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Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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Search a codebase or UI for places that don't animate but should, and reject everything that shouldn't. Read-only; it proposes motion with exact values, it does not implement it. Use when the user asks "what could be animated here?" or wants to "make this feel more alive". For fixing existing animations, use improve-animations or review-animations instead.

日本語の概要は準備中です。原文の説明を表示しています。

Run a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find everything about, research and file, go research, build a wiki on.

日本語の概要は準備中です。原文の説明を表示しています。

Security auditing for code, configs, and infrastructure. Use when the user wants to audit or improve security: scan for vulnerabilities (SQL injection, XSS, command injection, path traversal), detect hardcoded secrets and credentials, review auth and authorization, check dependencies for known CVEs, audit config files for insecure defaults, or generate security reports. Trigger on "security audit", "vulnerability scan", "code review for security", "find secrets", "check for vulnerabilities", "OWASP", "CVE", or questions about code security.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

日本語の概要は準備中です。原文の説明を表示しています。

Define a Loop — a durable scaffold that works a queue of recurring tasks autonomously (Sentry issues, Front tickets, small perf PRs) with its outputs held for human review. Use when someone wants an agent to keep doing a kind of work on a schedule, rather than one-off. Walks the interview, runs one real item live before arming anything, then writes the playbook and creates the loop.

日本語の概要は準備中です。原文の説明を表示しています。

Set up a Superset open-source contribution, from forking and cloning superset-sh/superset through local dev setup and the repo's rules to a merge-ready PR. Use when the user wants to contribute to Superset, fix a Superset bug themselves, add a feature to Superset, or prepare a PR against superset-sh/superset.

日本語の概要は準備中です。原文の説明を表示しています。

Collect and submit feedback about Superset (bug reports, feature requests, or general feedback) privately to the Superset team or as a public GitHub issue. Use when the user wants to report a Superset bug, request a feature, or send feedback about Superset.

日本語の概要は準備中です。原文の説明を表示しています。

Make a repository Superset-ready by authoring .superset/config.json with setup, teardown, and run scripts so every new workspace boots configured, then verifying with a real workspace. Use when the user wants to set up a project or repo for Superset, configure workspace setup scripts, or fix a failing workspace setup ("new workspaces don't have node_modules", "my .env is missing in workspaces", "setup script isn't running").

日本語の概要は準備中です。原文の説明を表示しています。

Turn a recurring chore into a Superset automation. Drafts the agent prompt, confirms the schedule or event trigger and the target, creates it with the CLI, and reviews the first run together. Use when the user wants a scheduled or recurring agent, a daily or weekly job, or an agent that reacts to a Slack message, GitHub pull request, Linear issue, Sentry issue or incoming email, or says things like "every morning do X", "automate this", "set up a cron agent", "run this on a schedule", "when someone opens a PR, do X".

日本語の概要は準備中です。原文の説明を表示しています。

Work the action items Circleback captured — find what is assigned to this user, do the ones the workspace can do, and update status only for work that is actually finished. Use when the user asks what they owe, "do my action items", "what's still open from", or wants to close, reassign, or add an action item.

日本語の概要は準備中です。原文の説明を表示しています。

Answer a question about money that already moved in Stripe — why a charge failed, what a customer was billed, where an invoice or payout stands — by reading the object chain instead of guessing from a dashboard total. Use when the user pastes a Stripe id, asks why a payment failed, asks what a customer is paying, or wants revenue, invoice, or subscription state.

日本語の概要は準備中です。原文の説明を表示しています。

Digest of what the user's Superset agents did while they were away, sweeping workspaces, tasks, and agent terminals to report what finished, what needs review, and what's blocked. Use when the user asks "what did my agents do", "what happened while I was away", wants a standup or summary of agent work, or returns after a break.

日本語の概要は準備中です。原文の説明を表示しています。

Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.

日本語の概要は準備中です。原文の説明を表示しています。

Reads and summarises ScenarioRunner output — the criteria pass/fail tables from --output/--file, the machine-readable --json and --junit result files, and the criteria JSON written alongside a --record recording — and runs the metrics module (metrics_manager.py) to compute custom measurements over a recorded run offline, without the simulator. Use when the user asks "did the scenario pass", "why did it fail", "summarise these results", "compare these runs", or wants distance/speed/lane metrics from a recording.

日本語の概要は準備中です。原文の説明を表示しています。

Installs Scenic, the probabilistic scenario description language, for the CARLA in use — pip installing it into the same interpreter that holds the CARLA client, optionally cloning the upstream repo for the example scenarios the wheel does not ship, and verifying that the `scenic` CLI, the client and Scenic's version-keyed blueprint table all agree. Use when the user asks to "install Scenic", "set up Scenic", "download Scenic", "pip install scenic", wants to run `.scenic` files, or has a Scenic whose model import or blueprint lookup fails.

日本語の概要は準備中です。原文の説明を表示しています。

Writes a new ScenarioRunner scenario — a Python class deriving from BasicScenario with its behaviour tree assembled from srunner atomic behaviours and criteria, plus the XML config that gives it a town, ego spawn and parameters — and registers it so `--scenario MyScenario_1` works. Also covers adding a scenario to a route via --additionalScenario. Generates a working skeleton and validates it without a simulator. Use when the user asks to "create/write a new scenario", "add a custom scenario", "make my own traffic situation", or wants to extend the scenario library.

日本語の概要は準備中です。原文の説明を表示しています。

Writes a new Scenic scenario (.scenic) from a natural-language description — "a car cuts in from the right lane", "a pedestrian crosses in front of the ego", "negotiate an unsignalized four-way" — by turning the request into a spec, generating the file from idioms proven on this build, and validating it by compiling and sampling before any simulator is involved. Use when the user asks to "write a Scenic scenario", "create a .scenic file", "turn this description into Scenic", "generate a scenario from text", or wants a Scenic scenario authored or repaired.

日本語の概要は準備中です。原文の説明を表示しています。

Spawns pedestrians that wander autonomously via WalkerAIController, and destroys them in the correct order. Pairs each walker.pedestrian.* with a controller.ai.walker, places them on the navigation mesh, and sends them to random navmesh points at walking speed. Use when the user asks to "spawn pedestrians/walkers", "add people/crowds", "make pedestrians walk around", or "remove the walkers". Reads the world settings first: if the world is already synchronous it never ticks it, and if it is asynchronous it makes it synchronous and drives the clock itself.

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Build crafted, highly detailed, interactive isometric SVG figures that explain a technical idea as a real physical object (a test rig, a dial gauge, a cabinet of drawers, a plotter, a lock), with many small precise solid parts, four-tone shading, correct painter's order, a framed card with title, hint and live readout, and calm eased motion. Use this whenever someone wants an isometric illustration, an explanatory diagram or figure for docs, a blog post, a landing page or an article, a "Linear-style" or "premium" technical drawing, an interactive explainer, or an SVG that shows how a system, algorithm or component works, even if they never say "isometric". Works framework-free (TypeScript/JS that writes SVG and HTML) and in React/Next.js, and can add realistic WebGL shaders (fire, exhaust, water, caustics, lamp light) that live inside the drawing in the same isometric perspective. Also publishes a finished figure to the Anatomy community gallery when asked.

日本語の概要は準備中です。原文の説明を表示しています。

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

日本語の概要は準備中です。原文の説明を表示しています。

Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.

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Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

日本語の概要は準備中です。原文の説明を表示しています。