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概要と使いどころ
Build real-time chat applications with Azure Communication Services Chat Java SDK. Use when implementing chat threads, messaging, participants, read receipts, typing notifications, or real-time cha...
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Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.
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。
Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.
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。
Act as FriendlyBet's CEO and primary front door for every FriendlyBet request through automatic company preflight. Own end-to-end routing, planning, execution coordination, department orchestration, decision-rights enforcement, quality gates, board-style reporting, and escalation only for strategic, legal, brand, cost, risk, or taste decisions. Use by default in FriendlyBet threads, with full department planning for broad, ambiguous, strategic, cross-functional, or outcome-driven requests such as SEO, new features, multi-sport expansion, product direction, release execution, company operations, or agent coordination.
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YouTube transcripts to summaries, threads, blogs.
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。
F16 on BitNet-embedding-270M prefill (8 threads) Supports I2_S conversion with optimized kernels on x86 CPUs Lossless inference with 2 bits per weight
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Orientation for custom (non-OpenAI) models running in the Codex app through the codex-router proxy. Explains that the app's native tools arrive as flattened codex_app__ and mcp__ names, that the router restores them so the app executes them, which companion skills to read before threads, browser, or computer-use work, and that a turn with no tool call ends the task. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, when codex_app__ or mcp__ tool names appear in the tool list, when a tool result just arrived and more work remains, or when thread, browser, or computer-use work is requested.
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This skill should be used when the user asks how to "use VibeTags", "add VibeTags annotations", "set up AI guardrails", "protect code from AI", "configure AI platforms", asks about @AILocked, @AIContext, @AIDraft, @AIAudit, @AIIgnore, @AIPrivacy, @AICore, @AIPerformance, @AIContract, @AITestDriven, @AIThreadSafe, @AIImmutable, @AIDeprecated, @AIObservability, @AIRegulation, @AIArchitecture, @AILegacyBridge, @AIStrictClasspath, @AIInternationalized, @AIPublicAPI, @AISchemaSafe, @AIStrictExceptions, @AIStrictTypes, @AIParallelTests, @AIIdempotent, @AIFeatureFlag, @AISecure, @AICallersOnly, @AISandboxOnly, @AIMemoryBudget, @AIPure, @AIDomainModel, @AIExtensible, @AIInputSanitized, @AISecureLogging, @AIExplain, @AIPrototype, @AISunset, @AITemporary annotations, or wants to control how AI tools interact with Java code.
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。
Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.
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。
Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.
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。
YouTube transcripts to summaries, threads, blogs.
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。
Social media command desk (Busabase App-in-Skill) for Kelly's Twitter/X, Facebook, and Instagram accounts (extensible to LinkedIn, YouTube, Threads, TikTok, Xiaohongshu), covering cross-platform monitoring and ECHO-based publishing. Use when the user invokes $kelly-social or /kelly-social, wants cross-platform account metrics and timelines, or needs a content calendar, draft review, engagement inbox, crisis playbook, share-of-voice, or pre-publish quality gate.
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 to process automated PR review feedback (Copilot, Greptile, Codex) and iterate until all AI threads are addressed, checks are green, and the PR is merge-ready. Trigger when the user says "address review feedback", "recycle reviews", or references an automated reviewer.
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 building or reviewing core Quarkus applications with CDI beans and scopes, SmallRye Config and profiles, lifecycle, interceptors and events, virtual threads, and test-friendly design. This should trigger for requests such as Review Java code for Quarkus application structure and CDI; Apply best practices for Quarkus configuration and beans; Improve CDI interceptors, events, or programmatic injection in Quarkus; Add virtual-thread configuration or tune CDI lifecycle. Part of cursor-rules-java project
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Use when you need to review, improve, or build Spring Boot 4.0.x applications — including proper usage of @SpringBootApplication, component annotations (@Controller, @Service, @Repository), bean definition and scoping, configuration classes and @ConfigurationProperties (with @Validated), component scanning, conditional configuration and profiles, constructor injection, @Primary and @Qualifier for multiple beans of the same type, bean minimization, graceful shutdown, virtual threads, Jakarta EE namespace consistency, and scheduled tasks. This should trigger for requests such as Review Java code for Spring Boot application; Apply best practices for Spring Boot application in Java code. Part of cursor-rules-java project
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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。
Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.
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Refactor Spring Boot and Java code to improve maintainability, readability, and adherence to enterprise best practices. This skill transforms messy Spring Boot applications into clean, well-structured solutions following SOLID principles and Spring Boot 3.x conventions. It addresses fat controllers, improper transaction boundaries, field injection anti-patterns, and scattered configuration. Leverages Java 21+ features including record patterns, pattern matching for switch, virtual threads, and sequenced collections.
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Genera endpoints REST con Kotlin + Spring Boot 3.4+ (Records, Virtual Threads, ProblemDetail)
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。
Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.
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。
Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.
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。
PRの未Resolveレビュー指摘に対応するスキル。「PRの指摘に対応して」「Resolveされていないスレッドを解消」「PR reviewの対応」「未解決のレビューコメントを修正」「Copilotの指摘を直して」「Codexの指摘に対応」「chatgpt-codex-connectorの指摘」「レビュー対応」で起動。PR URL or 番号 → 未Resolveスレッド取得 → 対象ファイル修正 → ユーザーへ最終ゲートとプッシュの実行手順を案内、の流れで進める。
Daily per-section review cycle driving a section toward the smallest, clearest form that still does everything it does today. Selects its section live each run (scripted), inventories every file, derives the target shape before any scan, works parallel reading threads into one ranked list and strikes it on a 2-3h budget. One PR per run; each run's report and Needs-Peter queue live in its own docs/health/runs/ file; 'resume' applies Peter's answers later. Use for the morning section-improvement run, 'doctor <section>', or 'run section doctor'.
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Resize, crop, or export any image or video into platform-ready social media assets using Adobe Creative Cloud tools. Use this skill when a user wants to prepare a photo, image, or video for one or more social platforms — Instagram, TikTok, LinkedIn, Facebook, YouTube, Snapchat, Pinterest, Threads, or X/Twitter. Triggers on: "prepare my image for Instagram", "resize for TikTok", "get this ready to post", "make versions for all platforms", "social media sizes", "crop for stories", "export for LinkedIn", "resize my video for social", "make social media assets", or any request to adapt a photo or video for specific platforms. Handles subject-aware cropping, AI canvas expansion, test previews before full runs, and same-ratio video resizing.
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