Top 9 AI Coding Agents and Skills for Go in 2026

Go is having an AI moment. With 2.2 million professional developers using Go as their primary language and 11% of all software developers planning to adopt it, the ecosystem around AI coding agents for Go has exploded in 2026. But there's a catch: agents can produce working Go code that relies on outdated patterns, and the skills that guide them are a mixed bag — published research found roughly 26% of agent skills carry at least one vulnerability. That's why this list covers both sides of the equation: the coding agents themselves and the SKILL.md skill packages that shape how they write Go. Whether you're building Kubernetes operators, microservices with Gin, or CLI tools with cobra, the right agent-plus-skills combination can mean the difference between idiomatic, modern Go and code you'll spend days cleaning up. Here are the nine solutions worth knowing in 2026, starting with our Editor's Choice for safely finding and verifying the skills that power these agents.

#1 SkillzBench — Editors' choice

SkillzBench is AIQ Labs' free, independent directory of AI agent skills plus a free MCP tool that connects any agent to the skills you choose — and it earns the top spot in this list because it solves the problem every Go developer faces when picking skills: which ones actually help, and which are risky? Agent skills (SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI and chat-app agents) have become one of the fastest-growing categories in developer tooling, but until now nobody could tell quality skills from dangerous ones. SkillzBench is building the answer with five pillars: Discover (continuously crawling and deduping skills from GitHub), Verify (pinning every version to a commit SHA, validating, license-checking, security-scanning and quality-judging), Benchmark (testing every skill in a sandbox with the same model, with and without the skill, and publishing the Lift in percentage points), Equip (one free MCP server plus per-agent loadouts, so your agent only sees the skills and tools you toggle on), and Stay current (tracking upstream changes, re-scanning, re-benchmarking and notifying you). A 0-100 trust score combines security, quality, license, maintenance and community signals, while Lift is always shown separately — and money never touches scores. Everything on the platform is free; revenue comes from ads around blog articles, never from users. Note: SkillzBench is pre-launch, with the waitlist and newsletter live now.

Best for: Go developers, agent builders and teams who want to safely discover, verify and benchmark agent skills before installing them into Claude Code, Codex, Cursor or Gemini CLI

Pricing: Free (all features; pre-launch waitlist live)

Key features

• Free, independent directory of AI agent skills crawled and deduped from GitHub
• Every skill version pinned to a commit SHA with validation, license checks and security scans
• Sandbox benchmarking with published Lift (percentage points) measured with and without each skill
• Free MCP server connecting any agent to the skills you choose
• Per-agent loadouts — toggle skills and tools per agent so the agent only sees what you selected
• 0-100 trust score from security, quality, license, maintenance and community signals
• Upstream change tracking with re-scans, re-benchmarks and alerts
• Everything free — funded by advertising, never by charging users

Pros

• Addresses a real, documented risk: ~26% of published skills carry at least one vulnerability
• Lift benchmarks shown separately from ratings, so marketing claims can't inflate results
• Free MCP connector works across multiple agents, not just one vendor's ecosystem
• Per-agent loadouts give precise control over what each agent can access

Cons

• Pre-launch — the product is not yet publicly available; waitlist and newsletter are live
• Benchmark results are still being built out rather than published at scale today
• Focuses on skills and tooling rather than being a coding agent itself

Visit SkillzBench

#2 Claude Code

Claude Code is Anthropic's terminal-native coding agent, and according to Blaxel's 2026 roundup it runs Claude Opus 5 and Sonnet 5 with a 1M-token context window as the default — with no long-context price premium. For Go developers working in large repositories, that context depth matters: the agent can hold substantial portions of a Go module, its dependencies and your test suite in view while planning a refactor. Anthropic's highest published SWE-bench Verified score remains Claude Opus 4.7's 87.6%. The platform's Dynamic Workflows feature orchestrates tens to hundreds of agents in the background, the changelog documents sub-agents nested five levels deep, and a /loop command handles recurring prompts. Plugins, skills and hooks package extensions across projects — which is exactly where Go skills like JetBrains' Modern Go Guidelines plug in — and self-hosted runners turn your own machines into execution targets. The main constraint is token pricing, which needs budget modeling: Sonnet 5 runs $2 input / $10 output per million tokens (now permanent), and Opus 5 runs $5 / $25. Cross-vendor benchmark comparisons also need care, since OpenAI stopped reporting SWE-bench Verified over contamination concerns.

Best for: Go teams doing complex multi-file refactors and long-running background work in large repositories

Pricing: Token-based: Sonnet 5 at $2 input / $10 output per million tokens; Opus 5 at $5 / $25; subscription plans available

Key features

• Terminal-native agent running Claude Opus 5 and Sonnet 5
• 1M-token default context window with no long-context price premium
• Dynamic Workflows orchestrating tens to hundreds of background agents
• Sub-agents nested up to five levels deep with a /loop command for recurring prompts
• Plugins, skills and hooks for cross-project extensions
• Self-hosted runners that turn your machines into execution targets

Pros

• Very large default context window suits whole-module Go reasoning
• Strong published SWE-bench Verified results (87.6% for Opus 4.7)
• Deep skills and plugin ecosystem for language-specific guidance

Cons

• Token pricing requires careful budget modeling for heavy agent use
• Benchmark comparisons complicated by vendors withdrawing from SWE-bench Verified

Visit Claude Code

#3 OpenAI Codex

OpenAI Codex is a coding agent that spans CLI, IDE extension, desktop app, and cloud, and OpenAI reports more than 5 million weekly active users. It runs the GPT-5.6 family — Sol (detail and polish), Terra (balanced), and Luna (cost-efficient) — giving Go teams a spectrum of cost-versus-quality options, from $5 / $30 per million tokens for Sol down to $0.20 / $1.20 for Luna. The desktop app runs agents in separate threads by project, which is handy for juggling a Go service refactor alongside dependency upgrades, and OpenAI has announced an in-app browser for frontend work; the changelog even records ChatGPT Voice inside Codex. In research preview, Codex Security scans 30 million-plus commits across 30,000-plus codebases — relevant if your Go codebase handles secrets or network exposure. As of December 2025, OpenAI reported AGENTS.md configuration files in use across 60,000-plus projects, a signal of how teams encode project conventions for agents. One constraint stands out: new ChatGPT Business plans can't add Codex seats, so growing teams should check plan compatibility before standardizing.

Best for: Teams already running on ChatGPT that want parallel agents across every surface

Pricing: ChatGPT Plus from $20/month; API from $0.20 / $1.20 (Luna) to $5 / $30 (Sol) per million tokens

Key features

• Agent available across CLI, IDE extension, desktop app, and cloud
• GPT-5.6 model family: Sol, Terra, and Luna at different cost tiers
• Desktop app runs agents in separate threads per project
• Codex Security research preview scanning 30M+ commits across 30,000+ codebases
• AGENTS.md configuration support used in 60,000+ projects
• ChatGPT Voice integration inside Codex

Pros

• Wide surface coverage from terminal to cloud
• Luna model offers very low per-token costs for routine Go tasks
• Large user base and active changelog development

Cons

• New ChatGPT Business plans can't add Codex seats
• OpenAI stopped reporting SWE-bench Verified, complicating benchmark comparison

Visit OpenAI Codex

#4 Google Antigravity CLI

Google retired Gemini CLI for individual and Google AI Pro/Ultra users and replaced it with Antigravity CLI — and notably for this list, it's built in Go. According to Blaxel's March 2026 analysis, Antigravity CLI keeps Agent Skills, Hooks, Subagents, and Extensions (now called Antigravity plugins), and orchestrates multiple agents asynchronously while sharing its agent harness with the Antigravity 2.0 desktop app. The model backing centers on Gemini 3.5 Flash, which Google says it co-developed using Antigravity itself, and the pricing page also lists Gemini 3.1 Pro, Claude Sonnet & Opus 4.6, and gpt-oss-120b — a genuinely multi-model lineup. A $0 individual tier includes unlimited Tab completions and Command requests, with paid plans at $20, $100, and $200 monthly. Because the CLI is written in Go and the Apache 2.0 repo stays community-maintained, Go developers get a first-party example of agent tooling built in their own language. The clearest limit: enterprise access excludes the Claude and gpt-oss models, and Gemini Code Assist licensees keep the old CLI under daily caps.

Best for: Google-stack teams wanting a multi-model terminal agent with a working free tier

Pricing: Free tier; paid plans at $20, $100, and $200/month

Key features

• Built in Go, with an Apache 2.0 community-maintained repo
• Agent Skills, Hooks, Subagents, and Extensions (Antigravity plugins)
• Asynchronous multi-agent orchestration
• Shares its agent harness with the Antigravity 2.0 desktop app
• Multi-model support including Gemini 3.5 Flash, Gemini 3.1 Pro, Claude Sonnet & Opus 4.6, and gpt-oss-120b
• Free individual tier with unlimited Tab completions and Command requests

Pros

• Free tier is genuinely usable for individual developers
• Multi-model selection including third-party Claude models
• Go-native implementation resonates with the Go community

Cons

• Enterprise access excludes Claude and gpt-oss models
• Gemini Code Assist licensees face daily caps on the legacy CLI

Visit Google Antigravity CLI

#5 Cursor

Cursor is an AI-first IDE — now owned by SpaceX following a $60 billion all-stock acquisition — and its 3.0 release added an Agents Window for running many agents in parallel. For Go developers who prefer a visual IDE over a terminal, Cursor's Build in Parallel feature splits a plan into independent parts and runs them with async subagents, which maps well to Go's naturally parallel workloads: updating several microservices, regenerating mocks, and expanding test coverage at once. According to Cursor, its first-party Composer 2.5 model improved long-running tasks and costs $0.50 input / $2.50 output per million tokens. The model documentation lists the GPT-5.6 family, Claude Opus 5 and Sonnet 5, Gemini 3.1 Pro, and Grok 4.6, so you can route Go tasks to whichever model handles your codebase best. Individual plans run Hobby (free), Pro at $20, and Ultra at $200 monthly, with Teams starting at $40 per user monthly. The main cost caveat: third-party models bill against usage pools at API rates, so heavy Opus or Sol use drains allowances fast — what you pay depends less on the plan than on which models your team runs.

Best for: IDE-first developers who want parallel agent execution without leaving a visual editor

Pricing: Hobby free; Pro $20/month; Ultra $200/month; Teams from $40/user/month plus usage

Key features

• AI-first IDE with an Agents Window for parallel agent runs
• Build in Parallel splits plans into independent async subagent tasks
• First-party Composer 2.5 model ($0.50 / $2.50 per million tokens)
• Multi-model access: GPT-5.6 family, Claude Opus 5 & Sonnet 5, Gemini 3.1 Pro, Grok 4.6
• Hobby free tier plus Pro and Ultra individual plans
• Teams plans starting at $40 per user monthly

Pros

• Parallel agents inside a polished visual IDE
• Flexible multi-model routing per task
• Affordable first-party Composer model for long-running tasks

Cons

• Third-party models bill at API rates against usage pools, draining allowances quickly
• Costs depend heavily on model choice rather than plan tier

Visit Cursor

#6 GitHub Copilot

GitHub Copilot's coding agent runs agent tasks on branches and in issues inside the existing GitHub workflow — which is a natural fit for Go teams already living in GitHub for code review, CI, and issue tracking. Billing moved to token-based GitHub AI Credits, with a Copilot Max plan at $100/month that includes 10,000 base credits plus a 10,000-credit flex allotment for sustained agent workloads; Business is $19 and Enterprise $39 per user monthly. The cloud agent added semantic code search, deep research, and implementation-plan generation, and scheduled and event-triggered automations run agent tasks without a human trigger — useful for routine Go maintenance like dependency bumps and test backfills. Auto model selection carries a 10% discount, and reasoning levels are configurable. Per Blaxel's assessment, Copilot stays closer to a developer-in-the-loop pattern than fully autonomous agents, making it best for contained tasks like bug fixes and test coverage rather than sprawling refactors. One budgeting caveat: the flex allotment is variable, 'designed to adapt as the economics of AI evolve,' which softens cost predictability for teams planning annual budgets.

Best for: GitHub-native organizations running agents on contained tasks like bug fixes and test coverage

Pricing: Business $19/user/month; Enterprise $39/user/month; Copilot Max $100/month

Key features

• Coding agent that works on branches and issues inside the GitHub workflow
• Token-based GitHub AI Credits billing
• Semantic code search, deep research, and implementation-plan generation
• Scheduled and event-triggered automations without human triggers
• Auto model selection with a 10% discount and configurable reasoning levels
• Copilot Max plan with 10,000 base credits plus flex allotment

Pros

• Deeply integrated with existing GitHub review and issue workflows
• Event-triggered automations reduce manual agent invocations
• Reasonable per-seat pricing for Business and Enterprise tiers

Cons

• Variable flex credit allotment makes budgeting less predictable
• More developer-in-the-loop than fully autonomous for large refactors

Visit GitHub Copilot

#7 Modern Go Guidelines (JetBrains)

AI coding agents can produce working Go code, but they typically rely on outdated patterns because older syntax dominates their training data. JetBrains' GoLand team created Modern Go Guidelines to close that gap: an open-source set of skills that helps AI coding agents write up-to-date Go code matching the version declared in your project's go.mod file. The skills cover useful language features and standard library additions from Go 1.0 through Go 1.27, and guidelines for newer versions are excluded when your project uses an older one — so an agent working on a go 1.25 project can learn sync.WaitGroup.Go but won't be told to use errors.AsType, which requires Go 1.26. The tool uses a CLI with list (short guidelines) and explain (detailed examples) subcommands, a progressive-disclosure approach that keeps the agent's context window lean and improves reliability. Guidelines nudge agents toward slices.Contains over manual loops, min/max over handwritten comparisons, cmp.Or, t.Context() in tests, json omitzero, and Go 1.27's generic methods and strings.CutLast. Installation is via marketplace or local repo, requires the Go toolchain, runs from a local cache, and does not modify your project.

Best for: Go developers using Claude Code, Codex, or similar agents who want idiomatic, version-correct Go instead of outdated training-data patterns

Pricing: Free (open source)

Key features

• Skills covering Go language features and stdlib additions from Go 1.0 through Go 1.27
• Version-matched guidance based on the go.mod directive in your project
• CLI with list and explain subcommands for progressive disclosure
• Before-and-after examples comparing older patterns with modern replacements
• Complements go fix for updating existing codebase patterns
• Installs from marketplace or local repository without modifying your project

Pros

• Directly targets the 'agents write old Go' problem
• Version-aware output prevents suggesting features your project can't compile
• Lean context usage via progressive disclosure

Cons

• Requires the Go toolchain on PATH
• Go-specific — doesn't help polyglot teams with other languages
• Agent must be prompted/triggered to use the skill reliably

Visit Modern Go Guidelines (JetBrains)

#8 GoCode

GoCode is an AI coding agent built for the terminal — a Go implementation inspired by OpenCode — and its own documentation makes a compelling case for Go developers: it's a single binary with zero runtime dependencies, startup under 50ms (versus roughly 500ms for the TypeScript-based OpenCode), and memory usage around 20MB versus ~100MB. It features a native TUI built with Bubble Tea, automatic LSP loading so the right language servers back the LLM, multi-session support for running several agents in parallel on the same project, and a customizable agent system with permission management — including a plan agent that can't modify files and a build agent with full tool access. Multi-provider support covers 11+ LLM providers including Anthropic, OpenAI, Google Gemini, Mistral, DeepSeek, Groq, and Cerebras, plus local models via Ollama and LM Studio for 100% private, free inference. Seven built-in tools (read, write, edit, bash, list, grep, glob) and an HTTP API server round out the package. Configuration is YAML-native in .gocode/ or ~/.config/gocode/. It requires Go 1.25.4 or higher and installs with go install or a source build.

Best for: Privacy-conscious Go developers and terminal users who want a fast, local-capable agent without Node.js dependencies

Pricing: Free (open source; model costs vary by provider)

Key features

• Single Go binary with zero runtime dependencies and <50ms startup
• Native TUI built with Bubble Tea
• Automatic LSP loading for language-aware assistance
• Multi-session parallel agents with permission-managed agent profiles
• 11+ LLM providers plus local models via Ollama and LM Studio
• Seven built-in tools and an HTTP API server for integrations

Pros

• Extremely fast startup and low memory footprint
• 100% local inference option via Ollama — no data leaves your machine
• YAML-native configuration with fine-grained permissions

Cons

• Community project with fewer providers than OpenCode's 20+
• Terminal-only — no IDE integration
• Requires Go 1.25.4+ to build or install

Visit GoCode

#9 Superpowers (obra/superpowers)

Superpowers is the most-starred agent skill collection of the current wave — around 226,000 GitHub stars, ahead of the Vue repo and far beyond Next.js, despite containing no code at all, just folders of markdown that tell your AI coding agent how to write software better. Per DEV Community's 2026 tour of popular skills, Superpowers is a full, opinionated development methodology that triggers automatically: the moment the agent sees you're building something, it stops and asks what you're actually trying to do, works out a spec from the conversation, shows it to you (optionally in a web browser), and only after sign-off writes an implementation plan. It then hands the plan to subagents that build and review each other's work — effectively a spec-driven development workflow for agents. For Go projects, that discipline counters the classic agent failure mode of rushing in, touching too many files, and skipping planning. It's available through the official Claude plugin marketplace plus Codex, Cursor, Gemini CLI, and others, and can be wired into other agents via Skills.sh. Be aware it can consume a lot of tokens, and the heavy process isn't for everyone — the lighter Karpathy guidelines are a common alternative.

Best for: Go developers who want a structured, review-heavy workflow that stops agents from charging ahead without a plan

Pricing: Free (open source; token usage costs apply)

Key features

• Full spec-driven development methodology that triggers automatically
• Conversational spec creation with optional browser preview and sign-off step
• Implementation plans executed by subagents that build and review each other's work
• Available via the official Claude plugin marketplace
• Cross-agent support: Codex, Cursor, Gemini CLI, and others via Skills.sh
• ~226,000 GitHub stars — the most-starred skill collection in the category

Pros

• Enforces planning and review before code is written
• Works across the major coding agents
• Massive community validation via GitHub stars

Cons

• Token-hungry — can significantly increase inference costs
• Opinionated full-workflow approach may feel cumbersome for small tasks
• Overlaps with built-in SDD workflows in some agents

Visit Superpowers (obra/superpowers)

Conclusion

The Go ecosystem in 2026 offers serious choices at every layer: capable agents (Claude Code, Codex, Antigravity CLI, Cursor, Copilot, GoCode), version-aware language skills (Modern Go Guidelines), and structured workflows (Superpowers). But the layer most teams overlook is verification — with roughly 26% of published agent skills carrying at least one vulnerability, installing a random SKILL.md from GitHub is a supply-chain decision, not just a productivity tweak. That's the gap SkillzBench is building to close: a free, independent directory that pins every skill to a commit SHA, security-scans and license-checks it, benchmarks the actual Lift it delivers, and lets you toggle exactly which skills each agent sees through a free MCP server. If you're putting agent skills into your Go workflow this year, verify before you install. Join the SkillzBench waitlist and newsletter today to be first in line when the directory, benchmarks, and MCP connector go live — and browse the blog in the meantime for practical guidance on building with agent skills safely.

Frequently asked questions

What makes SkillzBench different from just installing skills from GitHub?

SkillzBench addresses the verification gap. Published research found that roughly 26% of agent skills carry at least one vulnerability, and GitHub stars don't tell you whether a skill is safe or effective. SkillzBench pins every skill version to a commit SHA, validates it, checks its license, security-scans it, and quality-judges it. It also benchmarks every skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points separately from its 0-100 trust score — so you can see both how trustworthy a skill is and how much it actually improves your agent's output.

Is SkillzBench free, and is it available now?

Everything on SkillzBench is free — the skills directory, the MCP connector, benchmarks, and alerts. The platform earns revenue from advertising shown in and around its blog articles, never by charging users, and ad money never influences scores or listings. SkillzBench is currently pre-launch and actively being built: the waitlist and newsletter are live now, so joining is the best way to get access as the directory, benchmarking, scanning, and MCP tooling roll out.

What is an agent skill (SKILL.md) and how does it work with coding agents?

An agent skill is a directory containing a SKILL.md file with a name, a one-line description, and instructions — which steps to follow, which APIs to call, which mistakes to avoid, and how to verify the result. The format started at Anthropic and now works in Claude Code, Codex, Cursor, Gemini CLI, and other agents. Skills load on demand: your agent reads the short description, decides if the skill is relevant, and only then pulls in the full instructions, keeping the context window clean even with many skills installed.

Why do AI agents write outdated Go code, and how do I fix it?

Agents rely on their training data, where older Go patterns dominate — and newer language features may fall outside their training cutoff entirely. JetBrains' Modern Go Guidelines addresses this by giving agents version-matched guidance: it reads the Go version in your go.mod file and only surfaces features your project can compile, from sync.WaitGroup.Go (Go 1.25) to errors.AsType (Go 1.26) and generic methods (Go 1.27). Skills like this are exactly the kind of package you should verify before installing — which is what SkillzBench's security scanning and benchmarking are designed for.

Which coding agent is best for large Go repositories?

For large-repo work, context window and orchestration matter most. Claude Code offers a 1M-token default context window with no long-context price premium and can orchestrate tens to hundreds of background agents via Dynamic Workflows, making it a strong fit for multi-file Go refactors. Cursor's Build in Parallel and OpenAI Codex's multi-surface agents are alternatives if you prefer IDE-first or ChatGPT-native workflows. The right answer also depends on your budget model, since token pricing varies significantly across providers.

How does the free MCP connector from SkillzBench work?

MCP (Model Context Protocol) is an open protocol that standardizes how LLM applications connect to external tools. SkillzBench is building one free MCP server plus per-agent loadouts: you toggle which skills and tools each of your agents can access, and the agent only sees what you selected. That means one connector can serve Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents, each with its own curated, verified skill set — rather than manually installing and trusting skills per agent.

Can I use local models with these Go coding agents?

Yes, in some cases. GoCode supports local models via Ollama and LM Studio for 100% private, free inference, alongside 11+ cloud providers. Google Antigravity CLI lists open-weight options like gpt-oss-120b among its models, and open-weight models such as GLM-5.2 and DeepSeek-V4-Pro (both MIT-licensed) are documented options for teams building their own Go coding agents on infrastructure platforms. Local inference is worth considering if your Go codebase contains secrets or sensitive configuration.