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skill CX Codex GP ChatGPT official · OpenAI

circleci-config — Codex skill for CircleCI config

This skill reviews CircleCI configuration and suggests ways to cut pipeline waste, adjust caching, and address slow jobs. Use it when CI runs drag on or pipeline behavior is flaky.

Install into Codex · Skills CLI (Vercel) All methods and platforms ↓
npx skills add openai/plugins --skill config -a codex
Open on GitHub

What circleci-config does

The skill starts by identifying the longest and least stable jobs, then proposes incremental configuration changes. It examines duplicate jobs and workflow triggers, cache keys, data shared between jobs, and parallelism. Before changes are finalized, it calls for expected metric shifts and acceptance criteria.

It can draw on guidance for caching, moving data, test execution, and workflow patterns, depending on the issue. Its output includes bottlenecks, reasons for proposed edits, and a validation and rollback plan. It favors configuration changes over application edits when the config can address the problem. Blanket retries are a bad fix for deterministic failures. Bring the .circleci/ YAML files and baseline details such as job duration, failures, or cost.

What it helps with

  • identify the slowest CircleCI jobs
  • remove duplicate work from workflows
  • set cache keys around lockfiles
  • choose how jobs share generated data
  • parallelize bottlenecks and define validation criteria

Example prompts for the agent

  • Review .circleci/config.yml, identify slow jobs, and propose how to validate the changes.
  • Check the CircleCI cache setup and assess how its keys relate to lockfiles.
  • Analyze flaky test jobs and suggest where parallelism could address a bottleneck.

How to install circleci-config

1Skills CLI (Vercel)

npx skills add openai/plugins --skill config -a codex

2Manually: put the files in place

.agents/skills/config/SKILL.md

For all projects use ~/.agents/skills/

Step by step, with commands for each platform: Codex skills: how to install, where they live, how to call them

Questions about circleci-config

How do I install circleci-config in Codex?

Run npx skills add openai/plugins --skill config -a codex. The skill file is at .agents/skills/config/SKILL.md.

What does circleci-config do?

It reviews CircleCI configuration for bottlenecks and suggests changes to jobs, caching, data flow, or parallelism. It also outlines validation and rollback steps.

What should I provide for a CircleCI review?

Provide the YAML files from .circleci/, describe the pain point, and share baseline metrics such as job duration, retries, or failures.

Source text

SKILL.md · MIT
---
name: circleci-config
description: 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.
---

# 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

1. Build a baseline.
   - Identify top 1-3 longest jobs and top flaky jobs.
   - Capture before metrics (duration, pass rate, retries).
2. Remove pipeline waste.
   - Eliminate duplicate jobs/workflows.
   - Tighten branch/tag filters and workflow triggers.
3. 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.
4. Apply safe parallelism.
   - Parallelize only proven bottlenecks.
   - Keep fan-out/fan-in readable and deterministic.
5. 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:

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

© CircleCI · MIT · openai/plugins

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