「ak sk」の検索結果

ego-browser (ego-lite) is a Chromium-based browser that gives AI agents a CLI-accessible Node.js runtime for driving a real browser. Use this skill whenever the user needs to interact with a website opening pages, filling forms, clicking buttons, taking screenshots, extracting page data, testing web apps, logging into sites, automating browser operations, or any other browser automation task. Triggers include requests to "open a website", "visit a URL", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "extract content from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also used for exploratory testing, dogfooding, QA, bug hunting, or reviewing app quality. Prefer ego-browser over any built-in browser automation, web fetch, or other web tools.

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Split user stories into smaller, independently deliverable slices using the SPIDR method (Spike, Path, Interface, Data, Rules). Use this skill whenever the user asks to split a story, break down work, slice a story, asks "can this be split", mentions "SPIDR", says "story splitting", or wants to decompose a large ticket into smaller pieces. Also trigger when a user presents a story that seems too large for a single sprint and asks for help sizing or scoping 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。

Metakognitive Injektoren und Selbstgesprächs-Strategien (Self-Talk, Metacognitive Auditing, Evaluator Hooks, Pre-Flight Checklists, State Persistence & Personas). Integriert Aktive Informationssuche (Systemgedächtnis Gardener & USMC), Rehearsal (Aktives Abrufen & Wiedergeben), Baddeleys Arbeitsgedächtnis (State + Hooks), Miyake Exekutivfunktionen sowie CBT/ACT therapeutische Strategien.

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Einmaliger Tasksolver+Writer+Maintainer-Durchlauf fuer den Projektordner, in dem die Session gerade sowieso gearbeitet hat (inkl. Unterordner) — kein systemweiter Sweep. Loest offene triviale/autonome Register-Punkte, aktualisiert Dokumentation auf den gemessenen Ist-Stand (im Projekt UND in der Root der Pipeline, in der das Projekt liegt) und raeumt Strays/Temp- Dateien nach dem Papierkorb-Prinzip weg. Nutze diesen Skill bei /tidy-up, "raeum das Projekt auf", "mach hier sauber bevor wir weitermachen", am Ende einer Arbeitssitzung an einem Projekt, oder als faellige autonome Aufgabe innerhalb von /work-autonomous + /goal.

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。

Drafts one weekly status update or team status email (done, in progress, blocked, next, asks) from the notes, task lists, tracker exports and messages the user attaches, pastes or makes reachable, every bullet traced to a source and every gap marked UNKNOWN, returned in the chat as Markdown for the user to review and send. Use when the user asks to "write my weekly status update", "draft the team status email", "turn my notes into a status report", "what did we get done this week", "summarise this week for my manager" or "prepare Friday's update". Do not use for maintaining a project's living logs or a project-level report built from them, use project-status-tracker instead. Drafts for human review; never approves, authorises or signs off.

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。

Builds a self-contained single-file HTML project dashboard (status banner, milestones table, risks heat list, decisions log, links) from the project tracker files the user attaches, pastes or holds in configured knowledge sources, and returns the complete HTML source in chat ready to save as dashboard-project-date.html. Use when the user asks to "build a dashboard for this project", "make a status page I can pin in the team channel", "give me an HTML overview of milestones, risks and decisions", "refresh the project dashboard from the tracker" or "turn these tracker files into a one-page visual summary". Do not use for creating or updating the tracker files or for the written weekly status report, use project-status-tracker instead. Drafts for human review; never approves, authorises or signs off.

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。

Prepares a DRAFT leadership-meeting briefing pack from the reports, meeting notes, emails and dashboard exports the user provides: one headline, metrics exactly as stated with period and source, decisions the meeting must take, risks as reported, talking points with anticipated questions, a source reference on every line and UNKNOWN where the material is silent. Use when the user asks to "brief the leadership team", "prepare the exec pack", "pull together a briefing for the steering committee", "summarise these reports for the CEO", "what do I tell the board on Monday" or "build the pre-read for the management meeting". Do not use for a formal board or committee paper seeking a resolution, use board-paper-skeleton instead; for a personal one-page prep drawn from the user's own calendar and inbox, use meeting-prep-onepager. Drafts for human review; never approves, authorises or signs off.

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 a complete but efficient Nature-style Chinese PPTX presentation from a remote-sensing, Earth-observation, geospatial AI, or broader scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department report, or academic presentation from a remote-sensing research paper. It identifies the paper type and argument, selects only the figures needed for the story, writes Chinese slide content and speaker notes, creates the actual .pptx deck, and runs an explicit self-review/corrective revision loop focused on figure quality, text overflow prevention, and non-template visual design before delivery. Also trigger on general academic-presentation requests even without the word "Nature", such as turning a paper into slides, building an academic talk deck, conference/defense presentations, and Chinese phrasings like 论文做PPT、遥感论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、讲paper、读书报告PPT.

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。

Submission-grade remote-sensing Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, map-led composites, remote-sensing image plates, sensor/product workflow figures, validation/error/uncertainty panels, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family, Remote Sensing of Environment, ISPRS, IEEE TGRS, or other high-impact venues. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, and Chinese phrasings like 遥感论文配图、学术写作配图、科研绘图、科研作图、地图出图、论文图表、可视化.

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 full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract figures or tables into the right positions, preserve figure/table placement near relevant prose, or keep exact source anchors for every block. This skill must not degrade into a summary-only output unless the user explicitly asks for a summary. Also trigger on general paper-reading and translation requests even without the word "Nature", such as reading/translating an academic paper, literature reading, understanding a paper, and Chinese phrasings like 读论文、精读论文、论文翻译、文献翻译、文献阅读、学术阅读、帮我读这篇文章、翻译这篇paper.

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。

Generate or audit an `/llms.txt` file at the site root that makes the site legible to LLMs and AI answer engines at inference time, following the llms.txt proposal (Jeremy Howard, 2024). Use this skill whenever the user asks to "build llms.txt", "create llms.txt", "generate llms.txt", "audit llms.txt", "make my site LLM-friendly", "expose the site to AI assistants", "let ChatGPT / Claude / Perplexity cite my site", "add an AI assistants index", or asks about `.md` companion pages for LLM consumption. Output is a valid `llms.txt` file (or an audit) plus optional `.md` companion page generation for top pages.

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。

差分が「その差分自身を審査するレビュー基準・品質ゲート」を弱めていないかを diff-time で検出する。Check 1 レビュールールの削除・弱体化（.river/rules.md / .river/rules.d/*）、Check 2 実行時コンフィグの閾値・ゲート緩和（.river-review.{json,yaml,yml} の review.severity 引き下げ / exclude 拡大 / memory.suppressionEnabled 無効化 / selection.skills.exclude 追加）、Check 3 suppression entry の新規追加、Check 4 lint・静的解析設定からのルール削除や無効化、Check 5 branch protection・required check の緩和、の 5 Check を対象とし、これらが機能変更と同一 PR に混在し、かつ意図の宣言が無い場合に指摘する。report-only。workflow の permissions / action pin は gha-workflow-security、既存 suppression entry の使い分けは suppression-feedback、設定ファイルの一般的妥当性は config-json、.only / .skip / @ts-ignore / @ts-nocheck は heuristic-review.mjs の決定論検出器、リファクタと機能追加の混在は behavior-structure-separation へ委譲する

指摘対応の反復（レビューコメント→修正→再レビュー）で、個別には正しい修正の蓄積が当初スコープを逸脱した（scope creep）、または成立済みの前提（"動いていた"状態）を破壊した（premise break）連鎖を diff-time で検出する。技術的正しさとスコープ整合性を別軸で評価し、report-only で finding/question のみ出力する。1コメント単位のトリアージ・構造変更後の caller 残骸・plan 前提解消は隣接 skill へ委譲する

敵対的分析手法を統合したレビューの entry skill。認知バイアス対策の3手法 （Pre-mortem / War Game / Logic Torturing）と、宣言・主張と実態の乖離を突く claim-vs-actual 検出3パターン（Self-Contradiction / Refactor-Claim Audit / Cross-File Leakage）へルーティングし、通常のレビューでは見えない設計の盲点・ 防御の穴・論理の弱点・宣言と実装のズレを可視化する。

Detects patterns that make tests flaky and proposes stabilization steps.

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。

Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform. Use for /create-verification-skill, "make a control skill for this repo", or when a project has no scripted way to prove UI/CLI/service behavior.

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。

Background processing and heavy computations using Dart Isolates with Isolate.run(), compute() function, and platform channels. Use this skill when performing CPU-intensive operations without blocking UI (image processing, data parsing, encryption), implementing background tasks that prevent jank or frame drops, handling large file operations (JSON parsing, database migrations), running parallel computations, fixing UI freezes caused by main thread blocking, using compute() for one-off background work, creating long-lived Isolates with ReceivePort/SendPort, implementing multi-threaded algorithms, debugging isolate communication issues, or preventing memory leaks from unclosed ReceivePorts. Covers isolate spawn, message passing, ReceivePort cleanup (CRITICAL for leak prevention), SendPort communication patterns, error handling, serialization rules, and performance optimization for compute-heavy operations.

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。

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

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 alongside figma-use when the task involves translating an application page, view, or multi-section layout into Figma. Triggers: 'write to Figma', 'create in Figma from code', 'push page to Figma', 'take this app/page and build it in Figma', 'create a screen', 'build a landing page in Figma', 'update the Figma screen to match code', 'convert this modal/dialog/drawer/panel to Figma'. This is the preferred workflow skill whenever the user wants to build or update a full page, modal, dialog, drawer, sidebar, panel, or any composed multi-section view in Figma from code or a description. Discovers design system components, variables, and styles from Code Connect files, existing screens, and library search, then imports them and assembles views incrementally section-by-section using design system tokens instead of hardcoded values.

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。

Control local apps through Computer Use (the @oai/sky runtime) inside the Codex app. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, and the user asks to control the computer, operate a desktop app's UI, use Safari or Chrome through computer use, click or type in an app, or take a screenshot of an app. Prefer purpose-built connectors, APIs, or CLIs when they exist.

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。

Manage breaking API changes, migration guides, deprecation notices, and semver versioning. Use when introducing breaking changes, writing migration paths, updating changelogs, or releasing major versions.

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。

Migrate a .NET 10 project or solution to .NET 11 and resolve all breaking changes. This is a MIGRATION skill — use it when upgrading from .NET 10 to .NET 11, NOT for writing new programs. USE FOR: upgrading TargetFramework from net10.0 to net11.0, fixing build errors after updating the .NET 11 SDK, resolving source-breaking and behavioral changes in .NET 11 runtime, C# 15 compiler, and EF Core 11, adapting to updated minimum hardware requirements (x86-64-v2, Arm64 LSE), and updating CI/CD pipelines and Dockerfiles for .NET 11. DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 9 or earlier, greenfield .NET 11 projects, or cosmetic modernization unrelated to the upgrade. NOTE: .NET 11 is in preview. Covers breaking changes through Preview 3.

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 taste through volume of output, deliberate failure, and speed. Taste cannot be developed by watching. It's sharpened by making, failing, and making again. Use when designing practice routines, breaking through creative blocks, or helping someone move from analysis to execution.

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。

Generate an AI video end-to-end on Ozor.ai from a short text brief. Use this skill whenever the user wants to actually create a video (not just write a prompt) from a creative description, idea, or short brief — e.g. 'make me a video about X', 'generate a launch video for Y', 'create an explainer for Z', 'render a video', 'produce a 30s ad about ...', 'spin up a quick video for our new feature'. This skill runs the full Ozor MCP pipeline: generate_video → wait_for_job → export_video → wait_for_export → get_embed_code, and returns the shareUrl, editorUrl, downloadUrl, and a ready-to-paste <iframe> embed. Do NOT use this skill when the input is a document/PDF/PPTX/deck/URL — use ozor-document-video or ozor-url-to-video 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。