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

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+# 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: conditional-review-workflow
+# Tests: agent-builder-workflow
 
 ## Test Checklist
 

skills/conditional-review-workflow/SKILL.md

@@ -1,56 +0,0 @@
----
-name: conditional-review-workflow
-version: 1.0.0
-description: Process a user request through branching logic to approve or reject it,
-  producing a routed decision result
-inputs:
-- 'request: string - The user''s request or input collected at runtime via the input
-  agent'
-steps:
-- 'Start: Input agent collects the user request and stores it in the ''request'' state
-  field, then routes to Classify node'
-- 'Classify: Branching agent evaluates the ''request'' field and routes to Approve
-  node on success or Reject node on failure (on_failure)'
-- 'Approve: Default agent formats an approval message using the request and stores
-  it in the ''result'' output field'
-- 'Reject: Default agent formats a rejection message using the request and stores
-  it in the ''result'' output field'
-outputs:
-- 'result: string - Final message indicating whether the request was approved or rejected,
-  containing the original request'
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-review-workflow
-
-Process a user request through branching logic to approve or reject it, producing a routed decision result
-
-## Steps
-
-1. Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node
-2. Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)
-3. Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-4. Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field
-
-## Inputs
-
-- request: string - The user's request or input collected at runtime via the input agent
-
-## Outputs
-
-- result: string - Final message indicating whether the request was approved or rejected, containing the original request
-
-## Failure Modes
-
-- Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)
-- Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration
-- LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

skills/conditional-review-workflow/commands.md

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

skills/conditional-review-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-review-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request: string - The user's request or input collected at runtime via the input agent
-# Process: Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node → Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure) → Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
-# Outputs: result: string - Final message indicating whether the request was approved or rejected, containing the original request
-```

skills/conditional-review-workflow/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "conditional-review-workflow",
-  "version": "1.0.0",
-  "goal": "Process a user request through branching logic to approve or reject it, producing a routed decision result",
-  "inputs": [
-    "request: string - The user's request or input collected at runtime via the input agent"
-  ],
-  "steps": [
-    "Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node",
-    "Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)",
-    "Approve: Default agent formats an approval message using the request and stores it in the 'result' output field",
-    "Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field"
-  ],
-  "outputs": [
-    "result: string - Final message indicating whether the request was approved or rejected, containing the original request"
-  ],
-  "failure_modes": [
-    "Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)",
-    "Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration",
-    "LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline"
-  ],
-  "confidence": 0.85,
-  "explanation": "Extracted from AgentMap's documented CSV workflow example (ReviewFlow). This is a reusable conditional routing pattern that can be adapted for any approval/rejection, triage, or binary-decision scenario by modifying the branching prompt and agent types. The CSV-based declarative format makes it portable across the AgentMap framework.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}