Fiction, scripts, blogs, editing, and proofreading (1.8K skills)
窓際族物語のストーリー（あらすじ）からSeedance用の台本・動画生成プロンプト・セリフ音声（VOICEVOX/Irodori-TTSボイスクローン）・Codex参考画像を作成するワークフロー。ユーザーからストーリーを渡されたとき、台本やSeedanceプロンプトの作成・修正を頼まれたとき、キャラのセリフ音声の生成を頼まれたとき、クリップの参考画像（キーフレーム）生成を頼まれたときに必ず使用する。
The English summary is being prepared. Showing the original description.
Madogiwa StudioのRemote Web MCPを使い、窓際族物語のギャラリー、記事、エピソード、生成バージョン、使用モデル、登場メンバー、Seedanceプロンプト、入力画像・参照音声・資料、YouTube動画IDと公開状態を登録・確認する。ユーザーがMadogiwa Studioへの登録、公式サイトコンテンツ編集、動画アップロード、プロンプト同期、入力素材管理、Studio ID確認、MCP接続や同僚環境への導入を頼んだときに使用する。
The English summary is being prepared. Showing the original description.
GameDay シナリオを AWS FIS 実験テンプレート (CDK) として実装する。「シナリオを FIS にして」「実験テンプレートを作って」「障害注入を実装して」「fault-injection を書いて/直して」など、FIS 実験の実装・修正・レビューの全てで必ず使う。FIS アクションの選定、停止条件 (CloudWatch アラーム)、実験ロールの最小権限 IAM、爆発半径の制御 (selectionMode)、実験レポート設定、ECS タスクアクションの SSM サイドカー前提条件など、検証済みの判断基準とコード例を提供する。lib/constructs/fault-injection.ts を編集するときは内容にかかわらず該当する。
The English summary is being prepared. Showing the original description.
新しい GameDay シナリオを追加する。「シナリオを追加して」「新しい障害を考えて」「次の GameDay のネタが欲しい」「scenario-03 を作って」など、シナリオの新規作成・追補・改訂の全てで必ず使う。立案 (gameday-scenario エージェント) → 観測可能性チェック → scenarios/NN-<slug>.md への保存 → fis-experiment スキルへの引き継ぎまでの手順を提供する。特に「観測窓より速い障害は見えない」「読み取りプローブでは書き込み断が見えない」などリハーサル実測に基づく設計チェックを保存前の関門にする。
The English summary is being prepared. Showing the original description.
AI が生成した日本語テキストから「AI っぽさ」を取り除き、人間が書いた文章に書き換える。 日本語の文章を編集・推敲・レビューするときに使う。Wikipedia の "Signs of AI writing" を 基にした blader/humanizer の日本語版。英語固有のルールは日本語の等価パターンへ置き換え、 日本語特有のパターン（「することができます」の冗長形、敬語の均一化、主語の過剰明示、 カタカナ語の氾濫、定型評価語、体温のない結論、体言止めの乱発）を追加している。
The English summary is being prepared. Showing the original description.
日本語と英語の文章を ukwhatn 本人の文体で書く・下書きする・推敲する。PR 概要欄、レビュー指摘・返信、Slack・Discord のメッセージ、チーム内ドキュメント、対外ビジネスメール、コミュニティのフォーラム告知とその合意形成の投稿、記事・レポート・議事録・提案書等の長文を日本語で書く時、本人の代わりに文章を書く時、国際的なコミュニティの運営間チャットに英語で投稿する時に使用。AI 特有の冗長さ・難語・翻訳調を排し、相手と媒体に応じて敬体/常体と敬語の重さを切り替える。雑談・times の投稿は対象外。境界: 実装修正を伴う PR コメント対応は pr-comment（本スキルは文章作成のみ）、コミットメッセージは commit、画面の文言は writing-ui-text、コードコメントは writing-code。
The English summary is being prepared. Showing the original description.
AIが生成した日本語の推敲に使うスキル。「この文章を読みやすくして」「AIっぽさをなくして」「自然な日本語にして」「文章を脱臭して」といった依頼や、技術記事、仕様書、PR説明文、社内レポート、エッセイ・noteの文章を見直すときに使用する。意味と条件を保ち、不自然な比喩、曖昧な主述関係、不要な装飾やコピー調を整える。
The English summary is being prepared. Showing the original description.
Inventory a person's work history as facts before any document is written, listing each period of employment and what they actually did, splitting every experience into situation, their own actions, outcome, and stated role, and recording what each claim rests on (public record, their own records, someone who was there, or memory alone). Use when a user is starting a job search, needs material for a rirekisho, shokumu-keirekisho, or entry sheet, or wants to find which periods of their history they have never written down; do not use to judge their strengths, aptitude, or market value, to name a job they are suited for, or to write finished application copy.
The English summary is being prepared. Showing the original description.
Prepare for a Japanese job interview by working from the documents already submitted: find where an interviewer will dig in (claims the applicant cannot back up, work described in the first person plural, outcomes with no measure, gaps or overlaps in the employment history, must-have requirements the documents never answer), and turn them into questions to prepare evidence for. Use when a user has an interview scheduled and wants to know what will be asked and what to have ready, or wants to prepare reverse questions from earlier company research; do not use to predict whether they will pass, to write answers containing experience they did not report, or to schedule or contact anyone.
The English summary is being prepared. Showing the original description.
Assemble a Japanese shokumu-keirekisho (career history document) from an experience inventory, choosing between reverse-chronological, chronological, and skills-based layouts, mapping the target posting's requirements to the experiences that can support them, and drafting the summary, work history, skills, and self-PR so that each sentence stays within what its evidence supports and confidential details are abstracted. Use when a user wants to write or rebuild a whole shokumu-keirekisho for a specific posting or as a general version, or wants to decide which experiences to include and in what order; do not use to invent experience, numbers, titles, or qualifications, to predict whether it will pass screening, to fill in a rirekisho form, or to submit it anywhere.
The English summary is being prepared. Showing the original description.
Triage a GitHub issue that already has the cc-issue-created label and is assumed ready to start. Inspect the comment history first to decide whether human confirmation is needed (cc-need-human-check, highest priority — reserved for issue-level deadlock, or for confirmation items answer-issue-questions already investigated and explicitly marked unanswerable); otherwise close the issue as not needed, delegate unanswered confirmation items in the last comment or the description to answer-issue-questions (cc-answer-issue-questions), refresh a description stale relative to settled comment-history content (cc-update-issue), turn the issue into an Epic and split it into sub-issues when its work should ship as separate PRs (cc-epic-issue), or move to execution (cc-exec-issue). Any UI-changing issue is routed to the design-first flow (cc-create-ui-design) before execution; the exceptions are an issue whose description already names the .pen design to implement against, and a minor UI change that introduces no new visual decision (no new screen/section, no layout change, no new reusable component, no visual expression outside existing design tokens/components). Design files are never edited in the implementation PR. Dependency checks are out of scope.
The English summary is being prepared. Showing the original description.
Manage your HoneyComb exchange account. Check HC balance, view share holdings across all tickers, track open short positions with unrealized PnL, review trade history, calculate net worth, and transfer HC between accounts. Use when checking balances, reviewing positions, or managing funds.
The English summary is being prepared. Showing the original description.
Philosophy-first writing partner for Medium with tactical depth. Combines human storytelling values with production-ready technical content tools. Six content modes based on staff pick analysis, mode-specific quality gates, evidence requirements, annotated examples, and comprehensive scripts integration. Designed for sustainable practice, not algorithm gaming.
The English summary is being prepared. Showing the original description.
Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.
The English summary is being prepared. Showing the original description.
Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.
The English summary is being prepared. Showing the original description.
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
The English summary is being prepared. Showing the original description.
Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.
The English summary is being prepared. Showing the original description.
Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3/SQANTI-LR (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.
The English summary is being prepared. Showing the original description.
Writing style guide for technical content, social media, blog posts, READMEs, git commits, and developer documentation. Optimized to avoid AI detection patterns. Use when writing any content beyond code.
The English summary is being prepared. Showing the original description.
入力画像を、カメラ・構図・人物配置を保った白黒ラフ絵コンテへ変換する。動画制作の構図資料を画像から作る場合に使う。
The English summary is being prepared. Showing the original description.
docs (editable docs people share and comment on; the default for any document, named as a doc or not: a document, report, proposal, resume, cover letter, letter, contract, policy, form, template, worksheet, essay, handbook, guide, how-to, cheat sheet, SOP or other writing to keep, share, collaborate on, send, submit, print or sign; a doc exports to Word, PDF, Markdown or Google Docs, so needing a file to send, attach, upload, submit or print is no reason to pick Word, and a file nobody asked for is a doc, not Word; a plan, comparison, summary or notes asked in chat stays in chat; a pasted claude.ai artifact link may be a doc: check with docs tools first; Word or another file format named, tracked changes wanted, or a .docx to change or use as a template → that format's skill): making one → if no docs-connector instructions are in context, call its `guide` (topic.instructions) first; then create the doc (headings only, no body) before any search, file read or plan, even with files attached.
The English summary is being prepared. Showing the original description.
This skill enforces Sentry's blog writing standards across every post — whether you're helping an engineer write their first blog post or a marketer draft a product announcement.
The English summary is being prepared. Showing the original description.
Generate user-facing App Store release notes from git history since the last tag.
The English summary is being prepared. Showing the original description.
Extract and update the EcoSIM output-variable catalog from Fortran history-field registration calls such as hist_addfld1d and hist_addfld2d. Use only when explicitly asked to update, regenerate, refresh, or rebuild the list of EcoSIM output variables from HistDataType.F90 or related F90 source; do not invoke for ordinary EcoSIM output analysis unless the variable list itself must be updated.
The English summary is being prepared. Showing the original description.