Claude Skills by zenml-io

Use when cutting a release of the kitaru-skills repository — pushing develop to main, bumping the kitaru plugin version, tagging a version, or publishing a GitHub Release for the Claude Code marketplace.

Build a project-local Kitaru adapter for an unsupported Python or TypeScript agent framework. Use when a user wants to record or replay framework-native agent runs in Kitaru, needs a custom adapter, has no supported Kitaru integration for their framework, or needs to assess whether public framework hooks and the installed Kitaru SDK can support an adapter.

Give first-time users a short, value-first Kitaru tour with the PydanticAI returns agent example in the Kitaru repository. Use when someone has no agent or traces of their own, arrives from Kitaru onboarding, asks for a demo, tutorial, quickstart, or guided example, needs the quickstart example cloned or prepared, wants the coding agent to prepare trace annotations before they judge sessions, or wants to experience Kitaru's value before learning the full investigation method. Prepare a three-...

Guide the ZenML Pro hosted Kitaru onboarding tour from preloaded traces through human review, one reusable evaluator, and one bounded replay. Use only inside the controlled hosted onboarding runner. Resume exact durable workspace state, handle existing agents without name collisions, and keep the tour concise.

Build and validate a custom Kitaru trace importer when a provider, observability platform, export format, or agent framework has no suitable built-in importer. Use when a user wants to map provider traces into Kitaru sessions and nodes, join per-turn traces into longer sessions, preserve incomplete or failed trace evidence, choose script or package installation, test importer fidelity, register an importer version, import a bounded sample, or diagnose a partial import.

Guide users from their own agent code or recorded traces through Kitaru setup, session import or recording, human review, an accepted behavior, a versioned cohort, and evaluator selection, then hand one bounded change to the replay-experiment skill. Use when a user wants to connect or inspect an existing agent, import real traces, investigate a known bad or surprising session, discover recurring failure modes, learn the evidence-led review flow, resume an investigation, create a cohort from r...

Run and interpret one safe, bounded Kitaru replay comparison against an accepted cohort and exact evaluators. Use when a user wants to replay a cohort, test or compare a model, prompt, system prompt, parameter, agent version, or tool policy, supervise an experiment run, determine whether one candidate helped, or ask for one bounded change worth testing.