Perfup
raullenchai/Rapid-MLX
Autonomous performance optimization: research, PoC, benchmark, implement, review, PR
Kitaru just recipes, CLI structure and structured-output contract, analytics events, and PR-description conventions.
$ npx skills add zenml-io/kitaru --skill kitaru-dev -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zenml-io/kitaru kitaru-dev --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/zenml-io/kitaru.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/kitaru-dev .claude/skills/kitaru-dev && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "kitaru-dev" agent skill from https://github.com/zenml-io/kitaru/tree/develop/.agents/skills/kitaru-dev into .claude/skills/kitaru-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kitaru-dev", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/zenml-io/kitaru/tree/develop/.agents/skills/kitaru-devType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add zenml-io/kitaru --skill kitaru-dev -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zenml-io/kitaru kitaru-dev --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenml-io/kitaru.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/kitaru-dev .agents/skills/kitaru-dev && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "kitaru-dev" agent skill from https://github.com/zenml-io/kitaru/tree/develop/.agents/skills/kitaru-dev into .agents/skills/kitaru-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kitaru-dev", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zenml-io/kitaru --skill kitaru-dev -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zenml-io/kitaru kitaru-dev --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenml-io/kitaru.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/kitaru-dev .cursor/skills/kitaru-dev && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "kitaru-dev" agent skill from https://github.com/zenml-io/kitaru/tree/develop/.agents/skills/kitaru-dev into .cursor/skills/kitaru-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kitaru-dev", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/zenml-io/kitaru.git --path .agents/skills/kitaru-dev--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add zenml-io/kitaru --skill kitaru-dev -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zenml-io/kitaru kitaru-dev --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenml-io/kitaru.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/kitaru-dev .gemini/skills/kitaru-dev && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "kitaru-dev" agent skill from https://github.com/zenml-io/kitaru/tree/develop/.agents/skills/kitaru-dev into .gemini/skills/kitaru-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kitaru-dev", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install zenml-io/kitaru kitaru-devInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add zenml-io/kitaru --skill kitaru-dev -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zenml-io/kitaru.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/kitaru-dev .github/skills/kitaru-dev && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "kitaru-dev" agent skill from https://github.com/zenml-io/kitaru/tree/develop/.agents/skills/kitaru-dev into .github/skills/kitaru-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kitaru-dev", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zenml-io/kitaru --skill kitaru-dev -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zenml-io/kitaru kitaru-dev --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenml-io/kitaru.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/kitaru-dev .opencode/skills/kitaru-dev && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "kitaru-dev" agent skill from https://github.com/zenml-io/kitaru/tree/develop/.agents/skills/kitaru-dev into .opencode/skills/kitaru-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kitaru-dev", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
kitaru-devKitaru just recipes, CLI structure and structured-output contract, analytics events, and PR-description conventions.
Kitaru Dev is an agent skill from zenml-io/kitaru. Kitaru just recipes, CLI structure and structured-output contract, analytics events, and PR-description conventions. Use when running project commands, adding CLI commands or analytics events, or writing a PR description.
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/evaluation-contracts.md`).
It sits in AI & LLM Engineering, covering Pull requests and Structured output and tool calling. It works with Model Context Protocol and Python. The repository describes itself as: Agent traces you can run, not just read. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit 9d2df59. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
justuvFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Kitaru Dev loads about 2.6k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 1,320 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from zenml-io/kitaru at commit 9d2df59, republished under its Apache-2.0 licence (© zenml-io). 1,320 words, ~2,634 tokens.
.claude/skills/kitaru-dev/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Use this when you need the command catalog beyond the daily loop in the root AGENTS.md, or when adding CLI commands, analytics events, or PR descriptions.
uv sync: install the base SDK and development dependenciesuv sync --extra cli: include the optional CLIuv sync --extra mcp: include the optional native MCP serveruv sync --extra server: include server componentsuv sync --extra worker: include worker componentsuv sync --extra otel: include OpenTelemetry integrationsjust check: run formatting, lint, OpenAPI freshness, changelog fragments, typecheck, typos, YAML, actions lint, and linksjust openapi-check: verify that the committed OpenAPI specification matches the application schemajust changelog-check: validate the changelog fragments under changelog.d/just fix: auto-fix formatting, lint issues, and YAMLjust test: run the full pytest suitejust test tests/test_file.py::test_name: run one targeted testjust lint: lint onlyjust typecheck: type check onlyjust typos: typo check onlyjust format-check: check formatting without modifying filesjust yaml-check: check YAML formattingjust actions-lint: lint GitHub Actions workflows; requires actionlintjust zizmor: audit GitHub Actions workflow security with zizmorjust audit: audit Python dependencies with pip-audit and the documented ignore listjust links: check Markdown links offline; requires lycheejust links-external: check links including external URLs; slowjust example-coverage-audit: validate examples/example-coverage.yaml metadata and waiversjust build: build wheel and sdist locallyjust cli-artifact-smoke: verify clean CLI wheel and source installationsjust plugin-artifact-smoke: build every default-plugin wheel, load its configured entrypoints, and verify default registrationjust mcp-schema-check: verify public MCP registry budgets and committed snapshotsjust mcp-wheel-smoke: verify clean base and [mcp] installs from the wheel under dist/just migration-check: compare Alembic migrations with the ORM schema; requires PostgreSQLThere is no v2 kitaru init command or local extra. Do not carry the v1 .kitaru/ project-marker setup into v2 instructions or tests.
When resolving pyproject.toml or uv.lock conflicts, do not regenerate the whole lockfile: that silently reverts intentional dependency-security bumps. Upgrade only the packages involved and run just audit before pushing.
Before opening a PR that creates an independently published package, trace how it will be built, installed, discovered, and released. This applies to a new adapter, importer, evaluator, or other Python distribution; adding an evaluator inside the existing kitaru-evaluator wheel does not create a new distribution. For substantial packages, ask a bounded independent subagent to review the integration points and omissions, then verify its findings against the code. Record the applicable paths and any deliberate exclusions in Reviewer Notes or Release context so a reviewer can check the complete package path.
plugins/README.md. Add artifact import metadata for a non-default package, and check whether plugins/pyproject.toml needs an update.release/release-units.toml and the expected inventory in tests/scripts/test_release_units.py. Check that the release workflow and CI matrix discover it; edit fixed selections only when they actually exclude the new package.kitaru-* distribution to src/kitaru/worker/process.py::_FIRST_PARTY_KITARU_PACKAGES, including packages outside the default server catalog. Run the inventory-based test in tests/worker/test_process.py; the server catalog is not the worker's package list.default-catalog in the release inventory accordingly, and change DEFAULT_PLUGIN_DEFINITIONS and its tests only for an approved default. Do not add adapters to the server catalog.--no-install-package list in .github/workflows/ci.yml. There is no global plugin ignore list; those exclusions apply to their particular example or build. Update them only when the new package enters that path.uv accepts the package exception, and verify that older uv retains the prior command and warns. Only a postpublication registry install proves the published wheel resolves under that cutoff. Run the focused package tests, release-inventory test, and just plugin-artifact-smoke before handoff.Use plugins/DEVELOPMENT.md for package and candidate-server commands, and the kitaru-release skill for version selection and publication. Do not treat registration metadata or a local wheel as proof that the published package can be installed by a worker.
These require Node 22+ and pnpm.
just docs: preview docs locally at localhost:3000just docs-build: build the static docs exportjust docs-validate: validate the export as served under /docsjust generate-docs: regenerate the SDK and CLI reference contentscripts/generate_sdk_docs.py extracts the v2 SDK reference through a PUBLIC_API allowlist. Edit that allowlist and tests/scripts/test_generate_sdk_docs.py together; the test compares each published module against its __all__. The generator needs the fumapy bridge after installing the docs dependencies. scripts/generate_cli_docs.py generates CLI reference content from the offline kitaru schema contract rather than a hardcoded command list.
The native v2 server is installed with kitaru[mcp] and started with kitaru-mcp. It defaults to read-only; standard and destructive expose progressively broader capabilities.
Treat tests/mcp/snapshots/metrics.json and src/kitaru/mcp/registry.py as the inventory authorities. Do not copy tool counts into prose. Run just mcp-schema-check after changing MCP models, registry declarations, descriptions, annotations, or SDK versions. Build the wheel and run just mcp-wheel-smoke after entrypoint, packaging, lifecycle, or optional-import changes.
The kitaru console script is defined in pyproject.toml under [project.scripts]. src/kitaru/cli/__init__.py is the lazy entry point, src/kitaru/cli/app.py registers the shared Cyclopts applications, and command implementations live under src/kitaru/cli/.
Register new leaf commands through the _spec(...) and _register(...) metadata in src/kitaru/cli/app.py. Tests should call main([...]) with an explicit argument list and assert the returned integer exit code.
When changing evaluation, replay, or experiment commands or contracts, read Evaluation contracts.
Agent-facing commands use the version-1 structured contract. Success documents include schema_version, command, ok, warnings, links, and next_actions, plus item for one result or items, count, and page for a list. Streaming commands emit JSONL events. Structured errors are one JSON object on stderr with a stable error kind and exit code.
For agent-facing use, prefer --output json --machine --non-interactive --no-browser. A deliberate dashboard or device-login handoff is the exception.
Document login consistently: kitaru login starts the interactive managed-cloud device flow and connects to the Kitaru workspace selected or created in the browser. kitaru login SERVER targets the full managed or self-hosted instance URL, while kitaru login --local provisions or reuses the CLI-owned Docker or Podman Compose deployment. The local deployment defaults to http://localhost:8000; --port takes precedence over KITARU_LOCAL_PORT, and the selected port persists with the deployment. kitaru logout stops that deployment when it is selected, and kitaru logout --volumes also deletes its PostgreSQL data.
kitaru status shows the selected server, provenance, credential state, compatibility, and live-worker count. kitaru info adds local package, Python, platform, and server details. kitaru doctor runs independent local, server, authentication, and tooling checks without stopping after the first failure. These commands never print secret values.
Analytics events live in src/kitaru/analytics/events.py; source attribution lives in src/kitaru/analytics/source.py. Server-side feature events are emitted through the application analytics service. MCP attribution is set once for the MCP lifecycle through AnalyticsSource.MCP.
AnalyticsEvent in src/kitaru/analytics/events.py.Use a clear human-readable title without a [Codex] prefix. Include what changed, why it was needed, important implementation decisions, and reviewer focus areas. Link related issues when applicable.
Add a changelog.d/<pr-number>.<section>.md fragment for user-facing changes instead of editing CHANGELOG.md. Any slug works in place of the number while the PR does not exist yet. See changelog.d/README.md for the format.
Every PR description should include a Reviewer Notes H2 or H3 section that explains the story and risks of the change, plus a concrete Reproduction subsection. Keep local hygiene commands as a short note after reproduction rather than using them as a substitute for reviewer guidance.
© zenml-io, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file (references) in .agents/skills/kitaru-dev of zenml-io/kitaru.
Open the folder on GitHubat commit 9d2df59
Kitaru Dev next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Kitaru Dev this skillzenml-io/kitaru | 301 | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Perfupraullenchai/Rapid-MLX | 3.9k | — | ~1.6k | Automated safety check: Notes | Custom licence | |
| Gemini API DevAyuilos/Miffan | 192 | 1 repos | ~1.4k | Automated safety check: Pass | AGPL-3.0 | |
| Tool Designagentailor/fullstack-langgraph-nextjs-agent | 132 | — | ~3.2k | Automated safety check: Pass | MIT | |
| Agent Framework Azure AI Pymicrosoft/skills | 3.1k | 1 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Gemini API Devaiskillstore/marketplace | 430 | 3 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 |
raullenchai/Rapid-MLX
Autonomous performance optimization: research, PoC, benchmark, implement, review, PR
Ayuilos/Miffan
A skill your agent uses when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function…
agentailor/fullstack-langgraph-nextjs-agent
Design and verify tools that AI agents can actually use — for any framework or language (MCP servers, LangChain/LangGraph, function-calling, raw JSON schema; TypeScript, Python, or otherwise).
microsoft/skills
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai).
aiskillstore/marketplace
A skill your agent uses when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function…
retentioneering/retentioneering-tools
Help the user turn their Retentioneering ideas, friction reports, bug findings, or feature needs into high-quality upstream contributions: from capturing and validating the idea, through minimal…
zenml-io/kitaru
Kitaru documentation surfaces, link rules, and accuracy rules.
zenml-io/kitaru
Discover dependencies and prepare or execute Kitaru core and plugin releases, including version proposals, Kitaru UI selection, release PRs, ordered tag commands, artifact verification, and recovery.
zenml-io/kitaru
Add, reuse, or change a frontend-specific Kitaru REST response under /api/v1/ui and its OpenAPI contract in zenml-frontend-monorepo.
zenml-io/kitaru
Add or change a Kitaru framework adapter that records native agent runs or supports bounded replay.
zenml-io/kitaru
Add or change a separately packaged Kitaru trace importer that normalizes provider exports into imported sessions.
zenml-io/kitaru
Kitaru test layout, CI workflows, and release-workflow behavior.
Works with
Kitaru just recipes, CLI structure and structured-output contract, analytics events, and PR-description conventions. Kitaru Dev is an agent skill from zenml-io/kitaru. Kitaru just recipes, CLI structure and structured-output contract, analytics events, and PR-description conventions.
Kitaru Dev fits situations like: running project commands; adding CLI commands; analytics events; writing a PR description.
Run `npx skills add zenml-io/kitaru --skill kitaru-dev -a claude-code`. Or copy the skill folder (.agents/skills/kitaru-dev in zenml-io/kitaru) into .claude/skills/kitaru-dev in your project. Claude Code loads it when a task matches its description.
Run `npx skills add zenml-io/kitaru --skill kitaru-dev -a codex`. Or copy the skill folder (.agents/skills/kitaru-dev in zenml-io/kitaru) into .agents/skills/kitaru-dev in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zenml-io/kitaru --skill kitaru-dev -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kitaru-dev, .gemini/skills/kitaru-dev, .github/skills/kitaru-dev and .opencode/skills/kitaru-dev in your project.
Going by SKILL.md and its folder, Kitaru Dev needs the command-line tools its instructions call (just and uv). Our summary lists: Python 3; Docker.
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Kitaru Dev is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 509 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Kitaru Dev: Perfup (raullenchai/Rapid-MLX, 3.9k stars), Gemini API Dev (Ayuilos/Miffan, 192 stars), Tool Design (agentailor/fullstack-langgraph-nextjs-agent, 132 stars) and Agent Framework Azure AI Py (microsoft/skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
zenml-io (a GitHub organization) maintains it in zenml-io/kitaru, which has 301 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 7, 2026.
Source: zenml-io/kitaru on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.