api-experiments

Submit API experiment batches from a single process and poll for results.

11|2|Updated May 29, 2025

Search Tags:#api #llm #concurrency #memory #profiling #experiments #batch-api

Ready to run

npx skills add https://github.com/yulonglin/dotfiles --skill api-experiments

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: api-experiments
Source: https://github.com/yulonglin/dotfiles/tree/main/claude/local-marketplace/plugins/research-toolkit/skills/api-experiments
Command: npx skills add https://github.com/yulonglin/dotfiles --skill api-experiments

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamlines memory-aware experimentation for API-heavy LLM evaluations by guiding efficient batch and asynchronous patterns.

Core Features & Use Cases

Batch-centered execution: submit all configurations from a single process, then poll and collect results incrementally.
Real-time async workflows with controlled concurrency to support interactive iterations.
Guardrails and anti-pattern avoidance to prevent memory bloat and process explosions in large-scale runs.
Real-world use cases include large eval campaigns, multi-config sweeps, and reproducible experiment pipelines.

Quick Start

Provide your experiment configurations and run them through a single process that submits all batches, polls for completion, and stores the results incrementally.

Frequently Asked Questions about api-experiments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run large-scale LLM evaluations without causing memory bloat?▼

To prevent memory bloat during large-scale LLM evaluations, submit all configurations from a single process, poll for completion, and retrieve results incrementally rather than spawning parallel processes.

What is the best way to manage concurrency for batch API experiments?▼

The best way to manage concurrency for batch API experiments is to use a single-process submission model that polls the API and collects results incrementally, avoiding the parallel-process anti-pattern.

Why does my LLM experiment pipeline crash during multi-config sweeps?▼

Your LLM experiment pipeline likely crashes during multi-config sweeps because of the parallel-process anti-pattern, which causes memory bloat and process explosions; enforcing single-process submission and polling resolves this.

Can I use asynchronous workflows for iterative LLM API testing?▼

Yes, you can use asynchronous workflows with controlled concurrency for iterative LLM API testing, which supports interactive iterations while maintaining memory efficiency and preventing process explosions.

When do I need to enforce single-process submission for API-heavy experiments?▼

You need to enforce single-process submission for API-heavy experiments when running large eval campaigns or multi-config sweeps, ensuring incremental result retrieval and safeguarding against memory bloat from parallel processes.