「apt」の検索結果

Adapts an existing K-12 lesson (math, ELA, science, or social studies) for students at different proficiency levels (below / at / above grade level). Load this skill BEFORE asking the teacher any clarifying question about the lesson, tiers, or student levels. Triggers on explicit asks to differentiate, tier, or scaffold a lesson, and on implicit signals like "my students are at different levels". Produces 1 teacher-facing differentiation plan + 3 student-ready tier documents as editable, printable documents in a single output turn (shared content is written once, so tiers cannot drift). Uses the Learning Commons Knowledge Graph when connected; works without it. This skill adapts a lesson the teacher brings or names. Not for creating a new lesson from scratch — a new-lesson request that asks for differentiated or leveled materials is k12-lesson-plan-creation's job, one package. Not for grading, rubrics, assessment feedback, or quizzes.

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。

Capture learnings from the current session into the project-rules file (AGENTS.md, CLAUDE.md, or local override) so future sessions benefit. Use when the user says "revise the rules", "update AGENTS.md / CLAUDE.md with what we just learned", "save this to project memory", "remember this for next time", or at the end of a productive session when valuable context has emerged that is not yet documented. This complements agents-md-improver — improver audits, while this one captures.

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。

한국어 canonical 문서·UX copy를 EN/JA/ZH-CN/ZH-TW로 번역이 아니라 locale adaptation한다. thesis·사실·명령어는 보존하되 문장 순서, 주어, register, 제품 용어와 호흡은 각 언어에서 다시 쓴다. '다국어 적용', '영문/일문/간체/대만어 버전', 'locale 문서 만들어줘' 요청에 사용한다.

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-focused differential code review with blast radius analysis, risk-adaptive depth (DEEP/FOCUSED/SURGICAL), git history correlation, and structured finding format. Adapted from Trail of Bits. Use when reviewing PRs, commits, or code changes for security implications.

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。

Implements media and file management components including file upload (drag-drop, multi-file, resumable), image galleries (lightbox, carousel, masonry), video players (custom controls, captions, adaptive streaming), audio players (waveform, playlists), document viewers (PDF, Office), and optimization strategies (compression, responsive images, lazy loading, CDN). Use when handling files, displaying media, or building rich content experiences.

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 chapter-level briefs (`outline/section_briefs.jsonl`) from chapter skeleton plus section bindings before stable H3 decomposition.

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 fix broken OpenCLI adapters when commands fail. Load this skill when an opencli command fails — it guides you through collecting a trace artifact, patching the adapter, retrying, and filing an upstream GitHub issue after a verified fix. Works with any AI agent.

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 on the new System.Diagnostics.Process APIs introduced in .NET 11: Process.Run, Process.RunAndCaptureText, Process.StartAndForget, Process.ReadAllText/Bytes/Lines, KillOnParentExit, InheritedHandles, and StartDetached. USE FOR: starting, orchestrating, or capturing output from external processes in applications targeting net11.0 or later. DO NOT USE FOR: applications targeting .NET 10 or earlier, or basic pre-.NET 11 Process.Start usage without new APIs.

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。

Write, run, or repair .NET tests that use NUnit. Use when a repo uses `NUnit`, `[Test]`, `[TestCase]`, `[TestFixture]`, or NUnit3TestAdapter for VSTest or Microsoft.Testing.Platform execution. USE FOR: writing or reviewing NUnit tests; using [Test], [TestCase], [TestFixture], [SetUp], [TearDown] attributes; configuring NUnit3TestAdapter or NUnit.Analyzers. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

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。

snapDOM is a fast, accurate DOM-to-image capture tool that converts HTML elements into scalable SVG images. Use for capturing HTML elements, converting DOM to images (SVG, PNG, JPG, WebP), preserving styles, fonts, and pseudo-elements.

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 compliant Chinese mathematical-modeling problem-restatement chapter from a complete problem statement, or review a user's restatement against the original problem. Covers research background, problem review, and research overview; does not perform the separate problem-analysis chapter.

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。

Write a handoff file at .turbo/handoff/<YYYY-MM-DD>-<slug>.md capturing current session state — task, status, open decisions, in-flight changes, next step — so a fresh session can continue without re-deriving context. Use when the user asks to "create a handoff", "create handoff", "save handoff", "handoff before compact", "save session state", "handoff for next session", or "capture session 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。

Audit and rewrite an Instagram profile end-to-end for 2026: profile photo, searchable NAME field weighted with a keyword, @handle, bio (150 chars: value plus topic plus proof), goal-matched link, category label, story highlights, the first-9 grid, and up to 3 pinned posts. Triggers on "review my Instagram profile", "fix my bio", "optimize highlights", "profile audit". The whole follow decision happens on the profile header. Not for writing captions (use ig-caption-writer).

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 weekly Instagram content plan from a theme, audience, and content pillars. Produces a per-day mix of Reels, carousels, single images, and stories, each with a hook formula, angle, posting time, and primary goal, plus a weekly saves-and-shares goal (the 2026 lever), a story cadence, and a balance check across saves, shares, comments, and follows. Use to plan a week instead of ad-hoc posting. Not for drafting one caption (use ig-caption-writer) or one carousel (use ig-carousel-planner).

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。

Reverse-engineer the hook from a viral Instagram Reel or carousel, given a URL or a pasted caption. Identifies which of the 10 canonical 2026 formulas it uses (number-first, contrarian, relatable, confession, listicle, before/after, myth-buster, framework, pattern-interrupt Reel, how-I Reel), explains why it worked, names the primary goal, and returns a blank template mapped to your topic. Use to learn from a post you admire. Not for writing your own (use ig-caption-writer or ig-carousel-planner).

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。

Read your Instagram niche and profile from real data via Apify, no login. Scan a hashtag for the posts traveling now (likes, comments, owner) to see the format and hook that works. Pull profile stats for any handle, yours or a competitor's: followers, posts, bio, category. Instagram hides who liked or commented on other accounts, so this is discovery plus profiles, not engagers. Triggers on "what works in my niche", "scan the hashtag", "competitor stats". Not for writing captions (use ig-caption-writer).

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。

All-in-one FASTQ QC and adapter trimming. Auto-detects Illumina adapters, filters low-quality reads, corrects paired-end overlaps, emits HTML+JSON QC in one pass. 3-10x faster than Trim Galore/Trimmomatic. First step before STAR, BWA-MEM2, or Salmon.

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。

API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.

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。

Capture one supplied product URL as auditable, unadopted FF&E evidence and inspect its retained history. Use to clip a page or review prior captures; not to save a library product or adopt a schedule 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。

Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows, publication-ready matplotlib house style, and layout using routed visual-evidence checks. Use when checking or creating plot readability, axes, legends, line overlap, table structure, metric reporting, captions, grayscale accessibility, IEEE one-column/two-column layout, figure/table placement, claim-evidence mapping, reviewer first-impression risks, matplotlib/seaborn or ggplot2 figure code, SVG/PDF/TIFF/PNG export, Illustrator/Inkscape/PowerPoint/Figma finishing plans, or IEEE-style engineering evidence graphics.

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 you need to CAPTURE / PRODUCE XProf (jax.profiler) profiling artifacts from sgl-jax (SGLang on JAX/TPU) for later performance analysis: driving a live server (/start_profile, stage-separated prefill/decode), offline bench_one_batch --profile, or kernel microbench. This skill produces the trace files; it does not analyze them.

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 implementation patterns for Clean Architecture, Domain-Driven Design (DDD), and Hexagonal Architecture (Ports & Adapters) in NestJS/TypeScript applications. Use when structuring complex backend systems, designing domain layers with entities/value objects/aggregates, implementing ports and adapters, creating use cases, or refactoring from anemic models to rich domain models with dependency inversion.

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 implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Java 21+ Spring Boot 3.5+ applications. Use when structuring layered architectures, separating domain logic from frameworks, implementing ports and adapters, creating entities/value objects/aggregates, or refactoring monolithic codebases for testability and maintainability.

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。