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necatiincekara
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benchmark-triage

Reconcile experiment metrics and benchmark docs to recommend the next training action.

Updated Jun 17, 2025
Search Tags:#automation#metrics#benchmark#triage#decision-making#experiments#resource-planning
One-click install
npx skills add https://github.com/necatiincekara/Quanvolutional-Neural-Network --skill benchmark-triage-necatiincekara
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-triage
Source: https://github.com/necatiincekara/Quanvolutional-Neural-Network/tree/main/.agents/skills/benchmark-triage
Command: npx skills add https://github.com/necatiincekara/Quanvolutional-Neural-Network --skill benchmark-triage-necatiincekara

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates decision-making for which benchmark or training task to run next, given current artifacts, compute budget, and platform constraints.

Core Features & Use Cases

  • Reconciles metrics from experiments/*.json and benchmark documentation to identify gaps and compute next steps.
  • Estimates resource requirements (time, compute cost) and suggests suitable platforms (Colab, Mac, GPU clusters).
  • Outputs a concrete, prioritized action plan suitable for handoff to benchmark_strategist or automation.

Quick Start

Use this skill to determine the next benchmark given the latest experiment results and resource constraints.

Frequently Asked Questions about benchmark-triage

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

FAQPage Schema
How do I decide which ML benchmark to run next given compute budget constraints?▼

Benchmark triage automates ML experiment decision-making by reading experiment JSON metrics and budget constraints to output a prioritized next benchmark, platform, cost, and impact. It reconciles existing artifacts to identify gaps.

How do I prioritize machine learning experiments across Colab and Mac runtimes?▼

You can prioritize experiments by estimating resource requirements against platform constraints. The skill evaluates existing artifacts to suggest the best runtime, comparing Colab versus Mac environments to output a concrete action plan.

Can I automate training action decisions using experiment JSON files and benchmark summaries?▼

Yes, training action decisions can be automated by reading metrics from experiments/*.json and benchmark summary docs. The skill reconciles these artifacts to identify gaps and output a prioritized action plan for handoff.

What do I need to set up before using a data-driven benchmark prioritization workflow?▼

You need an AGENTS.md file, experiment metrics in experiments/*.json, and benchmark documentation like BENCHMARK_SUMMARY.md. These artifacts provide the current metrics and constraints required to output a recommended next benchmark.

Does benchmark triage work for collaborative ML research workflows and publication strategy?▼

Yes, benchmark triage applies to collaborative ML research workflows and reporting paper impact. It reads publication strategy docs to ensure the recommended next benchmark aligns with your research goals and outputs a concrete action plan.

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