By keboola
Comprehensive toolkit for building Keboola Python components with Agent Skills format. Includes specialized skills for component development, UI/schema design, unified testing (datadir, unit, VCR), debugging, consolidated code quality and backward compatibility review, and platform context knowledge.
npx claudepluginhub keboola/ai-kit --plugin component-developerSet up VCR functional tests for this Keboola component using the `component-developer:test-component` skill.
Code quality + backward compatibility review for Keboola Python components
Launch interactive schema tester for testing component configuration schemas
Expert Keboola Python component developer for implementing and extending components. Use when building new extractors/writers/applications, implementing features, adding incremental loads, separating API clients, applying self-documenting workflow patterns, or maintaining code quality with Ruff. Delegates UI/schema work to ui-developer.
Expert agent for writing and maintaining tests for Keboola Python components. Specializes in datadir tests, unit tests, mock-based tests, and VCR functional tests with keboola.datadirtest.
Expert in Keboola configuration schemas, conditional fields (options.dependencies), UI elements, sync actions, and schema testing. Can launch schema-tester and run Playwright tests. Specialized for configSchema.json and configRowSchema.json development.
Expert Keboola Python component developer for ongoing development work — implementing features, extending extractors/writers/applications, adding incremental loads, designing configuration schemas, separating API clients, applying self-documenting workflow patterns, and maintaining code quality with Ruff. Use this skill whenever the user is working on component logic, implementation, or architecture — whether building something new or extending existing code. Do NOT use for initial project scaffolding (that's get-started) or UI-only schema work (that's build-component-ui).
Mandatory initialization workflow for new Keboola Python components — use this skill before writing any code. Contains the exact cookiecutter command, Keboola-specific post-template cleanup steps, KBC_DATADIR directory structure, data/config.json format, and first commit format that are easy to get wrong without this reference. Invoke whenever the user is starting from a fresh or empty repository, has never built a Keboola component before, wants to run the cookiecutter template, or says things like "new extractor for X", "build ex-something from scratch", "fresh repo", "empty git repo", "brand new component", "never done this in keboola", "initialize a component", "scaffolding a new component", "just initialized the git repo", "nothing set up yet". Not for existing components that already have src/component.py.
Expert in Keboola configuration schemas, conditional fields (options.dependencies), UI elements, sync actions, and schema testing. Can launch schema-tester and run Playwright tests. Specialized for configSchema.json and configRowSchema.json development.
Canonical template files for Keboola Python components — Dockerfile, pyproject.toml, push.yml, build_n_test.sh, docker-compose.yml, pre-commit-config.yaml, and config-schema.md. Load this whenever creating or modifying any of these files in a component, or when checking whether non-source files are aligned with the official cookiecutter template. Any deviation from these templates must have an explicit reason. Internal utility — usually invoked via Task from develop-component or migrate-to-uv, but should be consulted any time one of these files is being touched.
Expert skill for debugging Keboola Python components. Use when a component is failing, a job returned an error, or behavior is unexpected. Uses whatever tools are available — Keboola MCP for job/config inspection, Datadog for logs, Linear/Jira for issue context, Slack for incident history, and local Bash for reproducing issues. Invoke for "failing job", "exit code 2", "component throwing an error", "why is my component not working".
Keboola platform context — automatically load this whenever working on any Keboola Python component task (developing, testing, debugging, reviewing, schema design). Contains platform-level knowledge about how the Keboola Connection platform executes components, manages configuration, handles state, and runs jobs. This is not about code patterns — it is about platform behaviour that affects how code should be written and tested. Always pull this before making decisions about config structure, row handling, state, parallelism, or test data layout.
Migrate Keboola Python components to modern uv build system with deterministic dependencies and ruff linting.
Full code review for Keboola Python components — covers both code quality (architecture, config/client patterns, typing, Pythonic best practices) and backward compatibility (configSchema changes, sync actions, output tables, state files, telemetry impact). Use for PRs, pre-merge checks, and component audits. Invoke whenever someone asks to review code, check a PR, audit before release, or verify that changes are safe for existing users.
Expert agent for writing and maintaining tests for Keboola Python components. Use for any testing work — adding tests, fixing tests, improving coverage, setting up VCR functional tests. Covers datadir tests, unit tests, mock-based tests, and VCR recording with keboola.datadirtest. Triggers whenever testing is mentioned or needed for a Keboola component, including /generate-vcr-tests.
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Uses power tools
Uses Bash, Write, or Edit tools
Comprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification
Team-oriented workflow plugin with role agents, 27 specialist agents, ECC-inspired commands, layered rules, and hooks skeleton.
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams