If you are choosing between Claude Code, Codex, and Gemini CLI, start with the job, not the model brand. On Agent Skill Exchange right now, the live catalog shows 2,126 skills across 10 frameworks. Of those, 266 are tagged Claude Code, 91 are tagged Codex, and 107 are tagged Gemini. That split says something useful: Claude Code currently has the deepest marketplace coverage, Codex is strong where bounded coding and automation matter, and Gemini shows up in workflows that benefit from broad tool access and flexible agent loops.

This guide is for developers, engineering leads, and tool builders who want a practical answer to one question: which AI coding agent is best for your actual workflow? We will compare where each tool fits, where each one feels awkward, and how the ASE catalog can help you evaluate real-world skill support before you commit to a stack.

Table of contents

• What the marketplace data says
• Claude Code is best for deep repo work
• Codex is best for bounded terminal automation
• Gemini CLI is best for open agent workflows
• Side by side comparison
• How to choose for your team
• FAQ

What the marketplace data says

Most AI coding agent comparisons stay abstract. They compare model quality, context windows, or pricing pages and stop there. That helps a little, but it misses the operational question: what can your team actually install, reuse, and standardize?

ASE gives a better lens because it reflects live packaging behavior. The marketplace currently lists 2,126 skills, 17 categories, and 10 frameworks. When we filter the live catalog, we see 266 Claude Code skills, 91 Codex skills, and 107 Gemini skills. In plain English, Claude Code has the largest skill footprint of the three, Gemini already has meaningful coverage, and Codex is smaller but more targeted.

Coverage is not everything, but it matters. A bigger skill surface usually means more shared patterns, more reusable workflows, and less time building wrappers from scratch. If your team wants a marketplace-led setup, that ecosystem density is a real advantage.

For related background, see our earlier pieces on skill distribution across OpenClaw, Claude Code, and Codex and building one skill for multiple agents.

Claude Code is best for deep repo work

Anthropic describes Claude Code as an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with development tools across the terminal, IDE, desktop app, and browser. That product shape shows up in the kinds of ASE skills built around it. Claude Code skills tend to emphasize repeatable workflows, review loops, team conventions, and higher-level reasoning over large code surfaces.

This is where Claude Code usually feels strongest:

• Understanding a large unfamiliar repository before making changes.
• Reviewing a spec, then planning, editing, testing, and checking the diff in one long session.
• Using reusable skill packs to encode team process, not just one command.
• Coordinating multi-step work where the tricky part is judgment, not syntax.

A good example from ASE is Run Claude Code with spec-driven quality gates via Pilot Shell. That is not a thin shortcut. It wraps Claude Code in a spec, approval, and verification workflow. Another strong example is SuperClaude, which leans into reusable command and persona patterns.

# Typical Claude Code style task
claude
> Read the repo, compare the auth flow to the product spec,
> patch the edge cases, update tests, and explain the tradeoffs.

If your team values long-horizon reasoning and consistent working style, Claude Code is usually the safest first pick. The tradeoff is that it can feel heavier than a narrower terminal agent when you just want a tightly bounded job done fast.

Codex is best for bounded terminal automation

OpenAI’s Codex CLI documentation describes Codex as a coding agent that runs locally in your terminal, can read and change code in the selected directory, and supports approval modes, local code review, subagents, web search, and Codex Cloud tasks. That wording lines up with how Codex feels in practice: direct, terminal-native, and well suited to clearly scoped work.

Codex tends to fit best when you want:

• A bounded task with crisp inputs and outputs.
• Automation that can live inside CI, scheduled jobs, or PR review flows.
• Explicit approval behavior before commands or edits run.
• A faster handoff from prompt to code change without a lot of ceremony.

The ASE catalog reflects that bias. One representative listing is codex-action, which is built around running bounded Codex jobs inside GitHub Actions for pull request review and repository maintenance. That is a very Codex-shaped use case.

# Typical Codex style task
codex exec "Update the failing Jest snapshots, run tests, and summarize the diff"

# Or use a hosted review flow inside CI with a Codex-oriented skill.

Codex is especially attractive for teams that want terminal ergonomics and repeatable automation without turning every task into a long conversational session. The tradeoff is marketplace breadth. With 91 ASE skills, the Codex catalog is useful, but it is not yet as deep as Claude Code’s.

One more practical note from OpenAI’s docs: Windows support for Codex CLI is still described as experimental, with WSL2 recommended for the best experience. If your org is heavily Windows-native, that matters.

Gemini CLI is best for open agent workflows

Google’s Gemini CLI documentation calls Gemini CLI an open source AI agent that runs in your terminal and uses a ReAct loop with built-in tools plus local or remote MCP servers. That combination is why Gemini often appeals to builders who want openness, terminal control, and broad tool composition.

Gemini CLI usually makes the most sense when you want:

• An open-source terminal agent with visible extension points.
• MCP-centric workflows that combine multiple tools and context sources.
• One assistant that can code, research, fetch, and orchestrate side tasks.
• Closer alignment with Google developer tooling or Gemini Code Assist.

The current ASE catalog shows 107 Gemini skills, which already puts Gemini ahead of Codex on raw count. The listings are also more varied than many teams expect. Examples include Terraform Module Boilerplate Assembler and SerpAPI Answer Box Extractor. That mix hints at Gemini’s broader agent identity: it is not only about code generation, it is often about combining code work with adjacent automation.

# Typical Gemini CLI style task
gemini
> Inspect this Terraform repo, check module drift, search current provider docs,
> and propose the smallest safe patch.

Gemini’s tradeoff is consistency. Some teams love the breadth and open-source feel. Others prefer a narrower tool with a more opinionated workflow. If your work constantly crosses the line between code, docs, search, and tool orchestration, Gemini becomes much more compelling.

Side by side comparison

How to choose for your team

If you are still stuck, use this short rule set:

1. Pick Claude Code if your team wants the most mature ASE skill coverage and spends a lot of time in large codebases, reviews, and multi-step implementation sessions.
2. Pick Codex if your team wants tighter terminal workflows, automation jobs, and explicit execution controls around small to medium coding tasks.
3. Pick Gemini CLI if your team wants an open agent that can combine coding with MCP servers, web workflows, and broader orchestration.

The other smart option is not choosing only one. A lot of high-performing teams standardize on a primary agent, then keep a second one for a narrow class of work. Claude Code for core implementation plus Codex for CI review is a sensible pairing. Claude Code for repo work plus Gemini for research-heavy tasks is another.

If you want to reduce switching costs, start with marketplace evidence. Browse ASE by framework, inspect the live skill pages, and check whether the workflows you actually need already exist.

Frequently asked questions

Which is better, Claude Code or Codex?

Claude Code is usually better for deep reasoning across a larger repository and repeatable skill-driven workflows. Codex is usually better for bounded terminal tasks, CI jobs, and explicit execution control. The better choice depends on whether your bottleneck is reasoning depth or operational speed.

Is Gemini CLI better than Codex for coding?

Not across the board. Gemini CLI is often better when coding is mixed with search, MCP tools, and broader agent orchestration. Codex is often better when the task is narrower and you want a direct terminal-first coding loop.

What does ASE marketplace coverage tell me?

It tells you how much reusable workflow packaging already exists for each framework. Today ASE shows 266 Claude Code skills, 107 Gemini skills, and 91 Codex skills. That is not a quality ranking by itself, but it is a useful adoption and ecosystem signal.

Conclusion

So, which AI coding agent is best? For most teams starting from scratch, Claude Code is the strongest default because it combines capable repo reasoning with the deepest ASE marketplace support. Codex is the better pick when tight terminal automation and bounded execution matter most. Gemini CLI makes the most sense when you want an open, tool-rich agent that spans coding and adjacent workflows.

The point is not to crown one universal winner. It is to match the agent to the work. If you want to evaluate that with real artifacts instead of marketing pages, browse the live ASE catalog and compare the skills each framework already supports.

Next step: open the ASE marketplace, filter by framework, and inspect three real skills before you make a tooling decision. You will learn more from that than from another generic benchmark chart.