gabrielmoreira

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas.

Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow.

Use when the user requests diagrams, flowcharts, architecture diagrams, ER diagrams, UML / sequence / class diagrams, network topology, ML/DL model figures (Transformer/CNN/LSTM), mind maps, or any visualization. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Best suited when the diagram needs custom styling, rich shape vocabulary, swimlanes, or exportable images (PNG/SVG/PDF/JPG). Generates .drawio XML and exports locally via the native draw.io desktop CLI.

Unity Editor automation via REST API — create scripts, analyze scenes, manage assets, control editor, orchestrate workflows, run batch jobs, and route by Unity version. Triggers: Unity, Unity Skills, in Unity, automate Unity, Unity editor automation, create Unity script, scene summary, analyze Unity scene, Unity GameObject, Unity prefab, Unity asset, Unity workflow, Unity batch, grant Unity skill, Unity allowlist, Unity skill schema, multi-instance Unity, set Unity version, Unity自动化, Unity编辑器, Unity技能, 操作Unity, 在Unity中, Unity工作流, Unity批处理, Unity授权, Unity白名单, Unity多实例, Unity实例切换, Unity场景分析.

Index of Unity Skills functional modules and advisory design modules. Browse available skill modules, check operating-mode requirements (Approval/Auto/Bypass), and pick the right module for the task. Triggers: Unity module list, Unity skill index, browse Unity skills, find Unity module, Unity module catalog, Unity skill categories, mode requirements, Unity advisory modules, Unity 模块列表, Unity 技能索引, 浏览 Unity 技能, 查找 Unity 模块, Unity 模块目录, Unity 技能分类, 模式要求, Unity 设计模块.

Unity UGUI (Canvas-based UI) creation and layout: Canvas, Panel, Button, Text (TMP auto-detect), Image, RawImage, InputField, Slider, Toggle, Dropdown, ScrollView, Scrollbar, RectTransform anchors and layout groups. This is the GameObject + Canvas system; for UXML/USS use the `uitoolkit` module. Triggers: UGUI, UI, Canvas, ScreenSpaceOverlay, Button, Text, TextMeshPro, TMP, Image, RawImage, Panel, Slider, Toggle, Dropdown, ScrollView, ScrollRect, Scrollbar, InputField, RectTransform, anchor, pivot, layout group, vertical layout, horizontal layout, grid layout, LayoutElement, CanvasGroup, mask, RectMask2D, outline, shadow, 界面, 按钮, 文本, 文字, 图片, 面板, 滑块, 开关, 下拉, 滚动视图, 滚动条, 输入框, 锚点, 布局, 画布, 蒙版.

Predict enhancer activity in DNA sequences using the Genomic Intelligence G0 DeepSTARR model, via the hosted /v1/tasks/enhancer/predict API. Returns per-window activity scores.

Use when designing, reviewing, refactoring, or implementing code that should stay simple, testable, readable, and sustainable. Applies principles for investigating before changes, keeping few layers, making I/O explicit, using dependencies deliberately, organizing by feature, naming consistently, and presenting plans progressively.

Makes answers easier to start reading, scan, resume, and understand without losing important detail. Uses useful-answer-first structure, short paragraphs, clear literal headings, simple language, preserved nuance, natural voice, and small ASCII diagrams when they clarify structure or flow.

Use when working on TypeScript code and needing to choose the smallest focused TypeScript skill for coding standards, boundaries, composition, configs, async control, error handling, observability, security, or testing.

Use when TypeScript code needs to express failure with a canonical app-owned error model, family-level runtime wrappers or Result-based propagation, stable error codes, retry classification, and safe boundary translation.

Use when TypeScript code coordinates async work — Promise.all vs sequential, bounded concurrency, AbortSignal cancellation, cleanup/teardown, or process lifecycle (SIGTERM, graceful shutdown).

Use when TypeScript code moves provider, SDK, API, request, response, generated, or raw input shapes across owned application boundaries.

Use when TypeScript work involves naming, abstractions, classes, local reasoning, semantic centers, full cutovers, type assertions, or design cleanup.

Use when TypeScript work involves dependency construction, composition roots, factories, lifecycle, scope, singletons, caching, or runtime provider selection.