「rl」の検索結果

Fix iOS AVPlayer "CoreMediaErrorDomain error -12939 - byte range length mismatch" or similar video streaming failures when a proxy/edge/CDN service sits between the client and object storage (GCS, S3, R2). Use when: (1) iOS video fails with -12939 "byte range length mismatch - should be length 2 is length N", (2) curl with Range: bytes=0-1 returns full file instead of 2 bytes, (3) one URL path works with Range but another path to the same storage doesn't, (4) proxy advertises Accept-Ranges: bytes but returns 200 with full file for range requests. The root cause is typically a proxy handler that constructs a new request to the backend without forwarding the client's Range header.

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。

Fix invisible or poorly visible SVG icons in Flutter when using flutter_svg package. Use when: (1) SVG icons appear invisible despite correct colors in the SVG file, (2) Using Opacity widget to change icon visibility/state but icons don't render properly, (3) Tab bar or navigation icons disappear or have wrong colors, (4) Icons with fill="white" don't show on dark backgrounds. The fix is to use ColorFilter.mode() instead of Opacity widget for SVG icon state styling.

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。

Add any Hugging Face image or video model, checkpoint or LoRA to Guaardvark from a URL, list what is installed, and download registry models on request. Use when the user pastes a huggingface.co link or a .safetensors URL, asks "can it run <model>", or wants a new LoRA or checkpoint available in the Studio.

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 professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.

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。

Semi-automated fact-checking, source credibility evaluation, misinformation detection, prebunking, and evidence-ledger based claim verification. Use when a user asks to verify a claim, article, social post, screenshot, URL, quote, data point, compare sources, detect manipulation, evaluate source reliability, or generate an HTML fact-check card/share-safe correction. Supports text, URLs, screenshots/images, two-source comparison, and topic-level prebunking in Bulgarian, English, Russian, EU policy, health, science, and geopolitical contexts. Trigger phrases: "is this true?", "is this fake?", "fact check this", "compare these sources", "проверка на факти", "истина ли е", "дезинформация ли е", "сравни тези източници".

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。

Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.

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。

A terminal-native marketing system rendered entirely in Berkeley Mono — every word on the page, from the hero headline down to the footer fine print, is monospaced. The page itself reads like a manpage or a static-site README: warm cream canvas (`#fdfcfc`), nearly-black ink (`#201d1d`), 4px-radius rectangles for the few interactive elements, and bracketed `[+]`/`[-]` ASCII markers used as bullets. The brand's only "visual moment" is a single dark hero card that mocks up the OpenCode TUI itself — black background, monospaced terminal output, ASCII pipe characters, and a wordmark rendered as block-pixel ASCII. Every section sits as a hairline-bordered text block on the cream canvas with no shadows, no gradients, no decorative imagery, and no non-monospaced character anywhere in the system.

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。

Svelte を使う外部リポジトリ（ツール / ライブラリ / アプリ）を git submodule として取り込み、Svelte コンパイラを rsvelte に差し替えても完全に同等に動作するかを検証する。既存のサブモジュールが指定された場合は最新化したうえで再検証し、回帰があれば rsvelte 側を自動修正する。「/verify-svelte-compat <url-or-name>」で実行。

Think and work like an expert Limnologist. Use when a task calls for Limnologist judgment. Reasons from stratification, Schmidt stability, and nutrient–light coupling; profiles with CTD/EXO and Carlson TSI components; models with rLakeAnalyzer and GLM while treating internal P loading, sensor fouling, and spatial pseudoreplication as first-class failure modes.

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。

Think and work like an expert Critical Care Researcher. Use when a task calls for Critical Care Researcher judgment. Reasons from acute physiology trajectories, modular organ dysfunction, and cluster-aware trial design through APACHE/SAPS/SOFA scoring, Berlin/Sepsis-3/KDIGO definitions, MIMIC-IV phenotyping, and target-trial emulation with clone-censor-weighting while treating immortal-time bias, cluster contamination, competing risks from early death, and sepsis-phenotype cohort inflation as first-class failure modes.

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。

Think and work like an expert Climate Risk Analyst. Use when a task calls for Climate Risk Analyst judgment. Reasons from TCFD/IFRS S2 four-pillar disclosure, NGFS Phase V orderly/disorderly/hot-house scenarios, hazard–exposure–vulnerability and RMS/AIR/CLIMADA cat economics, transition carbon-price and stranded-asset pathways, and PCAF financed emissions while treating exposure geocoding quality, Kotz damage-function retraction, and scenario-not-forecast misuse as first-class failure modes.

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。

Earnings preview deck, quarterly earnings presentation, earnings summary slides, consensus vs actual presentation, earnings preview report, pre-earnings analysis, earnings expectations deck, quarterly preview, upcoming earnings summary, earnings announcement preview

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 3D assets with Tencent's Hunyuan3D family — open-weight self-hosted models (Hunyuan3D-2.0/2.1 and HunyuanWorld / HY-World for scenes) and the hosted, closed API tiers (2.5, PolyGen, 3.0/3.1 via Tencent Cloud and third-party hosts). Use when a task involves image-to-3D or text-to-3D mesh generation, the shape-then-texture (DiT + Paint / PBR) workflow, choosing between self-hosting and a hosted API, checking the Community License's Territory (EU/UK/South Korea) and 1M-MAU commercial gate, sizing GPU/VRAM for local inference, ComfyUI integration, or reviewing and post-processing (retopology, UVs, decimation) the meshes these models produce. Not for image, video, or general LLM tasks.

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 academic papers, scholarly articles, and research publications through SandBase. Use when asked for literature review, academic citations, scholarly research, paper discovery, or scientific evidence gathering.

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 changing REPL clear/reset/resume/surface behavior (onClearTerminal, transcriptSeq remount, Ink Static paths, Ctrl+O/Ctrl+E transitions). Enforces single-owner reset transaction and prevents black-screen/flicker/duplicate-row regressions.

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 Rust TUIs from ratatui (or cursive, Python textual) to SuperLightTUI v0.23. Use when porting an existing TUI codebase to SLT, or when the user asks "how do I do X from ratatui in SLT". Korean triggers "ratatui 마이그레이션", "SLT로 포팅", "이걸 SLT로".

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 Rust TUI apps with SuperLightTUI v0.23 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.

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 Rust TUIs from ratatui (or cursive, Python textual) to SuperLightTUI v0.20. Use when porting an existing TUI codebase to SLT, or when the user asks "how do I do X from ratatui in SLT". Korean triggers "ratatui 마이그레이션", "SLT로 포팅", "이걸 SLT로".

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 Rust TUI apps with SuperLightTUI v0.20 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.

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。

技術記事の内容から読者の理解度をチェックする4択クイズを指定のYAML形式で生成するスキル。ユーザーが「この記事のクイズを作って」「理解度チェックを作成して」「記事を読んだ人向けの問題を作って」「selfAssessment形式でクイズを作って」と依頼したときに必ず使う。ブログ記事、技術ドキュメント、チュートリアル、解説記事、技術書の章など、読者の学習内容を確認する問題を作る必要があるあらゆる場面で使う。記事のURL・マークダウン・テキスト・ファイルパスのいずれを渡されても動作する。

Google and Meta paid media account structure evaluation. Audits campaign/ad set architecture against conversion volume minimums, budget thresholds, and targeting overlap. Identifies over-segmentation, under-segmentation, budget fragmentation, and structural anti-patterns blocking algorithmic learning. Provides consolidation roadmaps with migration plans. Use when inheriting accounts, quarterly health checks, or before scaling budgets.

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。

You are an expert in Drizzle ORM, the lightweight TypeScript ORM that maps directly to SQL. You help developers write type-safe database queries that look like SQL (not a new query language), generate migrations from schema changes, and deploy to serverless environments with zero overhead — supporting Postgres, MySQL, SQLite, Turso, Neon, PlanetScale, and Cloudflare D1.

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。

Elixir is a functional language on the Erlang VM (BEAM) for concurrent, fault-tolerant and distributed systems. Use when someone asks to "write Elixir", "build a Phoenix app", "Phoenix LiveView", "GenServer", "supervision tree", "Ecto schema or changeset", "mix project", or needs lightweight processes, "let it crash" reliability and real-time web UIs.

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。

You are an expert in Amazon EventBridge, the serverless event bus for building event-driven architectures. You help developers route events between AWS services, SaaS applications, and custom microservices using event rules, patterns, transformations, dead-letter queues, and scheduling — decoupling producers from consumers with content-based routing that scales automatically.

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。