「ds」の検索結果

TikTok Ads deep analysis covering creative quality, tracking, bidding, campaign structure, and TikTok Shop. Evaluates 28 checks with emphasis on creative-first strategy, safe zone compliance, and Smart+ campaigns. Use when user says TikTok Ads, TikTok marketing, TikTok Shop, Spark Ads, Smart+, or TikTok campaign.

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。

The live fieldwork layer for psycho-logic — go look at the actual target market NOW instead of reasoning from your training-data stereotype of it. Use when a psycho-logic move needs grounding in a specific place: the user asks 'what do competitors in [region/country] actually do', 'what's the real price band / norm here', 'how do local leaders position themselves', 'research this market before we localise', 'what signals trust to buyers in X', or is about to launch/adapt in a market you haven't studied. Runs structured web research in the local language — local competitors and their signals, price bands, category conventions, review/trust sources, local terminology, taboos and regulations — and synthesises a compact perception profile the four moves consume. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic; pair with cultural-context (the lens that tells you what to look for). 日本語・中文の相談（「現地の実態を調べて」「调研当地市场」）にも使う。

有馬記念分析システムのアーキテクチャ/レイヤー境界レビュー。docs/ARCHITECTURE.md のレイヤー構成（commands / domain / repositories / models / features / constants / database / utils / types）に対し、レイヤーディレクトリの意味と配置、参照系(queries)と更新系(aggregates)の分離、DB 接続の注入可能性とテスト容易性、リッチドメインモデルの維持（commands 肥大・ドメイン貧血症）、重み・閾値の constants 単一定義、ファイル配置と命名語彙の一致、不要な抽象化、種別分岐のポリモーフィズム化、生成引数の凝集（5個以上は分割シグナル）を検証。純粋性・依存方向の逆流・DB 接続の生成箇所は lint が error で担保するため扱わない。Use when user says 'アーキテクチャレビュー', 'レイヤー境界チェック', 'review architecture', or '依存方向の確認'.

Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

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。

Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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。

Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.

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。

Spaced-repetition flashcard system. Create cards from facts or text, chat with flashcards using free-text answers graded by the agent, generate quizzes from YouTube transcripts, review due cards with adaptive scheduling, and export/import decks as CSV.

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 the user asks which PRDs exist — 'listar PRDs', 'quais PRDs temos', 'list PRDs' — with status, version, roadmap state and GitHub Project. Read-only.

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。

Produz variações de anúncio para Google, Meta, LinkedIn e TikTok Ads, com matriz de ângulos e limites de caractere por posicionamento. Use quando o usuário disser "anúncio", "criativo", "Google Ads", "Meta Ads", "tráfego pago", "variações de anúncio", "headline de anúncio".

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。

Process, parse, create, and manipulate Excel spreadsheets (.xlsx, .xls) using libraries like xlsx, exceljs, or SheetJS for data import/export and spreadsheet automation. Use when reading Excel files for data import, generating Excel reports, exporting data to spreadsheets, parsing bulk uploads, creating financial reports, manipulating workbooks and worksheets, or building data export features.

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。

When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' 'ad performance optimization,' 'write me some ads,' 'Facebook ad copy,' 'Google ad headlines,' 'LinkedIn ad text,' or 'I need more ad variations.' Use this whenever someone needs to produce ad copy at scale or iterate on existing ads. For campaign strategy and targeting, see paid-ads. For landing page copy, see copywriting.

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。

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

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。

Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

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。

Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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。

Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.

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 when the user asks about CMV maintenance program requirements under 49 CFR 396.3 — what records must be kept, retention periods (12-18 months on vehicle file + 1 year after disposal), what counts as systematic inspection/repair/maintenance, preventive maintenance scheduling, brake adjustment records, and what auditors look for in vehicle files. Cite 49 CFR 396.3 + 396.21.

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 when an inspector requests ELD records during a roadside inspection. Covers the three required transfer methods, what data must be transmitted, and common 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。

Audit or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).

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 building content-heavy sites with Astro, deciding between SSG/SSR/hybrid, choosing client directives (idle/visible/load/media), structuring content collections with type-safe schemas, integrating React/Vue/Solid/Svelte components in the same project, or migrating from Next.js for marketing/docs. Triggers: client:idle vs client:load tradeoffs, content collections schema with zod, image optimization via @astrojs/image, view transitions, server islands, MDX layouts. NOT for SPA-style apps that need full interactivity (use Next/Remix), Astro internals/plugin authoring, or non-content sites where islands provide no benefit.

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。

Design discovery, directories, and guilds for agent swarms at scale. Use when agents need to find each other on a shared surface: capability indices, yellow-pages directories, whois/phonebook routing, reputation-aware discovery, and guild structures that scope trust. Covers the canonical prior art (FIPA Directory Facilitator, Contract Net, Chord/DHT, DNS-SD/Consul, EigenTrust, Greif's merchant guilds) and the failure modes (read-poverty, Sybil reset, Goodhart on rank, directory staleness, cold-start). Triggers: "agents can't find each other", "who owns this file/skill", "route a question to the right agent", "capability registry", "agent reputation", "guild / federation of fleets". NOT for issuing identities, granting authority, or automatically assigning work from a directory entry.

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。

Directs game audio and judges it by numbers — sound palettes, SFX and music briefs, Godot import settings, licence-checked generator briefs, and measured file checks (loudness, peaks, clipping, silence, loop seams). Trigger for game sound design or an audio style guide; an SFX or music list; a loop that clicks; sounds too loud or uneven; what LUFS a game should hit, or normalizing to it; checking WAV or Ogg files before import; or say soundsmith (direct, brief, audit, test). Direction, not DSP; generating a sound starts here. Running ComfyUI is comfyrunner's; audio code godotsmith's; transcription whisperrunner's; canon lorescribe's; pixel art pixelsmith's.

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。

Directs screens and proves they fit — game menus and HUD, web dashboards, single-page artifacts, store-page layouts and plugin panes. Writes the design brief (layout, hierarchy, spacing and type scales, every state, focus and hit size, a check against generic-looking output) and probes the rendered page at phone-to-desktop widths in every theme. Trigger to design or lay out a menu, HUD, dashboard, settings screen or panel, or review its mockup before the build; when a page scrolls sideways on a phone, text is clipped or a dark theme breaks; to check that a page or pane fits from 320 to 1440 px in light and dark; for UI direction before a build, even when nobody says design; or say uismith (direct, probe, fit-first audit). The probe needs optional Playwright. A critique, scored review or brand pass of a built page, palettes and type are brandscribe's; sprites and icons pixelsmith's; Godot scene proof godotsmith's; words commscribe's.

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。

Directs pixel art that must read at several zoom scales at once — per-scale palette and silhouette rules, terrain-versus-unit contrast, a one-scene look test, and briefs for an artist or image generator. Trigger when a game renders one world through zoom bands or levels of detail, a pixelated 3D or 2.5D band with billboards included; when a sprite, unit or icon vanishes against terrain zoomed out; for a cross-band look test or pixel-art style guide; or say pixelsmith (test, brief, audit, 3d). Works from a described scene. Drawing is an art tool's job; rendering and LOD code the game's engineering; a brand palette brandscribe's; game audio soundsmith's.

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。

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

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。