「lime」の検索結果

Use when working with Baselime — baselime serverless observability platform for AWS Lambda, CloudFlare Workers, and Vercel functions. Covers log and trace querying, alert management, service discovery, and performance analysis of serverless workloads. Use when investigating serverless function performance, querying Baselime events, managing alerts, or analyzing cold start and duration metrics.

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 Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

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。

Rembric dashboard UI work (dark monochrome zinc surfaces, lime accent, Geist type, Spectrum data-tables) — building / editing / extending pages, styles, components, modals, tables, filters, pagination, mobile responsive, destructive confirmations. Use when the user asks for changes under `apps/web/src/app/dashboard/` or `apps/web/src/components/dashboard/`, mentions Rembric design tokens (lime, zinc, command bar, StatusPill, hl-lime, data-table), or requests a new dashboard page / form / table.

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 Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

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 Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

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 for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

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 for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

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。

Fornece orientação para pós-treinamento de LLM com RL usando slime, um framework Megatron+SGLang. Use ao treinar modelos GLM, implementar fluxos de trabalho personalizados de geração de dados, ou precisar de integração estreita com Megatron-LM para escalabilidade em RL.

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 Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

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。

Run Mom Test–style customer-discovery interviews to validate or kill an unbuilt idea — generate a non-leading interview guide, conduct or coach the conversations, and synthesize signal vs compliments. Load when the user asks to do customer discovery, run problem interviews, validate an idea with users, run a Mom Test, design an interview guide, or says "talk to customers", "validate the problem", "interview users", "Mom Test this", "did real users want it", "synthesize my interviews", "I just talked to N people". Sub-skill of `venture-exploration`. Hard-bans "would you use this?", solution-pitching, friend/family-only ICP, and treating compliments as validation. Calls `secure-*` before synthesizing any pasted external transcripts.

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 for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

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 for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

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。

RL post-training for LLMs with Megatron and SGLang.

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。

Swiss-grid fintech deck template in black / warm paper / neon-lime contrast. Use when users ask for premium data-story slides with strict modular layout, bold numeric cards, restrained motion, and keyboard/click navigation in one HTML file.

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。

Set up and run RuView mmWave / FMCW radar sensing — ESP32-C6 + Seeed MR60BHA2 (60 GHz, heart rate / breathing rate / presence) and HLK-LD2410 (24 GHz, presence + distance), plus mmWave↔WiFi-CSI sensor fusion (48-byte fused vitals, MR60BHA2/LD2410 auto-detect, v0.5.0+). Use when the deployment includes a millimetre-wave radar alongside or instead of WiFi CSI.

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 for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

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 for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

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。

Draws hand-authored SVG spot illustrations in a bold pop-geometric style for design and coding scenes. Identifying traits: a thick navy outline on every shape, solid navy extrusions that always step down-right, saturated flat fills (hot pink, lemon, aqua, violet, mint, lime) textured with navy hatching and dots, and a dense overlapping cluster of chunky props (browser windows, pinwheel tiles, iso cubes, lightning bolts) around one slim cartoon character. Covers palette, line weights, extrusion and union-outline techniques, a JS helper for the repeated marks, characters and hands, composition and a render-and-critique loop. Use when someone asks for bold pop, pop geometric, retro pop, memphis-style, navy-outline, chunky outlined or loud colourful illustrations, or for empty states, onboarding, 404 or error pages, blog headers, feature spots and marketing cards in that look.

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。

Fiscaliste IA pour la fiscalité personnelle des particuliers français : optimisation et déclaration de l'impôt sur le revenu, IFI, revenus du capital, revenus fonciers, equity salarial, crypto-actifs et PER. Couvre le calcul de l'IR (barème, quotient familial, décote, PAS, CEHR, revenus exceptionnels), la déclaration 2042 et annexes, les revenus du capital (PFU vs barème, PEA, assurance-vie, dividendes, plus-values), les revenus fonciers (micro/réel, déficit, LMNP, SCI IR), l'equity startup (RSU, BSPCE, stock-options, PEE/PERCO), la fiscalité crypto (PAMC, 2086), l'IFI et les déductions (PER, pension alimentaire). Triggers: impôt sur le revenu, IR, 2042, quotient familial, décote, PAS, PFU, flat tax, PEA, assurance-vie, LMNP, revenus fonciers, déficit foncier, SCI IR, RSU, BSPCE, stock-options, PEE/PERCO, crypto, 2086, IFI, PER, plafond PER, niche fiscale, optimisation fiscale, simulation IR, TMI. Hors scope : succession/donation (notaire), IS/SASU/arbitrage dividende-salaire/SCI IS (comptable).

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。

Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user-research questions, interpreting ambiguous feedback, or designing customer-discovery that avoids false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.

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 posing a sculpt or preparing it for 3D printing in ZBrush: "pose this character", Transpose Master, TPoseMesh, Gizmo posing with masks, ZSphere rig, Proxy Pose, weapon in the hand, stretching after posing, "3D print this", 1/6 scale statue, miniature, resin or FDM, hollowing, wall thickness, drain holes, keys and pins, cutting into parts, 3D Print Hub, Scale Master, Decimation Master, STL or 3MF, size in millimeters.

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。

Validates business ideas through customer conversations using Rob Fitzpatrick's Mom Test framework. Use when planning user interviews, getting product feedback, validating ideas, designing discovery questions, or extracting honest signal from biased customer responses. Covers good vs bad questions, deflecting compliments, anchoring fluff, pressing for commitment, and customer slicing.

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。

This skill should be used when the user asks to "write a poem", "write a limerick", "write a sonnet", "haiku", "villanelle", "ballad", "song lyrics", "a song for my character", "a rhyme", "rhyming couplets", "does this scan", "check the meter", "fix the rhythm of this poem", "rhyme scheme", "iambic pentameter", "a prophecy in verse", "a nursery rhyme", "rhyming picture book", or wants verse written, scanned, or revised, whether inside a story project or as a standalone poem. NOT for accidental rhymes in prose (use line-editing) or permissions for quoting someone else's poem or song lyrics (use editorial-review).

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。

Make kinetic-typography motion reels (MP4) in code: showreels, portfolio or work reels, product promos, intro films, "motion design"-style videos with bold condensed type, HUD micro-type, black/cream/lime palette, three.js generative layers (particle terrain, liquid marble, chrome knot, particle clouds), shape-continuity transitions and a synthesized, beat-locked score. Canvas 2D + WebGL rendered frame by frame in headless Chrome, encoded with ffmpeg. Use when the user asks for a reel, showreel, motion-graphics / kinetic-type video, a "Work Reel" in this style, or to extend/re-cut the Work Reel ’26.

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。