「xp」の検索結果

4人の異なる専門家ペルソナ（20年ベテランエンジニア、TDDエキスパート、Clean Codeエキスパート、バグハンター）で ソースコードを批判的にレビューするスキル。設計判断、テスト容易性、可読性、バグ検出の観点から コードの問題を検出し、改善提案を行う。Python/JavaScriptに対しては追加のチェックポイントを適用。 バグハンターは状態遷移、例外パス、依存関係、非同期競合に特化し、本番環境での問題を事前に検出する。 Use when reviewing source code from multiple expert perspectives to find design flaws, testability issues, code quality problems, and runtime bugs. Triggers: "critical code review", "multi-persona code review", "expert code review", "code quality assessment", "find bugs".

Graded exposure for anxiety disorders: Fear hierarchy, SUDs scale, exposure planning and guidance. Psychoeducation only, not implementation.

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。

Exposición graduada para trastornos de ansiedad: Jerarquía de miedo, escala SUDs, planificación y orientación de la exposición. Solo psicoeducación, no ejecución.

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。

Manages your own skill landscape: surveys and compares existing skills (Audit mode), researches the web for new skills/plugins (Explore mode), and is at the same time the installer that generates lean subskills (Skill-Finder, family umbrella, maintenance skills) instead of loading a monolith. Use this skill for "compare/audit skills", "which skills are duplicated", "form skill families", "clean up/consolidate skills", "maintain the skill register", "find skills/plugins for topic X", "install new skills", "browse the skill marketplace", or for `/skill-explorer`. Delivers a sub-report per family and a globally numbered decision list; installs/uninstalls only after a security check and explicit approval.

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。

Graduierte Exposition bei Angststörungen: Angsthierarchie, SUDs-Skala, Expositionsplanung und -begleitung. Nur Psychoedukation, keine Durchführung.

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。

Google TypeScript Style Guide reference for writing, reviewing, and refactoring TypeScript code. Use this skill whenever you are writing TypeScript code, reviewing TypeScript code, refactoring TypeScript, configuring ESLint or linter rules for TypeScript, or when the user asks about TypeScript coding conventions, naming conventions, type system best practices, or import/export patterns. Also trigger when the user mentions 'Google style', 'gts', 'TypeScript style guide', or asks 'how should I name this', 'should I use interface or type', 'how to handle null/undefined' in a TypeScript context. TypeScriptを書く・レビューする・リファクタリングする、ESLintの設定をする、「命名規則」「型の使い方」「import/exportのルール」「interfaceとtypeどっち」「anyを使っていい？」「nullチェックどうする」「Google式」「コーディング規約」「スタイルガイド」などの質問にも対応します。TypeScriptのコードに関する質問や作業であれば積極的にこのスキルを使ってください。

Operational guide for the NV oOS assistant export/import track — the canonical nvoos-assistant bundle format (v1), the shared portability engine, its five surfaces (WP-CLI, REST, admin UI, AI tools, backup provider), the Pro blueprint exporter, credential-redaction rules, legacy/blueprint import compatibility, test conventions, and the Content Graph port checklist. Use when extending or debugging assistant export/import, adding a surface, changing the bundle schema, fixing round-trip fidelity, or continuing the portability track ("continue the portability plan", "add assistant export to X").

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 optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

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 and evaluate paid-ad experiments with hypotheses, randomization units, sample-size and duration assumptions, guardrails, platform experiment tools, analysis, and decision rules. Use for A/B test, split test, experiment design, hypothesis, statistical significance, sample size, test duration, or experiment readout.

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。

Scan Gmail (default: today's inbox), produce an anonymized summary with action points, then export each email to Linear as an issue with a sensible priority. No personal details — names, email addresses, or company identifiers — ever leave the summary or reach Linear. Use when the user types /gmail-linear-exports, or asks to turn their inbox into Linear issues / export emails to Linear.

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。

リポジトリの構造・技術スタック・機能一覧・セットアップ手順を動的に解析し、 包括的なドキュメントを自動生成するスキル。言語・フレームワーク問わず汎用的に動作する。 Use this skill whenever the user asks to: - Explain a repository / リポジトリの説明 / リポジトリの概要 - Generate a README / README生成 / README作成 - Understand project structure / プロジェクト構成を教えて / 構造を説明して - Learn how to run a project / 使い方を教えて / 動かし方 / セットアップ方法 - Get a repo overview / repo overview / explain this repo / what does this repo do - List features / 機能一覧 / このプロジェクトは何ができる？ - Onboard to a new codebase / コードベースのキャッチアップ / 新しいリポジトリに入った - "このリポジトリ何？" "プロジェクトの全体像" "how to run this project" - "コードの全体を把握したい" "ドキュメントを整備して" "技術スタックを知りたい" This skill is especially useful when joining a new project, onboarding team members, or when a repository lacks proper documentation. It works with ANY repository regardless of programming language, framework, or project type.

You are a code education expert specializing in explaining complex code through clear narratives, visual diagrams, and step-by-step breakdowns. Transform difficult concepts into understandable expl...

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。

You are a code education expert specializing in explaining complex code through clear narratives, visual diagrams, and step-by-step breakdowns. Transform difficult concepts into understandable explanations for developers at all levels.

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。

Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expression errors, or working with webhook data in workflows.

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。

Full lifecycle expense reimbursement: single invoice intake and staging, batch aggregation to generate reimbursement drafts, approval workflow, ledger and dashboard. Trigger when user mentions reimbursement, expenses, invoices, travel reimbursement, approval workflow, or needs to organize / verify / approve expense records. Also applies when user needs to enter invoices, view reimbursement dashboards, ledgers, or compliance checks.

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。

Tests APIs for excessive data exposure where endpoints return more data than the client application needs, relying on the frontend to filter sensitive fields. The tester intercepts API responses and analyzes them for leaked PII, internal identifiers, debug information, or sensitive business data that the UI does not display but the API transmits. This maps to OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving API data leakage testing, excessive data e...

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。

Capture The Flag challenge solving — reverse engineering, steganography, forensics, crypto, web exploitation, binary exploitation, OSINT. Pattern recipes for fast triage and direct exploitation.

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。

Write or edit Japanese articles, reports, guides, and public-facing drafts so the argument is clear and the prose reads naturally. Preserve the author's meaning, evidence, and voice when revising. Use for requests to write from a topic or notes, remove AI臭 / AIっぽさ, improve awkward wording or structure, or format notes as 箇条書き, チーム共有, 社内共有, a decision note, handoff, Slack post, or Teams post. For existing prose, ask Gemini through Antigravity CLI to rewrite naturally by default, then audit against the source; use the current model if unavailable. Do NOT use for ordinary Japanese answers, progress updates, technical explanations, code review, English-only prose that is not a bullet-formatting request, or an explicitly incompatible format.

How to explore and make sense of PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to understand what scouts they have, how each one is behaving, and whether the fleet is actually working. Covers surveying the fleet and its schedules, reading recent scout runs and drilling into a single run's reasoning, inspecting the durable scratchpad memory the fleet has built up, tracing a run to the findings it emitted, and assessing a scout's health and performance over time (cadence, success rate, emit rate, signal-to-noise). Read-only and exploratory — to write or tune a scout, use `authoring-signals-scouts` instead. Trigger on "what are my scouts doing", "how is my <x> scout performing", "show me recent scout runs", "why did this scout find/emit nothing", "what has the fleet learned", "explore scout run <id>", "is my scout working".

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。

Analyze session replay patterns across experiment variants to understand user behavior differences. Use when the user wants to see how users interact with different experiment variants, identify usability issues, compare behavior patterns between control and test groups, or get qualitative insights to complement quantitative experiment results.

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 PostHog experiments and feature flags for configuration issues, staleness, and best-practice violations. Read when the user asks to audit, health-check, or review experiments or feature flags, check flag hygiene, or verify experiment setup.

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。

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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。

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

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End-to-end bulk time-course analysis from an expression matrix to temporal gene modules and per-cluster pathway enrichment. Orchestrates temporal DE (limma splines or DESeq2 LRT), Mfuzz/tslearn soft clustering of expression-profile shapes, GAM trajectory fitting, per-cluster GO enrichment against a temporal-gene background, and an OPTIONAL circadian rhythm-detection branch (MetaCycle/CosinorPy) that runs only when the design covers >=2 full cycles with >=6-8 evenly spaced samples per cycle. Use when analyzing a bulk time-series expression experiment from any omics platform and deciding limma-splines vs DESeq2-LRT for temporal DE, soft vs hard clustering, whether the sampling design even licenses rhythm detection, and which background to use for enrichment. Not for single-cell pseudotime (see temporal-genomics/trajectory-modeling for the bulk-vs-pseudotime boundary) or unknown-period discovery (see temporal-genomics/periodicity-detection).

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。