Giskard-AI

Run the full code quality gate for the giskard-oss monorepo—format, lint, typecheck, and unit tests across all packages. Use when finishing a feature, before opening a PR, or when the user asks to check quality or run the gate.

Describes the merge-ready workflow for the giskard-oss monorepo using the repository root Makefile and the same steps as GitHub Actions—format, check, unit tests, optional pre-commit. Use when preparing or updating a pull request, confirming CI will pass, running pre-push validation, or when the user asks for a branch to be merge-ready.

Guides implementation of new or changed eval checks in the giskard-checks package—Check subclasses, discriminated kind registration, public exports, and tests. Use when adding or modifying built-in checks, LLM-based judges, @Check.register kinds, serialization of TestCase or Scenario, or when editing files under libs/giskard-checks/src/giskard/checks related to checks.

Generates tailored giskard.checks evaluation suites for RAG (Retrieval-Augmented Generation) systems. Use whenever a user describes a Q&A bot grounded in documents, a knowledge-base chatbot, a retrieval system, or wants to evaluate answer groundedness, faithfulness, hallucination, retrieval quality, citation accuracy, or out-of-scope handling. Triggers on phrases like "evaluate my RAG", "test my retrieval", "check groundedness", "build a RAG eval suite", "eval my chatbot answers from docs", "test if my agent hallucinates", "check if my answers are faithful to the sources", or any evaluation task involving an agent that answers from documents, FAQs, wikis, or a knowledge base. Use this skill even when the user does not explicitly say "RAG" but describes an agent grounded in documents. For adversarial / red-teaming evaluation, use the `scenario-generator` skill instead. This skill focuses on quality, not safety.

Generates tailored giskard.checks test scenarios and suites for AI agents. Use when user describes their agent and fears, asks to "create scenarios", "test my agent", "generate checks", "evaluate my chatbot", "red-team my AI", or wants to build adversarial test cases for LLM-based applications.