optimize-model-compute — for Claude Code optimize-model-compute, dataform, community, for Claude Code, ide skills, Optimize, Compute, BigQuery, Reservations, Purpose

v1.0.0

Sobre este Skill

Perfeito para Agentes de Dados em Nuvem que necessitam de otimização automática de custos do BigQuery e priorização de cargas de trabalho. Resumo localizado: Optimize BigQuery compute costs by assigning Dataform actions to slot reservations. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Recursos

Optimize Model Compute (BigQuery Reservations)
Assigning new models/actions to appropriate compute tiers (reserved vs on-demand)
Rebalancing reservation assignments based on priority changes
Optimizing costs by moving low-priority workloads
Optimize BigQuery compute costs by assigning Dataform actions to slot reservations

# Tópicos principais

HTTPArchive HTTPArchive
[7]
[4]
Atualizado: 3/10/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

Perfeito para Agentes de Dados em Nuvem que necessitam de otimização automática de custos do BigQuery e priorização de cargas de trabalho. Resumo localizado: Optimize BigQuery compute costs by assigning Dataform actions to slot reservations. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Por que usar essa habilidade

Habilita os agentes a atribuir automaticamente ações do Dataform a reservas de slots do BigQuery com base na prioridade e estratégia de otimização de custos, utilizando reservas do BigQuery e preços sob demanda para gerenciamento eficiente de cargas de trabalho.

Melhor para

Perfeito para Agentes de Dados em Nuvem que necessitam de otimização automática de custos do BigQuery e priorização de cargas de trabalho.

Casos de Uso Práticos for optimize-model-compute

Automatizar atribuições de ações do Dataform a reservas de slots do BigQuery
Reequilibrar atribuições de reservas com base em alterações de prioridade
Otimizar custos do BigQuery movendo cargas de trabalho de baixa prioridade para preços sob demanda

! Segurança e Limitações

  • Requer configuração do BigQuery e do Dataform
  • Limitado a reservas de slots do BigQuery e preços sob demanda
  • Dependente da configuração da estratégia de priorização e otimização de custos

About The Source

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Demo Labs

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ e etapas de instalação

These questions and steps mirror the structured data on this page for better search understanding.

? Perguntas frequentes

O que é optimize-model-compute?

Perfeito para Agentes de Dados em Nuvem que necessitam de otimização automática de custos do BigQuery e priorização de cargas de trabalho. Resumo localizado: Optimize BigQuery compute costs by assigning Dataform actions to slot reservations. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Como instalar optimize-model-compute?

Execute o comando: npx killer-skills add HTTPArchive/dataform/optimize-model-compute. Ele funciona com Cursor, Windsurf, VS Code, Claude Code e mais de 19 outros IDEs.

Quais são os casos de uso de optimize-model-compute?

Os principais casos de uso incluem: Automatizar atribuições de ações do Dataform a reservas de slots do BigQuery, Reequilibrar atribuições de reservas com base em alterações de prioridade, Otimizar custos do BigQuery movendo cargas de trabalho de baixa prioridade para preços sob demanda.

Quais IDEs são compatíveis com optimize-model-compute?

Esta skill é compatível com Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use a CLI do Killer-Skills para uma instalação unificada.

optimize-model-compute tem limitações?

Requer configuração do BigQuery e do Dataform. Limitado a reservas de slots do BigQuery e preços sob demanda. Dependente da configuração da estratégia de priorização e otimização de custos.

Como instalar este skill

  1. 1. Abra o terminal

    Abra o terminal ou linha de comando no diretório do projeto.

  2. 2. Execute o comando de instalação

    Execute: npx killer-skills add HTTPArchive/dataform/optimize-model-compute. A CLI detectará sua IDE ou agente automaticamente e configurará a skill.

  3. 3. Comece a usar o skill

    O skill já está ativo. Seu agente de IA pode usar optimize-model-compute imediatamente no projeto atual.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

optimize-model-compute

Resumo localizado: Optimize BigQuery compute costs by assigning Dataform actions to slot reservations. This AI agent skill supports Claude Code, Cursor, and

SKILL.md
Readonly
Upstream Repository Material
The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

Optimize Model Compute (BigQuery Reservations)

Purpose

Automatically assign Dataform actions to BigQuery slot reservations based on priority and cost optimization strategy. Routes high-priority workloads to reserved slots while using on-demand pricing for low-priority tasks.

When to Use

  • Assigning new models/actions to appropriate compute tiers (reserved vs on-demand)
  • Rebalancing reservation assignments based on priority changes
  • Optimizing costs by moving low-priority workloads to on-demand
  • Ensuring critical pipelines get guaranteed compute resources

Configuration File

Reservations are configured in definitions/_reservations.js:

javascript
1const { autoAssignActions } = require("@masthead-data/dataform-package"); 2 3const RESERVATION_CONFIG = [ 4 { 5 tag: "reservation", // Human-readable identifier 6 reservation: "projects/.../reservations/...", // BigQuery reservation path 7 actions: [ 8 // Models assigned to this tier 9 "httparchive.crawl.pages", 10 "httparchive.f1.pages_latest", 11 ], 12 }, 13 { 14 tag: "on_demand", 15 reservation: "none", // On-demand pricing 16 actions: ["httparchive.sample_data.pages_10k"], 17 }, 18]; 19 20autoAssignActions(RESERVATION_CONFIG);

Implementation Steps

Step 1: Source Configuration

TODO: User will provide details on how to determine which models should use reserved vs on-demand compute

Step 2: Update Configuration

  1. Open definitions/_reservations.js
  2. Add or move actions between reservation tiers:
  • Reserved slots (reservation: 'projects/...'): Critical, high-priority, SLA-sensitive workloads
  • On-demand (reservation: 'none'): Low-priority, ad-hoc, or experimental workloads

Step 3: Verify Changes

bash
1# Check syntax 2dataform compile 3 4# Validate no duplicate assignments 5grep -r "\.actions" definitions/_reservations.js

Decision Criteria

FactorReserved SlotsOn-Demand
PriorityHigh, SLA-boundLow, flexible
FrequencyRegular, scheduledAd-hoc, occasional
Cost PatternPredictable usageVariable, sporadic
ImpactCritical pipelinesExperimental, samples

Key Notes

  • Each action should appear in only ONE reservation config
  • File starts with _ to ensure it runs first in Dataform queue
  • Changes take effect on next Dataform workflow run
  • Package automatically handles global assignment (no per-file edits needed)

Package Reference

Using @masthead-data/dataform-package (see package.json)

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