Creates Karibu server-side unit tests for Vaadin views covering navigation, component interactions, form validation, grid operations, and notifications. Use when the user asks to "write Karibu tests", "unit test a Vaadin view", "test the UI server-side", "create view tests", or mentions Karibu testing, Vaadin unit tests, or server-side UI testing.

Creates Playwright browser-based integration tests for Vaadin views using the Drama Finder library for type-safe element wrappers with accessibility-first APIs. Use when the user asks to "write Playwright tests", "create e2e tests", "write integration tests", "test in the browser", "write IT tests", or mentions end-to-end testing, browser tests, UI integration tests, Playwright for Vaadin, or Drama Finder. Also trigger when the user references a use case (UC-*) and asks for Playwright or E2E tests.

Review completed implementation against approved specs to ensure all requirements are satisfied. Use after finishing implementation work, before marking a task as done, or when a stakeholder asks to verify deliverables against requirements. Produces a review summary with pass/fail per requirement. Common triggers: "review my work", "check against spec", "did I miss anything", "is implementation complete".

Refactor a regular Kotlin `class` with hand-written `equals`, `hashCode`, `toString`, and Java-bean accessors (getX/setX) into an idiomatic Kotlin `data class` with `val` properties, nullable `?` types, and compiler- generated equality. Removes the boilerplate that ports from Java leave behind. Use when the user asks to "make this a data class," "convert this POJO," "kotlinify this model," or shows a class with all three of equals / hashCode / toString manually overridden.

Scaffold the `jbaruch/coding-policy` gh-aw PR review workflows into a consumer repository: copies the packaged paired workflow templates (OpenAI + Anthropic reviewers), compiles them with `gh aw`, and opens a PR. After merge, every pull request in the repo is reviewed against the latest published `jbaruch/coding-policy` rules by the reviewer whose family differs from the PR's declared author model — avoiding self-review bias per `rules/author-model-declaration.md`. Use when the user wants to add, install, enable, scaffold, set up, or wire up an automated policy review / PR reviewer / coding-policy CI reviewer in a consumer repo. Also use to upgrade, update, refresh, or pull the latest reviewer templates into a consumer repo that already has the scaffold installed — the skill switches to override mode in that case.

Migrate a legacy tile.json plugin to the .tessl-plugin/plugin.json form. Runs `tessl plugin migrate`, renames .tileignore to .tesslignore, removes the obsolete tile.json, re-lints, then reconciles residual "tile" wording to "plugin" in the repo's prose. Use when a repo still has a tile.json (and no .tessl-plugin/plugin.json), or when the user asks to migrate / convert / modernize a tile.json plugin, move off tile.json, or adopt the plugin.json manifest form.

Structured workflow for shipping code via GitHub pull requests: PR creation, dual-lens automated review (gh-aw for `rules/*.md` compliance + Copilot for doc accuracy and cross-step consistency), merge, and branch cleanup. Covers readiness checks, version reasoning, review polling, feedback handling, and post-merge verification. Use when the user wants to open a pull request, ship code, merge a branch, or handle post-merge cleanup on GitHub.

Generates a structured, human-readable reviewer packet summarising what changed in a pull request, why it matters, what was verified, and where human attention is most needed. Use when the user asks for a PR review summary, a code review packet, a human-readable change report, or wants to hand off review findings to a human reviewer. Produces a scannable document: quick approvals (low-risk PRs) can be assessed in under 30 seconds; detailed reviews (high-risk PRs) in under 2 minutes. Outputs a formatted markdown packet with risk rating, verification status, ranked findings, unresolved questions, and a recommended review focus — making human review faster without replacing human judgment.

Build a compact, trustworthy evidence pack before deeper PR review starts. Use this skill when a pull request needs review — it is always the first step. Triggered by requests to review code, check a PR, review my changes, review a merge request, or any similar code review or pull request review request. Collects PR context, runs deterministic verifiers, classifies risk, maps hotspots, and checks for missing artifacts. Produces the evidence pack that all downstream review skills consume.

Evaluates which code review comments (review tiles) actually produced changes after a pull request is merged or closed, by passively collecting outcome data from the GitHub API and git history — zero developer friction. Use when analyzing post-merge pull request outcomes, assessing code review effectiveness, measuring review feedback impact, or answering questions like "how did PR #6 go?", "which review comments were accepted?", or "did any escaped defects appear after this pull request merged?" Produces a structured per-finding outcome record (accepted / rejected / ignored / superseded), merge time delta, escaped defect count, and AI authorship correlation for each PR.

Check an already-open issue or pull request body against the host repo templates and draft a suggested comment for human review, not posting. Use when asked to triage an existing issue/PR, decide whether an existing body is good enough before responding, review whether it follows the repo template, or draft a comment asking for missing information. Triggers include "triage this issue", "review this existing PR", "does this PR follow the template", "check whether the PR body follows the repository's pull request template", "check whether the issue body follows the repository's issue template", "quick check on this open issue", "body is good enough", "asked for anything more before I respond", and "what's missing from this PR body". For NEW issue/PR drafts use `propose`; for own PR pre-submission verification use `preflight`.

Generate quality checklists that validate requirements completeness, clarity, and consistency — produces scored checklist items linked to specific spec sections (FR-XXX, SC-XXX). Use when reviewing a spec for gaps, doing a requirements review, verifying PRD quality, auditing user stories and acceptance criteria, or gating before implementation.

Generate Gherkin .feature files from requirements before implementation — produces executable BDD scenarios with traceability tags, computes assertion integrity hashes, and locks acceptance criteria for test-driven development. Use when writing tests first, doing TDD, creating test cases from a spec, locking acceptance criteria, or setting up red-green-refactor with hash-verified assertions.

Generate dependency-ordered task breakdown from plan and specification. Use when breaking features into implementable tasks, planning sprints, or creating work items with parallel markers.

Validate cross-artifact consistency — checks that every spec requirement traces to tasks, plan tech stack matches task file paths, and constitution principles are satisfied across all artifacts. Use when running a consistency check, verifying requirements traceability, detecting conflicts between design docs, or auditing alignment before implementation begins.

Execute the implementation plan by coding each task from tasks.md — writes source files, runs tests, verifies assertion integrity, and validates output against constitutional principles. Use when ready to build a feature from a tasks.md plan, start coding against an Intent Integrity Kit implementation plan, develop from the task list, resume a partially completed implementation, or run the implement phase of the iikit workflow.

Convert tasks from tasks.md into GitHub Issues with labels and dependencies. Use when exporting work items to GitHub, setting up project boards, or assigning tasks to team members.

Report a bug against an existing feature — creates a structured bugs.md record, generates fix tasks in tasks.md, and optionally imports from or creates GitHub issues. Use when fixing a bug, reporting a defect, importing a GitHub issue into the workflow, or triaging an error without running the full specification process.

Resolve ambiguities in any project artifact — auto-detects the most recent artifact (spec, plan, checklist, testify, tasks, or constitution), asks targeted questions with option tables, and writes answers back into the artifact's Clarifications section. Use when requirements are unclear, a plan has trade-off gaps, checklist thresholds feel wrong, test scenarios are imprecise, task dependencies seem off, or constitution principles are vague.

Initialize an IIKit (Intent Integrity Kit) project, uninit (remove IIKit scaffolding before `tessl uninstall`), check IIKit feature progress, select the active IIKit feature, and display the IIKit workflow command reference. Use when starting a new IIKit project, running IIKit init or setup, uninstalling/removing/uninit-ing IIKit before running `tessl uninstall`, checking IIKit status, switching between IIKit features, looking up IIKit available commands and phases, or asking for help with the IIKit workflow.

Get typed structured output from a Koog 1.0 agent — pick between `nodeLLMRequestStructured` (graph DSL, schema-driven JSON) and `responseProcessor` (top-level on the agent factory, simpler shape). Defines the `@Serializable` output class and wires it into the strategy or the factory. Use when the user asks to "return JSON", "get a typed response", "use structured output", "return a data class from the agent", or shows a `data class` they want as the agent's output.

Add token-budgeting and per-provider tokenizer support to a Koog 1.0 agent — install the tokenizer feature, set per-run or per-node budgets, and react to budget exhaustion (compress history, abort, swap models). Use when the user asks to "limit tokens per run", "add a token budget", "prevent runaway agent costs", "use a tokenizer", or describes cost containment requirements.

Add a new tool to an existing Koog 1.0 agent. Pick the right registration style (@Tool + ToolSet annotation, Tool[TArgs,TResult] subclass, or sub-agent-as-tool), define the args, and wire the tool into the agent's ToolRegistry. Use when the user asks to "add a tool to my agent", "expose something to the LLM", "let the agent call a function", or "wrap this agent as a tool for another agent". Assumes a scaffolded Koog 1.0 project — for new projects start with the scaffold-agent skill.

Author a custom graph strategy for a Koog 1.0 agent — pick the right node types, chain tool execution correctly, build edges with the infix vocabulary, and reach for subgraphs (`subgraphWithTask`, `subgraphWithVerification`) when steps deserve their own model, prompt, or tool subset while still sharing the agent's message history. Use when the user asks to "write a custom strategy", "build a graph for the agent", "author a strategy DSL", "add a verify-and-fix loop", "use subgraphs", or describes multi-step orchestration that won't fit inside `singleRunStrategy()`. Subgraphs are not for context isolation — for an independent agent that does not see the parent's history, use `Skill(skill: "add-tool")` Step 3 (sub-agent-as-tool).

Add in-process caching of LLM calls to a Koog 1.0 agent via `prompt-executor-cached` — cache whole prompt→response pairs locally so identical calls skip the API. Distinct from provider-side Anthropic prompt caching (covered by `enable-prompt-caching`). Backends include in-memory (default), file-based, and Redis. Use when the user asks to "cache LLM responses", "avoid duplicate API calls", "add a response cache", "cache to Redis", or describes repeated identical prompts in dev/test.

Author prompts for a Koog 1.0 agent using the `prompt { ... }` DSL — system messages, user turns, few-shot examples, mixed media, and runtime augmentation via the `SystemPromptAugmenter` / `UserPromptAugmenter` family. Use when the user asks to "write a system prompt with examples", "add few-shot examples", "build a prompt", "augment the prompt at runtime", or moves beyond the single-string `systemPrompt` parameter on the factory.

Author a Koog 1.0 agent as a typed pipeline of domain-modeled subtasks — tools sliced by access pattern (read / write / communication) into separate ToolSets, inter-subtask handoffs as `@Serializable` `@LLMDescription`-annotated data classes (not text prompts), each subtask wired with `subgraphWithTask[In, Out]` using its own model and tool subset, self-correction loops via `subgraphWithVerification[T]` + `CriticResult[T]`. The integration pattern Koog's own banking demo uses. Use when the user asks to "model the agent as a pipeline", "build a multi-stage agent with typed handoffs", "give each stage its own tools", "build a verify-and-fix loop with typed data", or describes a workflow with distinct phases that should hand structured data to each other.

Enable Anthropic prompt caching for a Koog 1.0 agent — automatic caching is on by default in 1.0, but explicit `cacheControl` breakpoints let you control which parts of long prompts get cached. Surfaces cache-hit metrics through the OpenTelemetry token-usage span. Use when the user asks to "enable prompt caching", "reduce Anthropic costs", "add cache_control", "set cache breakpoints", or describes expensive repeated calls with shared system prompt content.

Install per-step event handlers on a Koog 1.0 agent — tool-call start/end, LLM request/response, agent finish, error events. Useful for stdout logging during development, visualizing planner decisions on stage during demos, or pushing events to a non-OTel sink. Use when the user asks to "log tool calls", "see what the agent is doing", "add event handlers", "visualize the planner", "trace each step" — anything where the goal is human-readable per-step output, not production metrics.

Work with Koog 1.0 agent state — typed key-value `storage` on `AIAgentContext`, history compression strategies (TL;DR, sliding window, fact retrieval), and the `LongTermMemory` feature (which replaces the removed `AgentMemory`) for cross-session recall. Use when the user asks to "store state across nodes", "compress conversation history", "remember things across sessions", "add long-term memory", or names any of these surfaces.

Migrate a Koog 0.x codebase to 1.0. Koog 1.0 (2026-05-21) removed every @Deprecated API in one sweep — 0.x code does not compile against 1.0. Walks through construction surface, planner module split, graph DSL renames, Java interop overhaul, HTTP transport decoupling, memory APIs, OpenTelemetry, persistence, prompt package move, retired models, and JDK/tooling minima. Use when the user asks to "migrate from 0.x", "bump Koog to 1.0", "upgrade Koog", or shows code that uses pre-1.0 APIs (e.g., AIAgent.invoke, AgentMemory, AIAgentPlannerStrategy.builder).

Model a problem domain as a tree of typed subtasks the Koog 1.0 planner can execute — the `PlannerNode` data model, parallel vs sequential composition, accessing in-flight subtasks through `AIAgentStorage`, retry-on-parse-failure edges, and TL;DR compression between phases. Goes deeper than `use-planner` (which only picks the planner factory). Use when the user asks to "decompose into subtasks", "model the agent's work as a tree", "compose parallel and sequential steps", "retry failed subtasks", or describes a richer planner usage than the basic factory.

Persist a Koog 1.0 agent's chat history to a durable backend — JDBC database (`chat-history-jdbc`), AWS storage (`chat-history-aws`), or SQL-typed chat memory (`chat-memory-sql`) — so conversations survive process restarts and can be retrieved by session ID. Distinct from generic agent persistence (state checkpoints) and from LongTermMemory (fact retrieval). Use when the user asks to "save chat history", "persist conversations", "resume a chat by session", "store chat in Postgres / DynamoDB".

Give a Koog 1.0 agent the ability to query a SQL database safely — install `agents-features-sql`, register the database connection, and expose schema-aware query tools the LLM can call. Includes safety guidance (read-only by default, schema scoping, parameterized queries). Use when the user asks to "let the agent query the database", "add SQL to my agent", "expose a database to the LLM", or describes data-retrieval needs the LLM should drive.

Bootstrap a new Koog 1.0 Kotlin agent project from scratch: Gradle setup with the right dependencies, JDK 17 toolchain, application Main that constructs an AIAgent via the top-level factory, and an environment-variable wiring for the LLM API key. Use when the user asks to "create a new Koog agent", "start a Koog project", "scaffold an agent app", or provides a directory and says "set up Koog here". Produces a runnable hello-world agent that the user can extend with tools, strategies, or features. Do NOT use when the user is constructing a planner, picking a strategy variant, or naming a specific agent shape inside an existing project — use `use-planner` or `author-strategy` instead.

Snapshot a running Koog 1.0 agent's state at arbitrary points and restore later — distinct from the persistence checkpoint loop in `add-persistence`. Snapshots are caller-triggered; persistence is automatic and continuous. Use when the user asks to "snapshot the agent", "save state at this point", "restore from a snapshot", or names the `agents-features-snapshot` module.

Test Koog 1.0 agents deterministically — install `agents-test`, mock the prompt executor with scripted responses, inject a fake `KoogClock` for time-sensitive logic, and assert on tool-call sequences. Use when the user asks to "test the agent", "mock the LLM in tests", "write unit tests for my Koog agent", or describes flaky or expensive tests that hit a real LLM.

Install the `agents-features-trace` feature to capture detailed internal trace events from a Koog 1.0 agent — node entries, edge transitions, planner decisions, feature lifecycle. Distinct from OpenTelemetry (production signal, GenAI vocabulary) and event handlers (high-level callbacks). Use when the user asks to "debug what the strategy is doing", "trace internal agent decisions", "see why the planner picked that step", or describes deep diagnostic needs.

Send non-text content (images, files, audio) to the LLM as message attachments in a Koog 1.0 agent — provider-aware encoding and the `attachments` block in the prompt DSL. Use when the user asks to "send an image to the LLM", "use multimodal input", "attach a file", "pass a PDF", or describes input the LLM should process that isn't plain text.

Use `FunctionalAIAgent` — the third concrete agent subtype in Koog 1.0 (alongside `GraphAIAgent` and `PlannerAIAgent`). Wraps a single suspending block, no graph DSL, no planner — just programmer-written logic that calls the LLM and tools directly. Use when the user asks to "skip the graph DSL", "write the agent body as plain code", "use AIAgentFunctionalStrategy", or describes a one-shot agent shape that doesn't warrant a topology.

Use a non-default LLM node variant inside a Koog 1.0 strategy — streaming output, multiple-choice sampling, content moderation, or forcing a specific tool call. Use when the user asks for "streaming", "multiple completions / sampling", "moderation", "force one tool", "force the LLM to call a specific tool", or names any of `nodeLLMRequestStreaming`, `nodeLLMRequestMultipleChoices`, `nodeLLMModerateText`, `nodeLLMRequestForceOneTool`.

Pick and wire a planner-driven Koog 1.0 agent — either LLM-based (the LLM picks the next action each turn, optionally with a critic loop) or GOAP (a classical planner searches a typed state space toward a goal). Pulls `ai.koog:agents-planner`, constructs the planner strategy, and wires it into `AIAgent(...)`. Use when the user asks to "use a planner", "let the agent plan", "use GOAP", "build a planning agent", names any of `Planners.llmBased`, `Planners.llmBasedWithCritic`, `Planners.goap`, `PlannerAIAgent`, `agents-planner`, or describes an open-ended task whose step sequence depends on runtime context.

Wire the Agent-to-Agent (A2A) protocol — expose a Koog 1.0 agent as an A2A server, or consume a remote A2A server as a client (typically to make a remote agent callable as a tool by a local agent). Use when the user asks to "expose the agent via A2A", "use A2A protocol", "call a remote agent", "register an A2A client", or names any of `a2a-server`, `a2a-client`.

Expose a Koog 1.0 agent through the Agent Client Protocol (ACP) — the lower-level bidirectional protocol used by tooling that needs fine-grained control over agent invocation lifecycle (cancellation, streaming progress, multi-turn negotiation). Use when the user asks to "expose the agent via ACP", "use Agent Client Protocol", "wire ACP", or names the `agents-features-acp` module.

Expose a Koog 1.0 agent through a Ktor server — install the `koog-ktor` plugin, load agent configuration from `application.conf` / `.yaml`, and register MCP servers inside the plugin block. Use when the user asks to "expose the agent over HTTP", "add Koog to my Ktor app", "wire the Ktor plugin", or describes a server-shaped deployment.

Connect a Koog 1.0 agent to an MCP (Model Context Protocol) server, using the primary 1.0 Streamable HTTP transport — or fall back to SSE / stdio when the remote server doesn't speak Streamable HTTP yet. Adds the agents-mcp dependency, builds a ToolRegistry from the MCP server's exposed tools, and merges it with the agent's existing tool registry. Use when the user asks to "connect to an MCP server", "use the GitHub MCP server", "add MCP tools to my agent", "wire Playwright MCP" or similar. Assumes a scaffolded Koog 1.0 project.

Wire Koog 1.0 into a Spring Boot application via `koog-spring-boot-starter` — per-provider autoconfig, `MultiLLMAutoConfiguration` aggregation, and the `application.yml` shape for agent name, model, system prompt, and tools (including MCP entries). Use when the user asks to "use Koog in Spring Boot", "wire the Spring starter", "configure providers via application.yml", "add Koog to my Spring app".