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Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

KPI体系設計とOKR策定支援スキル。ビジネス目標に整合したKPI設計、バランススコアカード、OKRフレームワーク、 ダッシュボード設計を提供。SMART原則、リーディング/ラギング指標の選定を支援。 Use when designing KPI frameworks, implementing OKR methodology, or creating performance measurement systems. Triggers: "KPI design", "OKR", "performance metrics", "balanced scorecard", "dashboard design", "measurement framework".

SoX（Sound eXchange）を使用した音声処理の専門スキル。音声ファイルの変換、編集、エフェクト適用を効率的に支援。フォーマット変換（WAV/MP3/FLAC/OGG/AIFF等）、音声編集（トリム、結合、分割、フェード）、エフェクト（ノイズ除去、ノーマライズ、EQ、リバーブ、コンプレッサー）、分析（soxi、stat、spectrogram）など幅広い操作をカバー。Use when converting audio formats, applying audio effects, trimming/splitting audio files, normalizing volume, removing noise, generating spectrograms, or processing audio for podcasts/music production.

Drafts a nonconformance report from inspection notes, test results and the governing specification or procedure: what was observed, where, the evidence with references, the requirement cited quoted verbatim with the departure from it, and containment proposals for the quality lead to decide. Never classifies severity, dispositions the item, names a root cause or closes the report. Use when the user asks to "write up this nonconformance", "draft an NCR from my inspection notes", "formalise this deviation" or "turn this snag into a defect report". Do not use for tracking the actions an NCR raises, use corrective-action-tracker 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。

Format the codebase. If $ARGUMENTS is provided, format only that target. Codex equivalent of the Claude /format command.

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 Listings callback for JetEngine — the per-field transform fired when a Dynamic Field widget renders meta values (Format date, Format number, Get post title, Convert units, …). Two registration paths are supported. Legacy 3-filter via jet-engine/listings/allowed-callbacks (label), jet-engine/listings/allowed-callbacks-args (control schema), and jet-engine/listing/dynamic-field/callback-args (positional args at runtime). Modern single-call $manager->register_callback($name, $label, $args) hooked on jet-engine/callbacks/register fires the three filters internally for you. Critical contract — the callback identifier you register MUST be a real PHP callable string (a global function name OR `Fully\\Qualified\\Class::method`). Bare static method names like `'unit_converter'` fail JE's is_callable() gate at apply_callback() and the rendered field silently becomes empty. Use when scaffolding a JetEngine companion plugin's listing-field transform, when seeing "callback appl...

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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 to "script this short video", "write a TikTok / Reels / Shorts script", "给这条抖音或视频号视频写脚本", or "fix the hook — viewers drop off in the first seconds"; produces timestamped beat-sheet scripts on the retention-gate model (0-2s hook / 2-5s confirmation / 5-15s payoff / loop-or-CTA) with per-scene script lines, on-screen text for muted viewing, asset keywords, and 9:16 format-param rows per platform norm card for TikTok, Reels, Shorts, 抖音, and 视频号 — plus 2-3 hook options from named hook families and an AI-content disclosure line by default on realistic synthetic media. Spec-only: rendering, TTS, and publishing stay with the user's own tools — no pipelines, no upload automation. Not for creator video briefs — use brief-generator; long-form video SEO belongs to content-writer. 短视频脚本/抖音分镜/开头钩子/竖屏9:16

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 inconsistencies in ID formatting, date/time display, monetary amounts, and enum/status labels compared to existing patterns in the codebase.

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Use when evaluating, critiquing, transforming, or generating a Master's Informatics thesis idea, research gap, novelty, research question, experiment, AI/ML/CV/NLP/LLM/Edge-AI method, or when distinguishing R&D research from software implementation using the Frascati Manual 2015.

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Create an Estuary derivation for stateless filtering, field selection, and per-document field transformation. Use for WHERE clauses, projecting fields, computing new fields from existing ones, or data cleansing. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (renaming alone is usually a projection, not a derivation). Use when user says "filter-transform derivation", "derivation to filter by status", "transformation with WHERE clause", "derive computed fields", "cleanse data with a derivation", or "stateless derivation".

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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 working with Digitalocean App Platform — digitalOcean App Platform management via the doctl CLI. Covers apps, components, deployments, logs, domains, alerts, and billing. Use when managing App Platform applications or checking deployment health.

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 working with Convertkit — convertKit (Kit) creator marketing platform management including subscriber management, forms, sequences, broadcasts, tags, and automation rules. Covers subscriber growth, email deliverability, sequence performance, and form conversion tracking.

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Use when working with Cloudformation — aWS CloudFormation stack management. Covers stack lifecycle, change sets, drift detection, template validation, nested stacks, stack sets, and event troubleshooting. Use when managing CloudFormation stacks, investigating deployment failures, detecting drift, or validating templates.

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 working with Cloudformation Deep — aWS CloudFormation deep stack management. Covers stack operations, change sets, drift detection, nested stacks, stack sets, resource imports, template analysis, and rollback troubleshooting. Use when managing complex CloudFormation deployments, debugging stack failures, detecting drift, operating stack sets, or analyzing template dependencies.

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。

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

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Implement, review, or improve localization and internationalization in iOS/macOS apps — String Catalogs (.xcstrings), LocalizedStringKey, LocalizedStringResource, pluralization, FormatStyle for numbers/dates/measurements, right-to-left layout, Dynamic Type, and locale-aware formatting. Use when adding multi-language support, setting up String Catalogs, handling plural forms, formatting dates/numbers/currencies for different locales, testing localizations, or making UI work correctly in RTL languages like Arabic and Hebrew.

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Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-section.

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Implement comprehensive API security testing using the 42Crunch platform to perform static audit and dynamic conformance scanning of OpenAPI specifications.

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。

Cross-platform creative quality audit covering ad copy, video, image, and format diversity across all platforms. Detects creative fatigue, evaluates platform-native compliance, and provides production priorities. Use when user says creative audit, ad creative, creative fatigue, ad copy, ad design, or creative review.

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Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols. USE THIS SKILL WHEN: - You need to audit ad revenue performance or diagnose yield drops - Someone asks about header bidding configuration or Prebid.js optimization - You are analyzing fill rates, eCPM trends, or ad unit performance - A project involves programmatic advertising, SSP integration, or ad serving - You need to verify ads.txt, sellers.json, or supply chain compliance - Someone mentions CPM floors, bid density, or auction dynamics - You are evaluating ad quality controls, viewability, or ad-related UX impact - A codebase integrates Google Ad Manager, AdMob, Prebid, or any SSP TRIGGER PHRASES: "ad yield", "eCPM", "fill rate", "header bidding", "Prebid", "programmatic ads", "ad monetization", "CPM optimization", "ad revenue", "ads.txt", "ad serving", "SSP", "ad unit performance", "viewability"

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。