「ds」の検索結果

This skill should be used when the user asks to "run pentest commands", "scan with nmap", "use metasploit exploits", "crack passwords with hydra or john", "scan web vulnerabilities with nikto", "enumerate networks", or needs essential penetration testing command references.

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。

PCI DSS 4.0.1 establishes 12 requirements across 6 control objectives for organizations that store, process, or transmit cardholder data. With PCI DSS 3.2.1 retiring April 2024 and 51 new requirements

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。

Vendor (copy-import) a single external Claude Code skill into your own skills repo correctly — with license compliance and full provenance/attribution. Deterministic, idempotent primitive: given an upstream ref (owner/repo/path@sha or a GitHub URL) it fetches the skill, detects and gates on the license, copies the folder, injects a provenance frontmatter block (license + metadata: author/source/upstream_commit/vendored_at/provenance), ships the upstream LICENSE, adds a ## Credits section, and records a CREDITS.md ledger entry. Use when the user asks to "vendor a skill", "import a skill", "clone a skill", "borrow someone's skill", "add an external skill with attribution/credit", or スキルを取り込む / vendoring / 他人のスキルを自分のリポに取り込む. This is the import primitive that a skill-scout/curator orchestrator calls; it does NOT discover or evaluate — it correctly imports ONE skill you already chose.

やりたいことを1行で言うと、B 型 loop engineering の 7 要素 (context / feedback / verification gate / termination / error handling / state / cost guard) を埋めた loop spec をコピペ可能な形で生成する。生成した spec は現行 Claude Code の実行プリミティブ (Workflow ツール / /loop skill / ralph-loop plugin / claude -p) に落とせる骨格になる (旧 `claude --goal` は廃止)。10 件 failure mode (F1-F10) の対策フックも自動挿入。Hybrid モード: 1 行入力 → デフォルト埋めで一発出力 → 「ここを調整したい」場所だけ短く聞き直す。Use when ユーザーが「loop を作りたい / loop-design / 自走ループ書いて / Workflow 用の spec 作って / 7 要素埋めて / verification gate ありで」と言ったとき、または `/loop-design` を実行したとき。Loop engineering の理論背景は Cards/loop-engineering-autonomous-agents、失敗モード対策は Cards/loop-failure-modes-mechanism-2026 を参照。

Product-research (选品) desk (Busabase App-in-Skill) for a cross-border e-commerce seller. The agent sweeps trend sources — Amazon BSR movers, TikTok viral product videos, Temu/AliExpress rising items, Google Trends terms, competitor new launches — and files product candidates with margin cards (landed cost, fees, breakeven ACOS) and competition reads; Kelly verdicts them develop / watch / drop, and develop items become sourcing and listing briefs handed to kelly-listing. Use when the user invokes $kelly-picks or /kelly-picks, or asks for 选品, product research, a product sourcing radar, BSR movers, TikTok viral products, a margin calculator, breakeven ACOS, competition reads, or product candidate triage.

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 a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.

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。

Delegate reading large or multiple files to the `bulk-read` command, which sends the files to a cheaper reader model and returns only its answer. Use when a question needs the content of a file over about 350 lines or of several files at once, when a PreToolUse guard denied an unbounded read, or when the user says "bulk read", "一括読み込み", or "まとめて読んで". Do NOT use for files you are about to edit, for subtle correctness reasoning such as concurrency bugs, or for short files that fit a bounded read.

Deploys and configures CrowdStrike Falcon EDR agents across enterprise endpoints to enable real-time threat detection, behavioral analysis, and automated response. Use when onboarding endpoints to EDR coverage, configuring detection policies, or integrating Falcon telemetry with SIEM platforms. Activates for requests involving CrowdStrike deployment, Falcon sensor installation, EDR policy configuration, or endpoint detection and response.

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。

Pick the right y-axis unit when creating or updating a TrendsQuery insight via `posthog:insight-create` or `posthog:insight-update`. Use when the agent is about to add a `formula` purely to convert units (e.g. dividing seconds by 60 to display minutes), when a `math_property` is a duration, currency, ratio, or large count, or whenever the user mentions "format the y-axis", "duration", "seconds", "minutes", "hours", "milliseconds", "ms", "percentage", "currency", "decimals", "axis label", or "axis unit" in the context of a graph insight.

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。

Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it. Profiles the genome first (k-mer spectrum -> size, heterozygosity, ploidy), QCs reads, chooses an assembly path by data type (SPAdes for Illumina, Flye for noisy long reads, hifiasm for HiFi, metaFlye for communities), polishes only when needed, decontaminates, scaffolds with Hi-C, and finishes with three-axis QC (contiguity + completeness + correctness). Use when assembling a genome from raw reads and deciding which assembler, whether to polish, and how to prove the result is good.

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 CARLA (branch ue4-dev, Unreal Engine 4.26) from source on Linux end-to-end — UE4 fork, the Carla server (editor C++ modules), the LibCarla Python client wheel, and content — then verifies them against a from-source server. Optionally builds the native ROS 2 interface in (ROS2=1 → --ros2, Fast-DDS/CycloneDDS/Zenoh), which is compile-time only and cannot be enabled later. Use when the user asks to "build CARLA from source", "compile CARLA ue4-dev", "set up CARLA on Ubuntu (incl. 24.04)", "build CARLA with ROS2 support", or "produce a CARLA server + client wheel". Cooking a distributable Dist/ package is a separate skill (package-carla-ue4); running the build is run-carla-server.

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。

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。

Add authorization methods for a new entity to AuthorizationService. Use after creating a resource service. Triggers on "add permissions", "authorization methods", "entity permissions", "add auth methods".

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。