「a」の検索結果

Reads a cash forecast (spreadsheet, model export, pasted table or the narrative that accompanies it) and returns a DRAFT assumptions sheet: every assumption the forecast rests on, stated or implied by the figures, with its value as read, its location in the model, the cash lines it drives, its stated source or "not stated", the evidence that would test it, an owner, and one neutral challenge question, so a reviewer can work through the assumptions one by one. Use when the user asks to "list the assumptions behind this cash forecast", "what is this cash flow forecast assuming", "prepare the challenge questions for the treasury review", "pull out the drivers of the 13-week cash forecast" or "build an assumptions register for the liquidity plan". Do not use for actuals against budget by line, use budget-variance-explainer instead; do not use for the case behind a capital request, use capex-request-pack instead. Drafts for human review; never approves, authorises or signs off.

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。

Register a custom WooCommerce payment gateway — extend WC_Payment_Gateway, declare $id / $title / $supports, implement process_payment returning array(result, redirect), optionally process_refund when refunds is in supports, register via the woocommerce_payment_gateways filter. The single most-confused thing AI gets wrong is payment_complete vs update_status — payment_complete runs the canonical paid-order state machine (status, transaction id, session flag, woocommerce_payment_complete action), update_status only changes the status string. Includes the always-forgotten WC cart empty_cart call after success. Use when integrating a payment provider, reviewing gateway code, or debugging "payment succeeded but cart didn't clear" / "order stuck in pending". Triggers on WC_Payment_Gateway, woocommerce_payment_gateways, process_payment, process_refund, payment_complete, get_return_url, woocommerce_payment_complete_order_status.

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 data visualization charts in Flutter using the fl_chart package (v1.2.0). Supports LineChart, BarChart, PieChart, ScatterChart, RadarChart, and CandlestickChart with implicit animations, touch interactivity, and pan/zoom transformations. Use when building any Flutter chart, graph, or data visualization — including line graphs for trends, bar charts for comparisons, pie/donut charts for proportions, scatter plots for distributions, radar charts for multi-axis comparisons, or candlestick charts for financial OHLC data. Also use when customizing chart axes, titles, tooltips, grid lines, touch callbacks, chart animations, or chart zoom/pan behavior.

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 just says "plan", "planen" or "roadmap": offer a template via aos-plan-canvas templates. Open plans and HTML artifacts in a local browser canvas where the human annotates elements, chats, and approves or requests changes without leaving the page. Use for planning, whenever a user says "plan", "roadmap", "visualize the plan", "review the plan" or /plan-canvas, right after a brainstorm or grill interview has produced a plan, and whenever any skill's artifact (plan, design, comparison, report, diagram) can be reviewed visually instead of with typed feedback.

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 ideas that don't sound logical, then validate them with cheap behavioural experiments — Rory Sutherland's 'the opposite of a good idea can be a good idea' plus revealed-preference testing. Use when the obvious/logical solution is saturated and the user wants genuinely different options, asks 'what's a creative/unconventional angle', 'how do we stand out', 'should we run a test/experiment', 'how do we know what people actually want', or is about to trust a survey/focus group. Covers deliberate inversion of the obvious move, why stated preferences lie, and designing small reversible experiments that watch behaviour. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「競合と同じ案しか出ない」「アンケートは好評なのに売れない」「思い切った案を安く試したい」）にも使う。中文咨询（“只能想出和竞品一样的方案”“问卷好评却卖不动”）也适用。

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。

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。

Super-Admin-only lock-down pattern. Use before touching admin.admins, admin.admin_audit_log, is_super_admin() / requireSuperAdmin() / selectIsSuperAdmin, app/api/admin/admins/**, app/(admin)/administration/users/admins/**, the admin RPCs (admin_promote, admin_update, admin_revoke, admin_list, admin_list_audit, admin_find_user_by_email), createAdminClient(), a new RLS policy, SECURITY DEFINER RPC, or admin-gated route, or when locking a sensitive table to Super Admin.

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。

AI language tutor for learning ANY language through conversation, vocab drills, grammar lessons, flashcards, and immersive practice. Use when the user wants to: learn a new language, practice vocabulary, study grammar, do flashcard drills, translate phrases, practice conversation, prepare for travel, learn slang/idioms, or improve pronunciation. Supports ALL languages including Spanish, French, German, Japanese, Chinese (Mandarin/Cantonese), Korean, Arabic, Hindi, Bengali/Bangla, Portuguese, Russian, Italian, Turkish, Vietnamese, Thai, Swahili, Hebrew, Polish, Dutch, Greek, and 100+ more.

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。

OpenClawへの自然な言葉の依頼をClaude Codeなどの外部コーディングエージェントへ振り分け、作業の開始や継続、スレッド内の会話をつなぐスキルです。

• Claude Codeをスレッドで開始
• 外部エージェントの作業を続けたいとき
• acpxから直接指示を渡したいとき

Next.js 16以降の開発で、起動や変更反映の遅さ、Turbopackとwebpackの使い分けを確認します。キャッシュやproxy.tsの命名も扱うスキルです。

• 開発サーバーの起動の遅さを調べたいとき
• コード変更の反映が遅いときの確認
• Turbopackとwebpackの選択

本番データベースの列・索引・データを変更する手順を整理します。大きなテーブルの分割更新や段階的な切り替え、復旧計画を含めて移行を設計するスキル。

• 大きなテーブルに列や索引を追加したいとき
• 停止を避ける段階的な列名変更
• 既存データを分割更新したいとき

Djangoの公開や変更提案の前に、環境・コード品質・データベース変更・テスト・脆弱性・設定を順に検証し、段階別の合否と修正すべき問題をレポートにまとめます。

• Djangoの変更提案前の確認
• マイグレーションを確認したいとき
• テストカバレッジの目標確認

ACTIVATES ONLY on an explicit user request for the ulw-plan workflow: the user themselves saying ulw-plan, ulw plan, /skill:ulw-plan, or asking in their own words for a work plan before coding. NEVER self-activates: a bare ulw/ultrawork run, an agent-side routing decision, or reading this file is not a request, and the plan-gated reviewers (metis/momus) stay locked without a user request plus a written .omo/plans plan file. Explore-first planning consultant (Prometheus) that grounds in the codebase, asks only the forks exploration cannot resolve - or researches them to best practice when the intent is fuzzy - waits for explicit approval, then writes ONE decision-complete work plan a worker executes with zero further interview. Triggers: ulw-plan, ulw plan, plan this, make a plan, plan before coding, interview me, break this down, start planning, plan mode.

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 whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: "chart", "graph", "plot", "data viz", "visualization", "dashboard", "analytics", "visualize data", "categorical colors", "sequential / diverging palette", "stat tile", "sparkline", "heatmap", "legend", "axis", "tooltip", "chart colors", "color by series".

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。

Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions 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。

Anti-forensics and evidence destruction techniques for red team operators conducting authorized engagements. Covers log clearing on Windows (wevtutil, Clear-EventLog, ETW provider patching) and Linux (journal truncation, utmp/wtmp binary editing, syslog manipulation), timestamp manipulation via Timestomp and SetMACE to defeat timeline analysis, filesystem-level anti-forensics including NTFS Alternate Data Streams for payload hiding and secure deletion with sdelete/shred, memory artifact removal to counter live forensics, disk artifact manipulation targeting MFT entries and USN journal records, network forensics evasion through encrypted C2 channels and DNS-over-HTTPS tunneling, and anti-VM/sandbox detection to avoid dynamic analysis environments. Tools: Timestomp, wevtutil, sdelete, shred, MimiPenguin, Invoke-Phant0m. Aligns to MITRE ATT&CK T1070 (Indicator Removal), T1027 (Obfuscated Files or Information), T1497 (Virtualization/Sandbox Evasion). Each technique includes the forensic artifact it targets, the destruction or manipulation method, and the defender perspective so operators understand detection gaps they must account 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。

Searches X (Twitter) for tweets by free-form advanced query and returns a normalized tweet list with text, author profile, engagement counts, media, hashtags, mentions, and cursor for pagination. Use when user mentions X search, Twitter search, scrape tweets by keyword, search X by hashtag, search Twitter by hashtag, find tweets about a topic, search tweets by date range, since until search Twitter, advanced Twitter search, tweets with media, tweets with images, tweets with video, top tweets, latest tweets, X latest tab, X top tab, min_faves, min_retweets, min_replies, filter verified Twitter, filter blue verified, from user Twitter, to user Twitter, mentioning user Twitter, geocode Twitter search, near location Twitter, lang code search Twitter, exclude retweets, exclude replies, collect tweets at scale, bulk tweets export, monitor brand mentions on X, monitor brand mentions on Twitter, competitor mention monitoring, KOL tweet harvesting, twitter keyword scraper, tweet keyword scraper, search X by keyword, search twitter, search x.com, x.com search, twitter advanced search, x advanced search, tweet collection by query, scrape twitter search results. Also applies to time-bounded research, sentiment analysis input gathering, hashtag campaign tracking, geo-targeted tweet collection, and any paginated bulk tweet collection driven by a search query.

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。

Etsy category page scraper: given an Etsy category URL (e.g. https://www.etsy.com/c/jewelry) and optional page number, returns paginated product listings with listingId, shopId, title, url, image, salePrice, originalPrice, currency, rating, reviewCount, shopName, isAd, freeShipping, badge from category and subcategory pages. Use when user mentions Etsy category, Etsy c/ URL, etsy.com category, browse Etsy categories, scrape Etsy category, Etsy category listing, extract Etsy category products, Etsy subcategory, Etsy jewelry, Etsy clothing, Etsy home decor, Etsy wedding, Etsy accessories, Etsy craft supplies, Etsy vintage category, Etsy category page scraper, Etsy taxonomy, get all products from an Etsy category, bulk category export from Etsy, Etsy niche scraping. Also applies to trend research within a specific Etsy category, competitor benchmarking within a category, sourcing suppliers by category, seasonal product monitoring, and paginated bulk collection driven by a category URL.

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。

Fetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.

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 custom dotnet new templates for correctness before publishing. Catches missing fields, parameter bugs, shortName conflicts, constraint issues, and common authoring mistakes that cause templates to fail silently. USE FOR: checking template.json files for errors before publishing or testing, diagnosing why a template doesn't appear after installation, reviewing template parameter definitions for type mismatches and missing defaults, finding shortName conflicts with dotnet CLI commands, validating post-action and constraint configuration. DO NOT USE FOR: finding or using existing templates (use template-discovery), creating projects from templates (use template-instantiation), creating templates from existing projects (use template-authoring).

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。

Find and assess datasets for a research question. Dispatches Explorer agents to search across data source categories, then Explorer-Critic to stress-test each candidate. Produces a ranked list with feasibility grades. Make sure to use this skill whenever the user wants to identify or evaluate data sources — not to search for papers or run analysis. Triggers include: "find data", "what data should I use", "find a dataset for this", "where can I get data on X", "assess datasets", "what datasets exist for", "help me find data", "is there data on this", "what are my data options", "I need data for this project", or any request to locate empirical data sources for a research question.

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。

ACTIVATES ONLY on an explicit user request for the ulw-plan workflow: the user themselves saying ulw-plan, ulw plan, /skill:ulw-plan, or asking in their own words for a work plan before coding. NEVER self-activates: a bare ulw/ultrawork run, an agent-side routing decision, or reading this file is not a request, and the plan-gated reviewers (metis/momus) stay locked without a user request plus a written .omo/plans plan file. Explore-first planning consultant (Prometheus) that grounds in the codebase, asks only the forks exploration cannot resolve - or researches them to best practice when the intent is fuzzy - waits for explicit approval, then writes ONE decision-complete work plan a worker executes with zero further interview. Triggers: ulw-plan, ulw plan, plan this, make a plan, plan before coding, interview me, break this down, start planning, plan mode.

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。

Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example datasets as reference candidates only, specify the core analysis modules and method choices, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, cohort availability, assay coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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。