Top 9 AI Coding Agents and Skills for Kotlin in 2026

Kotlin has firmly established itself as the preferred language for modern Android development and is increasingly used for backend services, multiplatform projects, and cross-platform applications. But in 2026, writing Kotlin is no longer just about picking the right IDE — it's about choosing the right AI coding agent and, increasingly, the right agent skills. According to the 2025 Stack Overflow Developer Survey, 84% of developers now use or plan to use AI tools in their workflow, and the global AI code tools market is forecast to reach $9.46 billion in 2026. For Kotlin developers specifically, the ecosystem has matured quickly: JetBrains maintains an official collection of Kotlin agent skills, the Koog framework makes it possible to build agents in Kotlin itself, and general-purpose agents like Claude Code, Codex, and Cursor all handle Kotlin codebases well. The catch? Agent skills — the SKILL.md instruction packages that teach agents how to work with Kotlin tooling — vary wildly in quality, and published research suggests roughly 26% of skills carry at least one vulnerability. This guide ranks nine of the best options for Kotlin work in 2026, from skill verification platforms to battle-tested coding agents, so you can pick the right mix for your stack.

#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 on this list because it solves the exact problem Kotlin developers face when adopting agent skills: figuring out 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) are everywhere, including JetBrains' own Kotlin agent skills repository, but nobody has been able to tell which ones are trustworthy. Published research found that approximately 26% of skills carry at least one vulnerability, making blind installation a genuine security risk for any codebase. SkillzBench is built around five pillars: Discover (continuously crawling and deduplicating skills from GitHub), Verify (pinning every version to a commit SHA, validating, license-checking, security-scanning, and quality-judging each skill), 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 users toggle skills and tools per agent and the agent only sees what was chosen), and Stay current (tracking upstream changes, re-scanning, re-benchmarking, and notifying users). Every skill carries a trust score from 0–100 based on security, quality, license, maintenance, and community signals, while Lift is always shown separately — and money never touches scores. Everything on SkillzBench is free: the directory, the MCP connector, benchmarks, and alerts. The platform is currently pre-launch and actively being built — the waitlist and newsletter are live — so Kotlin developers can join now to be first in line when the directory and benchmarking tools go public.

Best for: Kotlin developers and agent builders who want to use agent skills like JetBrains' Kotlin collection without installing unvetted, potentially vulnerable packages

Pricing: Free (all features; waitlist currently open)

Key features

• Free, independent directory of AI agent skills crawled and deduplicated from GitHub
• Security scanning, license checking, and validation of every skill — critical given ~26% of skills carry at least one vulnerability
• Every skill version pinned to a commit SHA for reproducibility
• Sandbox benchmarking that measures each skill's Lift in percentage points, with and without the skill
• Trust score 0–100 from security, quality, license, maintenance, and community signals
• One free MCP server with per-agent loadouts — agents only see the skills and tools you toggle on
• Upstream change tracking with re-scans, re-benchmarks, and notifications
• Completely free model, funded by advertising around blog articles — ad money never influences scores

Pros

• Addresses the documented security problem in the agent skills ecosystem (~26% vulnerable)
• Everything is genuinely free — directory, MCP connector, benchmarks, and alerts
• Lift scores are measured in a sandbox and kept separate from ratings, so results stay objective
• Works with Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents via a single MCP server

Cons

• Pre-launch — the product is not yet publicly available, though the waitlist is live
• Benchmark results for specific Kotlin skills are not yet published
• As a directory and verification layer, it does not itself write Kotlin code

Visit SkillzBench

#2 Claude Code

Claude Code is Anthropic's terminal-based coding agent, and in 2026 it consistently ranks at or near the top of independent evaluations for complex, repository-shaped work. Multiple 2026 comparisons describe it as the strongest option for deep reasoning, debugging, and architectural changes — exactly the kind of work Kotlin developers face when untangling coroutine-heavy async code or refactoring large multi-module Gradle projects. With Claude Opus 5.5 as its default model as of September 2026, it offers a 1M-token context window and scored 66 on Artificial Analysis's independent Coding Agent Index, ahead of Codex at 62. Claude Code operates via CLI, connects to GitHub repositories, and supports background agent tasks that complete asynchronously. It treats MCP (Model Context Protocol) as a first-class integration, which matters here: it means Claude Code can consume verified agent skills — including Kotlin-specific skill packages — through tools like SkillzBench's MCP connector. It also supports plugins, skills, and hooks that package extensions across projects, sub-agents nested five levels deep, and self-hosted runners that turn your own machines into execution targets. The main trade-off is cost. Token pricing requires budget modeling, and at $13.00 and roughly an hour per task on Opus 5.5 (per Artificial Analysis's measurements), it is the most expensive of the leading agents per task. Developers who need deep, careful reasoning on long-horizon Kotlin refactors generally find it worth it; those running high volumes of routine tasks may prefer cheaper options.

Best for: Kotlin teams tackling complex multi-file refactors, coroutine and concurrency debugging, and long-horizon work in large repositories

Pricing: Usage-based via Anthropic API; Claude Pro and Team plans available. Opus 5.5 runs $4/$20 per 1M tokens; Sonnet 5 is $2/$10

Key features

• Terminal-based agent with 1M-token context window by default on current models
• Claude Opus 5.5 default model, scoring 66 on the independent Coding Agent Index
• First-class MCP support for connecting external tools and verified skills
• Background agent tasks that complete asynchronously
• Plugins, skills, and hooks for packaging extensions across projects
• Sub-agents nested up to five levels deep with per-subagent model and effort control
• Self-hosted runners for using your own machines as execution targets
• Enterprise features including SSO and audit logging

Pros

• Widely regarded as the strongest reasoning agent for hard debugging and architectural work
• MCP-first design makes it easy to plug in verified Kotlin agent skills
• 1M-token context handles large Kotlin/Gradle monorepos
• Enterprise-grade security posture including sandboxed execution options

Cons

• Most expensive per-task among leading agents (~$13.00 per task on Opus 5.5 per independent measurements)
• Terminal-first workflow may not suit developers who prefer a visual IDE
• No air-gapped deployment option

Visit Claude Code

#3 OpenAI Codex

OpenAI Codex is a coding agent spanning a desktop app, cloud delegation, an open-source CLI, IDE extensions, and connected ChatGPT workflows — the broadest deployment surface of any agent in 2026. It was named a Leader in the Gartner Magic Quadrant for Enterprise AI Coding Agents in April 2026, with Gartner highlighting its enterprise controls: approval gates, RBAC, customizable policies, OS-level sandboxing, and auditable workspace governance. For Kotlin teams in regulated industries, those governance features are a significant differentiator. Codex runs the GPT-6 model family: Astra (the default, $10/$50 per 1M tokens), Sol ($2/$10, described as detail-and-polish oriented), and Luna ($0.10/$0.50, cost-efficient). Independent measurements show Codex is the fastest and cheapest of the leading agents per task — $2.99 and 22.3 minutes per task with Sol, versus $13.00 and over an hour for Claude Code with Opus 5.5. Developers in 2026 forums describe Codex as more deterministic on multi-step tasks: understanding repo structure, making coordinated changes, running tests, and iterating without drifting — useful behavior when an agent is modifying Kotlin code where type-safety errors compound. Codex also supports plugin marketplaces, and JetBrains' official Kotlin agent skills repository documents an installation path specifically for Codex, making it one of the easiest general agents to equip with Kotlin-specific skills. The main criticisms are that it lacks the default-IDE mindshare of Cursor or Copilot, and that long-running agent costs can feel opaque.

Best for: Enterprise Kotlin teams that need broad deployment options, strong governance controls, and low cost per task

Pricing: Usage-based on API token consumption (Astra $10/$50, Sol $2/$10, Luna $0.10/$0.50 per 1M tokens); ChatGPT Plus from $20/month; enterprise pricing via sales

Key features

• Available across desktop app, cloud, open-source CLI, IDE extensions, and ChatGPT workflows
• Gartner Magic Quadrant Leader (April 2026) with approval gates, RBAC, and OS-level sandboxing
• GPT-6 model family: Astra (default), Sol, and Luna at different price/performance points
• Fastest per-task completion among leaders: 22.3 minutes and $2.99 per task with Sol
• Plugin marketplace with documented support for JetBrains' Kotlin agent skills
• Parallel agents across multiple surfaces
• More than 5 million weekly active users reported by OpenAI

Pros

• Best cost-per-task and speed among the leading agents
• Strong enterprise governance: RBAC, approval gates, auditable workspaces
• Official installation path for JetBrains' Kotlin agent skills
• Model tiering lets teams match cost to task complexity

Cons

• Less default-IDE mindshare than Cursor or Copilot
• Long-running agent costs can feel opaque
• Some new ChatGPT Business plans cannot add Codex seats

Visit OpenAI Codex

#4 Cursor

Cursor is the market-leading AI-native IDE, reaching $2 billion ARR in February 2026 and subsequently acquired by SpaceX in an all-stock deal completed in August 2026. For Kotlin developers who prefer a visual editor over terminal-based agents, Cursor remains the baseline against which other tools are measured — 2026 developer forums consistently treat it as the default AI IDE even when people prefer other agents for specific tasks. Cursor's core strength is flow: fast autocomplete, chat inside the editor, and smooth handling of small-to-medium scoped tasks like feature tweaks, refactors, tests, and bug fixes — the daily grind of Android and Ktor development. Version 3.0 added an Agents Window for running many agents in parallel, and its Build in Parallel feature splits a plan into independent parts and runs them with async subagents. Cloud Agents run in isolated VMs, and the Projects coordinator agent plans work and delegates it to other agents. Cursor's model documentation lists the GPT-6 family, Claude Opus 5 and Sonnet 5, Gemini 3.1 Pro, and Grok 4.6 (now Cursor's first-party model), giving Kotlin teams unusual flexibility in choosing which model backs their work. Where Cursor draws criticism is on larger, more complex changes: 2026 threads still report issues with long-running refactors, looping behavior, and incomplete repo-wide understanding. Pricing changes have also been a top community concern, and third-party models bill against usage pools at API rates, so heavy use of premium models drains allowances quickly.

Best for: Kotlin developers who want a polished visual IDE with parallel agent execution and flexible model choice

Pricing: Pro from $20/month; Ultra at $200/month; Teams from $40 per user/month; enterprise pricing via sales

Key features

• AI-native IDE with fast tab completion and in-editor chat
• Cloud Agents running in isolated VMs with parallel sub-agents
• Build in Parallel: splits plans into independent parts run by async subagents
• Projects coordinator agent that plans and delegates work
• Multiple model options including GPT-6 family, Claude, Gemini 3.1 Pro, and first-party Grok 4.6
• Enterprise audit features and Cursor Blame distinguishing AI from human edits
• $2B ARR as of February 2026, making it the most widely adopted AI IDE

Pros

• Best-in-class everyday editing flow for small and medium Kotlin tasks
• Parallel agent execution without leaving a visual IDE
• Wide model selection including first-party Grok 4.6
• Enterprise audit features like Cursor Blame

Cons

• Reported struggles with long-running, repo-wide Kotlin refactors
• Third-party models bill at API rates against usage pools, making costs unpredictable
• Pricing changes have been a recurring community complaint

Visit Cursor

#5 GitHub Copilot

GitHub Copilot is the most mature option in the coding agent category, and for Kotlin teams already on GitHub Enterprise, it is the lowest-friction adoption path available in 2026. Copilot's agent mode brings real multi-file editing and cloud tasks into the product: the coding agent runs agent tasks on branches and in issues inside the existing GitHub workflow, and Copilot Workspace works directly from GitHub issues and pull requests. For Android teams whose Kotlin code already lives in GitHub, that integration means agents can pick up issues, open pull requests, and participate in review without any new tooling. In 2026, Copilot shipped every frontier model as generally available — Claude Opus 5, Kimi K3, Grok 4.6, Claude Fable 5.1, and GPT-6 Astra — so teams are not locked into a single model family. The cloud agent added semantic code search, deep research, and implementation-plan generation, and scheduled and event-triggered automations can run agent tasks without a human trigger. Billing moved to token-based GitHub AI Credits, with a Copilot Max plan at $100/month including 10,000 base credits plus a 10,000-credit flex allotment. Copilot's weaknesses are honest ones: power users describe it as less impressive than Claude Code on complex reasoning, quotas and model choices can feel opaque, and the flex credit allotment is explicitly variable — GitHub describes it as designed to adapt as the economics of AI evolve — which softens budget predictability. Still, for pragmatic teams, it remains the easiest agent to get approved and deployed.

Best for: GitHub-native organizations running Kotlin projects who want agent tasks inside their existing issues-and-PR workflow

Pricing: AI Credits model as of June 1, 2026; Business $19 and Enterprise $39 per user/month; Copilot Max $100/month

Key features

• Agent mode with multi-file editing and cloud tasks inside the GitHub workflow
• Copilot Workspace working directly from GitHub issues and pull requests
• Every frontier model generally available, including Claude, GPT-6 Astra, and Grok 4.6
• Semantic code search, deep research, and implementation-plan generation in the cloud agent
• Scheduled and event-triggered automations for hands-off agent runs
• Deepest GitHub ecosystem integration with SSO and audit logs
• Configurable reasoning levels and auto model selection

Pros

• Lowest-friction adoption for teams already on GitHub Enterprise
• Broadest model choice of any agent, all generally available
• Enterprise compliance, SSO, and audit logging built in
• Agents operate directly on issues and PRs, fitting existing review processes

Cons

• Less impressive on complex reasoning compared to Claude Code per developer reports
• Variable flex credit allotment reduces budget predictability
• Quotas and model selection can feel opaque to power users

Visit GitHub Copilot

#6 Koog (JetBrains)

Koog deserves a place on this list for a different reason than the others: it is not an agent you use to write Kotlin — it is the open-source framework from JetBrains for building AI agents in Kotlin itself. For Kotlin developers who want to understand how coding agents actually work, or who need to build custom agents for their own workflows, Koog handles the execution loop: sending prompts, parsing tool calls, and executing them, repeating until done. JetBrains' own blog series demonstrates building a minimal coding agent with Koog using just three tools — list directory, read file, and edit file — connected to a frontier model. Koog's feature set is production-oriented. It supports multiplatform development across the JVM, JavaScript, WebAssembly, Android, and iOS; built-in retries for fault tolerance; agent persistence that restores full state machines rather than just chat messages; and history compression for long-running conversations. It integrates with Spring Boot and Ktor — the frameworks most Kotlin backend teams already use — and offers observability through OpenTelemetry exporters with ready integrations for W&B Weave and Langfuse. Notably, Koog includes Model Context Protocol (MCP) integration, meaning agents built with Koog can consume MCP tools, including verified skill servers. The design lessons embedded in Koog are directly relevant to anyone using coding agents on Kotlin projects: its file tools report sizes and line counts to prevent context exhaustion, and its error messages are crafted to keep agents from going off the rails after failed edits. If you want to build rather than buy, Koog is the most idiomatic option in the Kotlin ecosystem.

Best for: Kotlin developers and teams building custom AI agents, especially on Spring Boot or Ktor backends

Pricing: Free and open source (model API costs apply separately)

Key features

• Open-source JetBrains framework for building AI agents in Kotlin
• Multiplatform support: JVM, JavaScript, WebAssembly, Android, and iOS
• Built-in retries, agent persistence, and full state-machine restoration
• History compression strategies for long-running conversations
• Spring Boot and Ktor integrations with an idiomatic, type-safe DSL
• Model Context Protocol (MCP) integration for consuming MCP tools
• Observability via OpenTelemetry exporters (W&B Weave, Langfuse)
• LLM switching and rerouting between OpenAI, Anthropic, Google, and local models via Ollama

Pros

• The most idiomatic agent framework for Kotlin, from Kotlin's creator
• Production-ready features: persistence, retries, observability, history compression
• MCP integration connects your agents to the broader tool ecosystem
• Multiplatform support covers JVM through iOS

Cons

• It is a framework for building agents, not a ready-to-use coding assistant
• Requires development effort to build and maintain your own agent
• Model API costs are separate and can add up

Visit Koog (JetBrains)

#7 Kotlin Agent Skills by JetBrains (Kotlin/kotlin-agent-skills)

JetBrains maintains an official GitHub repository of AI agent skills specifically for projects using the Kotlin language — and it is the most important skill collection for Kotlin developers to know about in 2026. Skills follow the Agent Skills standard (agentskills.io): self-contained folders that package instructions, scripts, and resources for an AI agent to use on a specific use case, each including a SKILL.md file with YAML frontmatter followed by the guidance the agent follows while the skill is active. The repository organizes skills with a clear naming convention (kotlin-<category>-<functional-name>) across categories such as backend — covering correct use of Spring, JPA, and other backend frameworks with Kotlin — and tooling, which includes build tooling skills like the AGP9 migration skill. Installation is well documented for multiple agents: an npx skills CLI command, Claude Code plugin marketplace commands, a Codex plugin marketplace flow, and manual copying into an agent's skills directory. This collection is exactly the kind of resource that benefits from careful vetting. Because skills are community-contributed instruction packages that agents follow, quality and safety vary — the broader ecosystem research showing roughly 26% of skills carry at least one vulnerability applies to instruction packages generally. Pairing this official collection with a verification layer like SkillzBench's security scanning and benchmarking is a sensible workflow for 2026: use JetBrains' skills for authoritative Kotlin guidance, but verify versions, licenses, and security before installing them into production agents.

Best for: Kotlin developers using Claude Code, Codex, or other Agent Skills-compatible agents who want authoritative, Kotlin-specific guidance

Pricing: Free and open source

Key features

• Official JetBrains collection of AI agent skills for Kotlin projects
• Follows the Agent Skills standard (SKILL.md files with YAML frontmatter)
• Backend category covering Spring, JPA, and other Kotlin backend frameworks
• Tooling category including build tooling skills such as AGP9 migration
• Consistent naming convention (kotlin-<category>-<functional-name>)
• Installation paths for Claude Code, Codex, the npx skills CLI, and manual setup
• Open for community contributions via GitHub issues and pull requests

Pros

• Authoritative source maintained by JetBrains, Kotlin's creator
• Practical, targeted skills for real Kotlin pain points like framework usage and build migrations
• Well-documented installation across multiple popular agents
• Free and open source

Cons

• Skill coverage is still growing; categories are limited to backend and tooling so far
• Skills are instruction packages, not verified software — vet before production use
• Requires manual installation and management per agent

Visit Kotlin Agent Skills by JetBrains (Kotlin/kotlin-agent-skills)

#8 Google Antigravity CLI (successor to Gemini CLI)

Google retired the Gemini CLI for individual and Google AI Pro/Ultra users and replaced it with Antigravity CLI — a multi-model terminal agent built in Go that shares its agent harness with the Antigravity 2.0 desktop app. For Kotlin developers, the headline feature is the working free tier: a $0 individual plan includes unlimited tab completions and command requests, with paid plans at $20, $100, and $200 monthly. Independent coverage highlights free access at 60 requests per minute and 1,000 per day with 1M-token context — the most generous free offering among major coding agents. Antigravity CLI keeps the capabilities that made Gemini CLI popular — Agent Skills, Hooks, and Subagents — with extensions now called Antigravity plugins. Support for Agent Skills is notable for this list: it means Kotlin skill packages following the Agent Skills standard can be used with Antigravity, alongside verification and loadout tools like SkillzBench's MCP connector. The CLI orchestrates multiple agents asynchronously, and model backing centers on Gemini 3.5 Flash, which Google co-developed using Antigravity; the pricing page also lists Gemini 3.1 Pro, Claude Sonnet and Opus 4.6, and gpt-oss-120b. The clearest limitation is model availability by tier: enterprise access excludes the Claude and gpt-oss models, and Gemini Code Assist licensees keep the old CLI under daily caps. The Apache 2.0 repository remains community-maintained. For Google-stack Kotlin teams — especially those using Firebase, Android Studio, and Google Cloud — Antigravity CLI is the natural terminal agent, and its free tier makes it an easy first step into agentic Kotlin development.

Best for: Google-stack Kotlin teams and developers who want a capable terminal agent with a genuinely usable free tier

Pricing: Free individual tier; paid plans at $20, $100, and $200 monthly

Key features

• Multi-model terminal agent built in Go, successor to Gemini CLI
• Free individual tier with unlimited tab completions and command requests
• Agent Skills, Hooks, and Subagents support (extensions now called Antigravity plugins)
• Asynchronous orchestration of multiple agents
• Model backing centered on Gemini 3.5 Flash, plus Gemini 3.1 Pro, Claude, and gpt-oss options
• Shared agent harness with the Antigravity 2.0 desktop app
• 1M-token context with 60 requests/minute and 1,000/day on the free tier

Pros

• Most generous free tier among major coding agents
• Multi-model support with asynchronous agent orchestration
• Agent Skills support aligns with the Kotlin skills ecosystem
• Shares a harness with the Antigravity 2.0 desktop app

Cons

• Enterprise access excludes Claude and gpt-oss models
• Gemini Code Assist licensees are limited to the old CLI under daily caps
• Successor transition may cause workflow disruption for existing Gemini CLI users

Visit Google Antigravity CLI (successor to Gemini CLI)

#9 Taskade AI Kotlin Coding Agent

Taskade offers a purpose-built AI Kotlin Coding Agent aimed at a different audience than the developer-first tools above: individuals and small teams who want Kotlin coding help embedded in a broader project workspace. According to Taskade's website, the agent can write Kotlin code snippets for specific tasks or features, find bugs and suggest corrective actions, offer code optimization insights, answer questions about Kotlin syntax and best practices, and automate repetitive coding tasks — essentially serving as an assistive partner for developers at the earlier stages of Kotlin work. Where Taskade differentiates itself is customization and context. Its agents can be tailored to follow specific coding styles and preferences, and can analyze documents containing project specifications — the site describes a bot that reads a project's specifications from a PDF and then writes code accordingly. That makes it useful for non-developers or mixed teams working on Kotlin-adjacent projects who want coding assistance inside the same workspace where they manage tasks, notes, and collaboration. Usage is straightforward: users click 'Use Agent' to add it to a workspace, customize it from the Agents tab, invoke it in projects with slash commands, and chat with it from the Agent Sidebar. It is not a substitute for a full coding agent wired into your repository — it does not offer the terminal-based, repo-scale autonomous workflows of Claude Code or Codex — but for quick Kotlin snippets, syntax questions, and spec-driven code generation in a collaborative workspace, it fills a real niche at a consumer-friendly price point.

Best for: Solo developers, students, and small teams who want Kotlin coding help inside a project-management workspace rather than a full IDE or terminal setup

Pricing: Included with Taskade plans; see Taskade's pricing page for current tiers

Key features

• Dedicated AI agent for Kotlin code generation and assistance
• Writes Kotlin code snippets for specific tasks or features
• Bug identification with suggested corrective actions
• Code optimization insights for performance and efficiency
• Answers Kotlin syntax and best-practice questions
• Customizable to follow specific coding styles and constraints
• Can analyze documents such as PDFs containing project specifications
• Integrates into Taskade workspaces with slash commands and an Agent Sidebar

Pros

• Easy setup: add the agent to a workspace and start using it immediately
• Document-aware customization lets it code from written specifications
• Combines coding assistance with task management and collaboration
• Accessible to non-expert users getting started with Kotlin

Cons

• Not a repository-scale autonomous agent like Claude Code or Codex
• Limited visibility into deep codebase context compared to IDE-integrated tools
• Best suited to snippets and smaller tasks rather than large Kotlin refactors

Visit Taskade AI Kotlin Coding Agent

Conclusion

Kotlin development in 2026 is agentic, but the smartest teams treat agents and skills as two separate decisions. On the agent side, Claude Code leads on reasoning depth, Codex on cost-per-task and governance, Cursor on everyday IDE flow, and Copilot on GitHub-native convenience — while Koog and JetBrains' official Kotlin agent skills repository show how deeply the Kotlin ecosystem has embraced agentic tooling. On the skills side, the picture is more cautionary: agent skills can dramatically improve how your agent handles Kotlin frameworks, build migrations, and backend patterns, but research showing roughly 26% of skills carry at least one vulnerability means installing them blindly is a security gamble. That is the gap SkillzBench is built to close — verifying every skill against a commit SHA, security-scanning and license-checking each one, and benchmarking real Lift in a sandbox so you know what a skill actually contributes before it touches your codebase. Everything on SkillzBench is free, and the waitlist is open now. If you are equipping Claude Code, Codex, Cursor, or Antigravity with Kotlin skills this year, join the SkillzBench waitlist and be first to browse, verify, and benchmark skills with confidence — because skills should be proven, not hoped for.

Frequently asked questions

What are AI agent skills, and why do they matter for Kotlin development?

Agent skills are self-contained folders that package instructions, scripts, and resources for an AI coding agent to use on a specific use case. Each includes a SKILL.md file with YAML frontmatter followed by guidance the agent follows while the skill is active. For Kotlin, skills can teach an agent how to correctly use Spring or JPA, handle build tooling like AGP9 migrations, or follow Kotlin idioms. JetBrains maintains an official Kotlin agent skills repository, and agents like Claude Code, Codex, and Antigravity CLI all support the Agent Skills standard. They matter because a general-purpose agent without Kotlin-specific guidance can produce non-idiomatic code or mishandle framework conventions.

Are agent skills safe to install?

Not automatically. Published research found that approximately 26% of agent skills carry at least one vulnerability, and skills are instruction packages your agent will follow — which means a malicious or poorly written skill can steer your agent's behavior across your codebase. Before installing any skill, including from reputable sources, you should verify its version, license, and security posture. This is precisely the problem SkillzBench addresses: it pins every skill version to a commit SHA, runs security scans and license checks, and publishes a 0–100 trust score so you can make an informed decision before installation.

Which AI coding agent is best for Kotlin in 2026?

There is no single best agent — it depends on your workflow. Claude Code is widely regarded as the strongest for deep reasoning, complex multi-file refactors, and coroutine-heavy debugging, scoring 66 on Artificial Analysis's independent Coding Agent Index. OpenAI Codex offers the best cost-per-task ($2.99 with Sol) and the strongest enterprise governance. Cursor is the leading AI-native IDE for developers who prefer visual editing, and GitHub Copilot is the lowest-friction choice for teams already on GitHub Enterprise. Google's Antigravity CLI offers the most generous free tier. Most teams in 2026 mix agents, matching models and tools to task types.

How much do AI coding agents cost for Kotlin development?

Costs vary widely. Antigravity CLI offers a free individual tier with unlimited tab completions and command requests. Cursor starts at $20/month (Pro) with an Ultra tier at $200/month. GitHub Copilot runs $19–$39 per user/month on Business and Enterprise, with a $100/month Max plan using AI Credits. Claude Code and Codex are usage-based: Claude Opus 5.5 runs $4/$20 per 1M tokens and measured around $13.00 per task independently, while Codex with Sol measured $2.99 per task. Koog and JetBrains' Kotlin agent skills repository are free and open source, though model API costs apply separately.

Can I build my own AI coding agent in Kotlin?

Yes, and Kotlin is a genuinely good language for it. Koog is an open-source framework from JetBrains for building AI agents in Kotlin, providing the execution loop (sending prompts, parsing tool calls, executing them, and repeating), multiplatform support across JVM through iOS, built-in retries, agent persistence, history compression, and MCP integration. JetBrains' blog series walks through building a minimal coding agent with Koog using just three tools — list directory, read file, and edit file. Koog integrates with Spring Boot and Ktor, making it idiomatic for existing Kotlin backend teams.

What is SkillzBench and is it really free?

SkillzBench is AIQ Labs' free, independent directory of AI agent skills plus a free MCP tool that connects any agent — including Claude Code, Codex, Cursor, and Gemini CLI/Antigravity — to the skills you choose. It continuously crawls and deduplicates skills from GitHub, pins every version to a commit SHA, security-scans and license-checks each one, and benchmarks skills in a sandbox to publish their Lift in percentage points. Everything is free: the directory, the MCP connector, benchmarks, and alerts. The platform is funded by advertising around its blog articles, and ad money never influences scores or listings. SkillzBench is currently pre-launch; the waitlist and newsletter are open for early access.

How do I install JetBrains' official Kotlin agent skills?

JetBrains' Kotlin/kotlin-agent-skills GitHub repository documents several installation paths. You can use the skills CLI with 'npx skills add Kotlin/kotlin-agent-skills', install via Claude Code's plugin marketplace ('claude plugin marketplace add Kotlin/kotlin-agent-skills' followed by 'claude plugin install kotlin-agent-skills@Kotlin'), use Codex's plugin marketplace flow, or manually copy a skill folder into your agent's skills directory. Skills follow the naming convention kotlin-<category>-<functional-name> across backend and tooling categories. Before installing, it is wise to verify the skill's version and security — tools like SkillzBench are being built to automate exactly that verification and benchmarking step.