AGENT SKILL

github-codespaces-efficiency

Audit and improve GitHub Codespaces efficiency. Use this skill when a user wants faster Codespaces startup, lower Codespaces spend, slim devcontainers, right-size machines, tune idle timeout, or scope prebuilds to branches with sustained usage.

License 许可
MIT
最近更新
2026-11-13
Stars
★ 4.1w
Forks
5.3k

01 前言 Front Matter

name
github-codespaces-efficiency
description
Audit and improve GitHub Codespaces efficiency. Use this skill when a user wants faster Codespaces startup, lower Codespaces spend, slim devcontainers, right-size machines, tune idle timeout, or scope prebuilds to branches with sustained usage.

02 资源文件 3

03 SKILL.md 正文

GitHub Codespaces Efficiency

Use this skill as a lean entrypoint for GitHub Codespaces efficiency work. Inspect the repo, identify waste, and load only needed references.

If no .devcontainer/ exists yet, load references/codespaces.md and define a baseline before proceeding with the steps below.

Use This Skill When

  • The user wants faster Codespaces startup or lower Codespaces spend.
  • The repo has a .devcontainer/ or explicit Codespaces configuration questions.
  • The user asks for devcontainer optimization, machine sizing, prebuild strategy, or idle-timeout guidance.
  • The user is setting up Codespaces for the first time or needs help creating a new .devcontainer/ from scratch.

Load Only What You Need

Core Workflow

1. Measure first

find .devcontainer -maxdepth 2 -type f
gh codespace list
repo=$(gh repo view --json nameWithOwner --jq .nameWithOwner)
gh api "/repos/$repo/codespaces/machines"

If gh auth fails or the user lacks repo admin scope, proceed with static analysis of .devcontainer/ files; mark machine-type and prebuild recommendations as unverified.

Look for: devcontainer image >2 GB or more than 10 features, machine type larger than usage data supports, missing devcontainer-lock.json (recommend adding — many repos predate lock-file support), prebuilds scoped too broadly, and idle timeout mismatched to usage patterns.

2. Apply guardrails

Check each proposed fix against these rules before recommending it:

  1. Does not remove tools the team uses every day — drop any fix that strips required development tools or extensions.
  2. Does not assume smaller is always better — balance machine cost against developer experience and throughput.
  3. Does not turn the devcontainer into a production image — drop any fix that adds production-only dependencies unless the team explicitly requires it.
  4. Incremental changes preferred — a greenfield baseline is appropriate only when no .devcontainer/ exists; flag (do not drop) changes that restructure an existing config.
  5. Repo changes stay separate from org settings — split any fix that mixes repo-editable files with org-level or user-level Codespaces settings into two distinct recommendations.

3. Select the top 3 fixes

From the six candidates below, keep only those supported by audit evidence from step 1 and passing all guardrails from step 2. Rank survivors by estimated monthly cost savings (USD). Select all candidates that meet both criteria, up to a maximum of 3.

  1. Trim devcontainer — remove features, packages, or extensions not needed for everyday development work; target image <2 GB and fewer than 10 features
  2. Right-size machine type — match to observed usage patterns; if data is unavailable, state assumptions explicitly
  3. Scope prebuilds — enable for the default branch, release/* branches active in the last 14 days, and branches with more than 5 Codespaces per week; disable for all others
  4. Tune idle timeout — 30 min default; 15 min if most sessions end before 30 min; 60 min if most sessions run longer
  5. Remove unused extensions or port-forwarding rules
  6. Reduce devcontainer image size and improve layer caching

4. Verify

  • Start a test Codespace to confirm devcontainer changes build and start as expected.
  • Validate machine sizing against observed usage when telemetry is available; otherwise mark as unverified.
  • Treat unexpected build or startup failures as real bugs even when the configuration looks correct.

Required Output

Waste sources: [top cost or startup-time drivers]

Proposed fixes: [top 3 changes supported by audit evidence and passing guardrails]

Validation: [proven live / static-only / remaining risk]

Impact:

  • Startup time: [expected] / [measured if available]
  • Monthly spend: [expected] / [measured if available]
  • Resource utilization: [expected] / [measured if available]

References

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