optimize-model-compute — community optimize-model-compute, dataform, community, ide skills

v1.0.0

Über diesen Skill

Perfekt für Cloud-Datenagenten, die eine automatisierte BigQuery-Kostenoptimierung und eine Priorisierung von Arbeitslasten benötigen. Optimize BigQuery compute costs by assigning Dataform actions to slot reservations. USE FOR managing which models use reserved slots vs on-demand pricing, updating reservation assignments, or analyzing cost vs priority tradeoffs for data pipelines.

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

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
48
Canonical Locale
en
Detected Body Locale
en

Perfekt für Cloud-Datenagenten, die eine automatisierte BigQuery-Kostenoptimierung und eine Priorisierung von Arbeitslasten benötigen. Optimize BigQuery compute costs by assigning Dataform actions to slot reservations. USE FOR managing which models use reserved slots vs on-demand pricing, updating reservation assignments, or analyzing cost vs priority tradeoffs for data pipelines.

Warum diese Fähigkeit verwenden

Ermöglicht es Agenten, Dataform-Aktionen automatisch BigQuery-Schlitzzuweisungen basierend auf Priorität und Kostenoptimierungsstrategie zuzuweisen, indem BigQuery-Reservierungen und On-Demand-Preise für eine effiziente Arbeitslastenverwaltung verwendet werden.

Am besten geeignet für

Perfekt für Cloud-Datenagenten, die eine automatisierte BigQuery-Kostenoptimierung und eine Priorisierung von Arbeitslasten benötigen.

Handlungsfähige Anwendungsfälle for optimize-model-compute

Automatisieren der Zuweisung von Dataform-Aktionen zu BigQuery-Schlitzzuweisungen
Neuausgleich der Reservierungszuweisungen basierend auf Prioritätsänderungen
Optimieren der BigQuery-Kosten durch Verschieben von Arbeitslasten mit niedriger Priorität zu On-Demand-Preisen

! Sicherheit & Einschränkungen

  • Erfordert BigQuery- und Dataform-Setup
  • Begrenzt auf BigQuery-Schlitzzuweisungen und On-Demand-Preise
  • Abhängig von der Konfiguration der Kostenoptimierungs- und Prioritätsstrategie

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

FAQ & Installation Steps

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

? Frequently Asked Questions

What is optimize-model-compute?

Perfekt für Cloud-Datenagenten, die eine automatisierte BigQuery-Kostenoptimierung und eine Priorisierung von Arbeitslasten benötigen. Optimize BigQuery compute costs by assigning Dataform actions to slot reservations. USE FOR managing which models use reserved slots vs on-demand pricing, updating reservation assignments, or analyzing cost vs priority tradeoffs for data pipelines.

How do I install optimize-model-compute?

Run the command: npx killer-skills add HTTPArchive/dataform/optimize-model-compute. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for optimize-model-compute?

Key use cases include: Automatisieren der Zuweisung von Dataform-Aktionen zu BigQuery-Schlitzzuweisungen, Neuausgleich der Reservierungszuweisungen basierend auf Prioritätsänderungen, Optimieren der BigQuery-Kosten durch Verschieben von Arbeitslasten mit niedriger Priorität zu On-Demand-Preisen.

Which IDEs are compatible with optimize-model-compute?

This skill is compatible with 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 the Killer-Skills CLI for universal one-command installation.

Are there any limitations for optimize-model-compute?

Erfordert BigQuery- und Dataform-Setup. Begrenzt auf BigQuery-Schlitzzuweisungen und On-Demand-Preise. Abhängig von der Konfiguration der Kostenoptimierungs- und Prioritätsstrategie.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add HTTPArchive/dataform/optimize-model-compute. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use optimize-model-compute immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

optimize-model-compute

Install optimize-model-compute, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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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