-node-orchestration/SKILL.md

@@ -1,108 +0,0 @@
----
-name: graph-based-node-orchestration
-version: 1.0.0
-description: Build a typed node graph that processes data through a sequence of operations
-  and produces a final result
-inputs:
-- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
-- blacknode package with Graph, Node, and cook functionality
-- Python script defining node types with inputs/outputs and connecting them via edges
-steps:
-- Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available
-  via require_nim_api_key()
-- Create a bn.Graph() instance to hold all nodes and their connections
-- Define individual nodes with specific input/output parameters (e.g., Text node with
-  'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-- Connect nodes together using edge definitions (from_port -> to_port) to establish
-  data flow
-- Execute the graph using g.cook() to run the pipeline and process data through the
-  node chain
-- Extract the final result from the output node to complete the workflow
-outputs:
-- A fully constructed graph with typed nodes and defined connections
-- Executed result (e.g., processed text, summary, or other output) from the final
-  node
-- A reusable pattern that can be adapted to different models, hardware, or task types
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# graph-based-node-orchestration
-
-Build a typed node graph that processes data through a sequence of operations and produces a final result
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install blacknode >= 0.3.0 Python >= 3.11 NVIDIA NIM API key (optional but recommended) Anthropic, OpenAI, or other LLM models
-```
-
-**Setup steps:**
-
-1. Clone the repository and install dependencies: pip install -e .
-1. Set NVIDIA_API_KEY or other required API keys in .env
-1. Run the example script: python examples/hello_agent.py
-1. For production, configure hardware pairing and deploy via the blacknode CLI
-
-## Key Files
-
-- `examples/converted_nvidia_nim.py - Full graph with Model, Text, LLMAgent, Output nodes`
-- `examples/hello_agent.py - Minimal agent example connecting Literal → LLMAgent → Print`
-- `blacknode/core - Graph and node implementation (internal)`
-
-## Steps
-
-1. Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()
-2. Create a bn.Graph() instance to hold all nodes and their connections
-3. Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-4. Connect nodes together using edge definitions (from_port -> to_port) to establish data flow
-5. Execute the graph using g.cook() to run the pipeline and process data through the node chain
-6. Extract the final result from the output node to complete the workflow
-
-## Implementation Details
-
-```python
-g = bn.Graph()
-```
-
-```python
-model = g.node('Model', **{'value': 'nim:meta/llama-3.1-8b-instruct'})
-```
-
-```python
-agent = g.node('LLMAgent', **{model})
-```
-
-```python
-result = g.cook(output, 'value')
-```
-
-## Inputs
-
-- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
-- blacknode package with Graph, Node, and cook functionality
-- Python script defining node types with inputs/outputs and connecting them via edges
-
-## Outputs
-
-- A fully constructed graph with typed nodes and defined connections
-- Executed result (e.g., processed text, summary, or other output) from the final node
-- A reusable pattern that can be adapted to different models, hardware, or task types
-
-## Failure Modes
-
-- Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail
-- Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow
-- Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail
-- Graph execution error due to incorrect edge configuration or circular dependencies
-- Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/graph-based-node-orchestration/commands.md

@@ -1,6 +0,0 @@
-# Commands: graph-based-node-orchestration
-
-## Available Commands
-
-- `/skill graph-based-node-orchestration` — Load this skill
-- `/run graph-based-node-orchestration` — Execute workflow

skills/graph-based-node-orchestration/examples.md

@@ -1,10 +0,0 @@
-# Examples: graph-based-node-orchestration
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model, blacknode package with Graph, Node, and cook functionality, Python script defining node types with inputs/outputs and connecting them via edges
-# Process: Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key() → Create a bn.Graph() instance to hold all nodes and their connections → Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
-# Outputs: A fully constructed graph with typed nodes and defined connections, Executed result (e.g., processed text, summary, or other output) from the final node, A reusable pattern that can be adapted to different models, hardware, or task types
-```

skills/graph-based-node-orchestration/metadata.json

@@ -1,34 +0,0 @@
-{
-  "name": "graph-based-node-orchestration",
-  "version": "1.0.0",
-  "goal": "Build a typed node graph that processes data through a sequence of operations and produces a final result",
-  "inputs": [
-    "NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model",
-    "blacknode package with Graph, Node, and cook functionality",
-    "Python script defining node types with inputs/outputs and connecting them via edges"
-  ],
-  "steps": [
-    "Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()",
-    "Create a bn.Graph() instance to hold all nodes and their connections",
-    "Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)",
-    "Connect nodes together using edge definitions (from_port -> to_port) to establish data flow",
-    "Execute the graph using g.cook() to run the pipeline and process data through the node chain",
-    "Extract the final result from the output node to complete the workflow"
-  ],
-  "outputs": [
-    "A fully constructed graph with typed nodes and defined connections",
-    "Executed result (e.g., processed text, summary, or other output) from the final node",
-    "A reusable pattern that can be adapted to different models, hardware, or task types"
-  ],
-  "failure_modes": [
-    "Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail",
-    "Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow",
-    "Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail",
-    "Graph execution error due to incorrect edge configuration or circular dependencies",
-    "Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a declarative graph-based orchestration pattern where nodes are connected via explicit ports and data flows through the graph. The pattern is highly reusable across different domains (robotics, research, data processing) because it separates graph structure from execution logic. The same graph construction and cook pattern can be adapted to different models (NIM, Anthropic, OpenAI), hardware targets (CPU, GPU, Jetson), and task types (LLM reasoning, file I/O, sensor processing).",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}