micsapp Lindy AI Performance Tuning
Overview
Optimize Lindy AI agent execution speed and reliability. Lindy agents run as multi-step automations where each step (LLM call, tool execution, API call) adds latency.
Prerequisites
- Lindy workspace with active agents
- Access to agent configuration and run history
- Understanding of agent step execution flow
Instructions
Step 1: Identify Slow Steps
Step 2: Consolidate LLM Steps
Step 3: Cache Agent Context Data
Step 4: Parallelize Independent Steps
Step 5: Optimize Trigger Configuration
For detailed implementation code and configurations, load the reference guide:
Read(${CLAUDE_SKILL_DIR}/references/implementation-guide.md)
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Agent timeout (>60s) | Too many sequential steps | Consolidate steps, add parallel execution |
| Step retry loop | Transient API failure | Set max retries to 2, add fallback step |
| Slow LLM step | Prompt too long or complex | Shorten prompt, use focused instructions |
| High run frequency | Trigger firing too often | Add filters to trigger configuration |
Examples
Basic usage: Apply lindy performance tuning to a standard project setup with default configuration options.
Advanced scenario: Customize lindy performance tuning for production environments with multiple constraints and team-specific requirements.
Output
- Configuration files or code changes applied to the project
- Validation report confirming correct implementation
- Summary of changes made and their rationale
See ORM implementation details for output format specifications.
Resources
- Official ORM documentation
- Community best practices and patterns
- Related skills in this plugin pack