Act-operator manages and standardizes Act Templates for AI workflows. It benefits operations teams by ensuring consistent AI agent behavior. It connects to LangChain and LangGraph for modular, scalable AI applications.
git clone https://github.com/Proact0/act-operator.gitAct Operator addresses the context gap that affects both human developers and AI agents by providing a structured environment for LangGraph projects. It implements three layers: scaffolding that generates consistent project structure, an executable Single Source of Truth (SSOT) layer with Act Templates and 50+ agent skills, and a feedback loop via CLAUDE.md that preserves architectural decisions across sessions. The tool bundles project conventions, CI workflows, base classes, and test structure into reusable templates, ensuring AI agents and developers operate from the same blueprint. Use cases include conversational agents, business workflow automation, multi-step data pipelines, and document processing flows.
1. **Identify your workflow**: Specify the exact AI workflow you need to standardize (e.g., 'customer support automation' or 'lead qualification'). 2. **Define constraints**: List your specific requirements like response times, tool access, or compliance rules. Be as detailed as possible. 3. **Generate the template**: Use the prompt template above, replacing all [PLACEHOLDERS] with your specific details. Paste into your AI tool. 4. **Validate the output**: Check that the generated template meets all your criteria. Test with sample inputs if possible. 5. **Integrate with LangChain/LangGraph**: Use the provided code snippet to implement the template in your existing workflow. Adjust agent roles as needed for your team structure. Tip: For complex workflows, break the standardization into smaller components (e.g., create separate templates for triage vs. processing).
Conversational agents and chatbots
Business workflow automation and orchestration
Multi-step data pipelines and processing
Document processing flows
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Proact0/act-operatorCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Standardize the AI workflow for [WORKFLOW_NAME] using Act Templates. Ensure all agents in [TEAM_NAME] follow the same behavior patterns for [SPECIFIC_TASK]. Apply the following constraints: [CONSTRAINTS]. Generate a complete Act Template with roles, tools, and response guidelines. Validate the template against [VALIDATION_CRITERIA].
### Standardized Act Template for Customer Support AI Agents
**Workflow Name:** Customer Support Automation
**Team Name:** Global Support Operations
**Task:** Handling refund requests for digital products
**Constraints:**
- Maximum response time: 2 minutes
- Must verify purchase within 30 seconds
- Only authorized agents can process refunds
- Log all interactions in CRM
---
**Agent Roles:**
1. **Initial Triage Agent**
- Tools: Purchase verification API, customer database lookup
- Response Guidelines:
- Greet customer by name within 10 seconds
- Confirm purchase details within 30 seconds
- Route to appropriate agent based on issue type
2. **Refund Processing Agent**
- Tools: Payment processor API, refund eligibility checker
- Response Guidelines:
- Explain refund policy clearly
- Process refund within 60 seconds if eligible
- Escalate to supervisor for complex cases
3. **Follow-up Agent**
- Tools: CRM update, satisfaction survey
- Response Guidelines:
- Send follow-up email within 5 minutes
- Include satisfaction survey link
- Log resolution in CRM
**Validation Criteria:**
- All agents must use consistent terminology
- Response times must meet SLA requirements
- CRM updates must occur in real-time
- Escalation paths must be clearly defined
**LangChain Integration:**
```python
from langchain_core.agents import ActTemplate
refund_act = ActTemplate(
name="Customer Support Refund Processing",
agents=[triage_agent, refund_agent, followup_agent],
tools=[purchase_verifier, refund_processor, crm_updater],
constraints={"max_response_time": 120, "verification_timeout": 30}
)
```
This template ensures all customer support AI agents maintain consistent behavior across regions while meeting performance targets.Simplify sustainable farming
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