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概要と使いどころ
Retrieve the list of root GameObjects in the specified opened scene (or the active scene when `openedSceneName` is empty). Supports token-saving path-scoped reads over the root-GameObjects array via `paths` or `viewQuery`. Use 'scene-list-opened' to enumerate scenes.
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。
Search prompt history and manage starred reusable prompts in BB.
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。
Move incoming issues and pull requests through triage states until each is actionable or closed — use when the queue has piled up or a report arrives unsorted
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 to cut an APM release from the current worktree: assess whether the cycle since the last tag warrants a patch or minor bump (semver discipline against the merged-since-last-tag diff), sanitize the [Unreleased] CHANGELOG block into a dated version block with one concise "so what" entry per merged PR (drop internal-only churn, consolidate duplicates), bump pyproject.toml + uv.lock, run the CI-mirror lint chain, and open the release PR. Activate on "ship a release", "cut v0.x", "release prep", "bump and PR", "open release PR", "what kind of release do we need", or any phrasing that ends in opening a release PR -- even when the user does not say "skill". Stops BEFORE tagging; tagging stays a human gate that triggers the release workflow. Refuses to bump to a major (>= 1.0.0) version without explicit operator confirmation.
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 to translate a classifier's in-place verdict into a precise, page-by-page work plan for the docs-sync panel. Activate after docs-impact-classifier returns verdict in_place; reads the candidate page list, fetches the actual page contents, narrows scope to specific sections within each page, and emits the per-page task brief the panel fans out against.
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 when the docs-impact-classifier returns a structural verdict, signalling that the documentation TOC must change to accommodate the PR. Proposes TOC deltas (new pages, moves, merges) and emits new-page outline stubs that the doc-sync panel later fleshes out. Holds the 3-promise narrative (consume / produce / govern) and the persona ramps as hard constraints.
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。
Implement and review React, React Native, and TypeScript UI for maximum practical render performance through leaf-local updates, stable identities, honest Hook semantics, and minimal effects. Use for components, hooks, callbacks, context, external stores, memoization, list rows, large dependency arrays, callback or state fanout, re-render reduction, and React Compiler-aware performance work.
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。
Build, validate, publish, update, inspect, query, roll back, or archive a dynamic Marina applet. Use for Artifact+-style web apps that should run behind Marina authentication, carry small bundled assets or data, use an applet-owned Postgres schema, or add a trusted Python ASGI backend without a Marina image deployment.
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 Iris to submit, inspect, debug, monitor, or recover jobs and tasks; diagnose scheduling and federation; deploy controllers; or reserve dev GPUs and TPUs. Use for ordinary Iris operations, including babysitting a job, controller rollouts, accelerator sessions, and stuck CoreWeave pods. Use a narrower production-run or Zephyr skill when that workflow is explicitly requested.
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。
Design or revise a Marin experiment pipeline that binds reusable work into an artifact graph. Use for new workflow entrypoints, stage wiring, and pipeline placement decisions; use a narrower domain skill when it owns the launch.
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。
Carry implementation through an isolated worktree and local handoff. Use only when this skill is explicitly invoked.
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。
求是方法论的入口与总原则。在每次新的顶层会话开始时加载一次，用「实事求是」约束全部判断，并只在明确适用时把任务路由到对应的方法论 skill；被派遣执行单一具体任务的子 agent 直接跳过。 English: Entry point and governing principle of the Qiushi methodology. Load once at the start of a top-level session to enforce "seek truth from facts" and route to a downstream skill only when it clearly helps. Delegated sub-agents on narrow tasks should skip it.
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。
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.
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。
Split datasets into training, validation, and test partitions with the right stratification and temporal rules. Use as a narrow preprocessing helper once the broader ML workflow is already chosen, not as the main route owner for an end-to-end ML task.
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Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
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 when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.
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Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.
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Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
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。
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
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。
Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.
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。
Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.
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。
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
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。
Comprehensive data visualization toolkit for creating beautiful, mathematically elegant visualizations with D3.js, Chart.js, and custom SVG. Use when (1) building interactive data visualizations, (2) designing color palettes for charts, (3) choosing scales and visual encodings, (4) creating data pipelines from Census/SEC/Wikipedia APIs, (5) crafting narrative-driven data stories, (6) making perceptually accurate charts, or (7) implementing force-directed networks, timelines, or geographic maps.
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。