Circleci Config Skill for Codex

Optimize CircleCI configuration for speed, reliability, and maintainability. Use when users ask to improve `.circleci/config.yml`, reduce CI runtime, tune caching/workspaces/parallelism, remove pipeline waste, or fix flaky pipeline behavior caused by configuration choices.

5k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

4915

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/openai/plugins --skill circleci-config

What comes with it

15 370 bytes besides the instruction

agents/openai.yaml

references/cache-optimization.md

references/patterns.md

references/persisting-data.md

references/test-results-and-splitting.md

The instruction itself

6 sections, as written by the author

CircleCI Config

Overview

Use this skill to improve CircleCI performance and stability without changing product behavior. Focus on measured bottlenecks first, then implement the smallest safe config changes with clear validation criteria.

Read references/cache-optimization.md when the request involves save_cache, restore_cache, persist_to_workspace, attach_workspace, cache key design, dependency caching, lockfiles, or complaints about low cache hit rates, oversized caches, or wasted persistence steps.

Read references/persisting-data.md when the request involves choosing between caches, workspaces, and artifacts, or when data is being moved between jobs inefficiently.

Read references/test-results-and-splitting.md when the request involves slow test jobs, parallelism, flaky test visibility, missing JUnit XML, or circleci tests run.

Read references/patterns.md when the request involves approvals, branch/tag filters, schedules, deploy flow structure, or environment promotion patterns.

Inputs To Gather

.yaml and .yml files in .circleci/ and any reusable config fragments
Current pain points: duration, flakiness, cost, or maintainability
Baseline metrics: slowest jobs, most frequent retries/failures, queue and run times
Risk tolerance for structural changes

Workflow

Build a baseline.
Identify top 1-3 longest jobs and top flaky jobs.
Capture before metrics (duration, pass rate, retries).
Remove pipeline waste.
Eliminate duplicate jobs/workflows.
Tighten branch/tag filters and workflow triggers.
Improve dependency and artifact flow.
Fix cache keys to include deterministic lockfile checks.
Use workspaces/artifacts to avoid rebuilding identical outputs.
Prefer cache scopes that are narrow, reproducible, and cheap to restore.
Apply safe parallelism.
Parallelize only proven bottlenecks.
Keep fan-out/fan-in readable and deterministic.
Validate impact.
Define expected metric changes and acceptance criteria before finalizing.

Guardrails

Prefer configuration fixes before proposing application code changes.
Do not add blanket retries to hide deterministic failures.
Preserve deployment safety gates while optimizing build/test stages.
Keep changes incremental and easy to revert.
Prefer language-specific cache directories over broad project snapshots.
Avoid cache keys that rotate every run unless the user explicitly wants effectively write-only caches.

Output Contract

Provide:

Baseline bottleneck summary.
Proposed or applied config changes with rationale.
Expected runtime/reliability impact.
Validation plan and rollback note.

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17k tokens

How to use it

Copy the folder

Take openai/circleci-config from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.