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Senior Regulatory Affairs Manager for HealthTech and MedTech companies. Prepares FDA 510(k), De Novo, and PMA submission packages; analyzes regulatory pathways for new medical devices; drafts responses to FDA deficiency letters and Notified Body queries; develops CE marking technical documentation under EU MDR 2017/745; coordinates multi-market approval strategies across FDA, EU, Health Canada, PMDA, and NMPA; and maintains regulatory intelligence on evolving standards. Use when users need to plan or execute FDA submissions, navigate 510(k) or PMA approval processes, achieve CE marking, prepare pre-submission meeting materials, write regulatory strategy documents, respond to agency queries, or manage compliance documentation for medical device market access.

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Analyzes competitor products and companies by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structured competitive intelligence. Produces feature comparison matrices scored across 12 dimensions, SWOT analyses, positioning maps, UX audits, pricing model breakdowns, action item roadmaps, and stakeholder presentation templates. Use when conducting competitor analysis, comparing products against competitors, researching the competitive landscape, building battle cards for sales, preparing for a product strategy or roadmap session, responding to a competitor's new feature or pricing change, or performing a quarterly competitive review.

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 and maintain one coherent company story across all audiences — employees, investors, customers, candidates, and partners. Detects narrative contradictions and ensures the same truth is framed for each audience's needs. Use when preparing investor updates, all-hands presentations, board communications, recruiting narratives, crisis communications, or when user mentions company narrative, messaging consistency, storytelling, all-hands, investor update, or crisis communication.

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Diagnose GKE Cloud TPU training throughput drops and step-time regressions (15%+ TPU duty-cycle drop) using ML Diagnostics Workload Monitoring (`gcloud alpha mldiagnostics monitored-events` / `hypercomputecluster.googleapis.com/v1alpha`) and 1-minute Cloud Monitoring system metrics (`kubernetes.io/node/accelerator/*`). Distinguishes hardware and network fabric throttling from workload resource bottlenecks (HBM capacity, host memory, or host CPU saturation). Use when TPU training throughput or duty cycle drops without crashing pods, when `PERFORMANCE_DEGRADATION` monitored events fire, or when triaging slow multi-slice training steps. Don't use for complete multi-slice XLA execution stalls with `HANG_DETECTED` logs (use gke-ai-troubleshooting-tpu-mxla-hang) or pod eviction/interruption restarts (use gke-ai-troubleshooting-jobset-interruption).

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。

Diagnoses and resolves Filestore client mount failures on Google Cloud, permission errors (EACCES), and network timeouts (ETIMEDOUT). Use when an NFS mount hangs or fails from a Compute Engine VM, GKE pod, Cloud Run service, or Vertex AI workload, when `mount.nfs` reports "Connection timed out" or "access denied by server", when checking whether VPC ingress firewall rules or `nfsExportOptions` allow a client IP, or when a previously working Filestore share suddenly stops mounting after an administrative change. Don't use for Cloud Storage (GCS) buckets, Persistent Disk, or Cloud NetApp Volumes, and don't use for Filestore capacity scaling or backup and export-policy auditing.

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Retrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.

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Validate and monitor OpenShell GitHub issues and PRs using the gator:* state machine. Use when asked to triage issues/PRs for project validity, gate PRs, run gator, validate submissions, or monitor PRs toward merge readiness.

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。

Inspects a recorded CARLA .log without replaying it — show_recorder_file_info for the header/actors/frames, collision queries between actor categories, and blocked-actor queries for vehicles that got stuck. Use when the user asks to "what's in this recording", "show recorder info", "which vehicles collided", "find collisions in the log", or "which actors were blocked/stuck".

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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

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。

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

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Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad libraries, and strategic gaps across supported platforms. Use for competitor ads, ad libraries, ad spy, competitive PPC analysis, competitor creative, Google Ads Transparency, Meta Ad Library, or paid-media competitor research.

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。

网文写作工具集基础设施部署与检查。为 Claude Code / OpenCode / Codex / Google Antigravity / ZCode / OpenClaw / Reasonix 提供内置适配；Web AI / 通用 Agent 可走 skills + AGENTS.md 文件模式。触发方式：/story-setup、$story-setup、「准备写书」「帮我搭一下环境」「配置写作项目」「检查写作环境」。

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短篇网文拆文。拆解爆款短篇小说（番茄短篇 / 故事会 / 知乎盐选 / 追妻 / 世情 / 重生 / 虐渣等通俗题材）的故事核、结构、情感线、反转设计、写作手法、共鸣层次。单一全量拆解管道：跑完 Stage 2-6 产出完整拆文报告，落盘到 拆文库/{书名}/，下游 story-short-write 同时读拆文报告 + 情节节点 + 写作手法 + 原文 + _meta.json 写下一篇。触发方式：/story-short-analyze、/短篇拆文、「拆短篇」「拆这篇短文」「短篇拆文」「精细拆解短篇」「8000 字短篇拆解」「番茄短篇拆文」「故事会拆解」「盐言故事拆解」「分析这篇短篇」——均进入同一管道。

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 the effects of a close-combat energy skill as one visual language in Three.js. Lightning that reads as lightning, dynamic but never in your face: trees of strips (trunk, forks of forks, hair-fine branchlets) with a fine white core in a violet-blue glow, each channel reaching out as a stepped leader, flaring as it connects, holding its shape and fading through an afterglow, one discharge at a time; a storm-cloud orb (a lit billow surface that churns in place, 10-15 plasma-globe arcs from a small white core to its inner wall, crawlers on the rim, a hairline fresnel); ground strikes that flash when the leader arrives, crawl along the stone's own cracks and reflect in wet stone (the demo's floor is a scanned CC0 texture); a torn black shadow with dry-brush edges that hangs off the orb and streams behind it, air bursts felt as refraction, orange sparks, fractured rocks that land or float in a storm domain, and impact frames that hold, invert in two-tone ink with tapered speed lines, shake and bend the lens, limited to three flashes a second. Use for anime or dark-fantasy skill effects, lightning or thunder fists, plasma or energy balls, charge-ups, dashes with shadow trails, ground strikes, storm or domain rings, ultimates, hit-stop, impact frames, negative flashes, screen shake, "black lightning", "thunderstorm fist", "realistic lightning", "afterimage" or "skill effects only". Includes a reusable module and a five-beat demo with failure-mode switches.

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Integrate with Octav API for cryptocurrency portfolio tracking, transaction history, and DeFi analytics across 50+ blockchain networks. Use when building applications that need to: (1) Track wallet balances and net worth across multiple chains, (2) Query transaction history with filtering and search, (3) Monitor DeFi protocol positions (Aave, Uniswap, etc.), (4) Access historical portfolio snapshots, (5) Analyze token distribution and holdings, (6) Pay per request as an autonomous agent via x402. Triggers on: "Octav API", "crypto portfolio API", "blockchain portfolio tracking", "DeFi analytics API", "wallet balance API", "transaction history API", "multi-chain portfolio", "Octav x402".

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。

This skill helps users automatically extract detailed video metrics and channel information from YouTube based on keyword searches using the BrowserAct API. The Agent should proactively apply this skill when users express needs such as extract specific keyword YouTube video detailed data, monitor the latest video performance of competitor channels, collect comment and like counts for videos on a specific topic, find AI agent tutorials published this week and extract metrics, evaluate total views and subscriber info for specific videos, scrape detailed metrics of marketing campaign videos, track video trends for a tech topic periodically, get high quality video list data for specified keywords on YouTube, mine detailed information of the latest YouTube videos, collect video duration and engagement data for specific industries, or monitor YouTube content creator performance metrics.

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Scrapes tweets from an X (Twitter) user profile timeline given a handle, with selectable mode: tweets, tweets+replies, or media-only. Returns normalized per-tweet data including text, author profile, engagement counts, media, hashtags, mentions, and cursor for pagination. Use when user mentions X user tweets, Twitter user tweets, profile timeline, user feed, @handle tweets, scrape Twitter user, scrape X account, get all tweets from a user, scrape user timeline, download user posts, export tweets from user, with_replies tab, replies tab Twitter, media tab Twitter, photos of user Twitter, videos of user Twitter, latest tweets from user, recent tweets from account, KOL tweets, influencer tweet history, account post extraction, twitter handle scraper, x handle scraper, get tweets by username, monitor a Twitter account, monitor an X account, daily tweet export from handle, scrape from:username, twitter profile posts, x profile posts, by handle, by username. Also applies to building a creator backlog, tracking competitor accounts, populating quote-tweet research datasets, or any paginated bulk collection driven by a single user handle.

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。

This skill helps users automatically extract structured channel data from YouTube search results via BrowserAct API. Agent should proactively apply this skill when users express needs like finding YouTube channels about specific topics, collecting data on YouTube content creators, tracking YouTube influencers in specific industries, getting YouTube channel information for competitor analysis, searching for YouTube channels related to keywords, monitoring YouTube channel updates for specific keywords, finding YouTube channels that recently published videos, extracting YouTube channel subscriber counts, discovering YouTube vloggers in specific niches, building a YouTube channel database for market research, batch extracting YouTube channel links and descriptions, or monitoring competitor channel growth.

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。

This skill helps users extract YouTube video transcripts and perform deep competitive analysis on the content. Agent should proactively apply this skill when users express needs like analyze YouTube video content strategy, perform competitive video content analysis, extract and analyze YouTube subtitles for marketing insights, understand competitor value propositions from their videos, identify target audience from YouTube video content, analyze pain points and needs mentioned in YouTube videos, evaluate competitor CTA strategies in video content, find content gaps in competitor YouTube videos, analyze video narrative structure and hooks, extract key messaging and positioning from YouTube content, benchmark competitor video content quality, research competitor marketing angles through video analysis, identify audience signals and terminology level in videos, analyze emotional tone and persuasion techniques in YouTube content.

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。

Browse a Taobao or Tmall shop's product catalog by shopId, returning paginated product listings with itemId and title. Use when user asks to scrape a Taobao shop, get all products from a store, list items in a Taobao/Tmall shop, fetch shop catalog by userId or shopId, 采集淘宝店铺商品, 抓取淘宝店铺所有商品, 获取天猫店铺商品列表, 淘宝店铺目录, 按店铺ID采集商品. Also applies to shop inventory monitoring, competitor store analysis, and bulk itemId collection from a specific seller.

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。

Safely refactors C#/.NET code without changing behavior. USE FOR any request to refactor, rename, move, extract, inline, merge, consolidate, deduplicate, split, or modernize C# code, including partial/generated declarations, wrappers, public APIs, serialization/reflection/configuration names, friend assemblies, conditional compilation, and multi-targeted projects. Also use when a request calls a feature, bug fix, package/framework upgrade, or public nullability change a refactor and the behavior-changing part must be separated or declined. DO NOT USE FOR ordinary feature or bug fixes not presented as refactoring; standalone upgrades after reclassification (use dotnet-upgrade); adding tests; or formatting-only work.

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。

Convert .NET projects and solutions (.sln, .slnx) to NuGet Central Package Management (CPM) using Directory.Packages.props. USE FOR: converting to CPM, centralizing or aligning NuGet package versions across multiple projects, inlining MSBuild version properties from Directory.Build.props into Directory.Packages.props, resolving version conflicts or mismatches across a solution or repository, updating or bumping or syncing package versions across projects. Also activate when packages are out of sync, drifting, or inconsistent -- even without the user mentioning CPM. Provides baseline build capture, version conflict resolution, build validation with binlog comparison, and a structured post-conversion report. DO NOT USE FOR: packages.config projects (must migrate to PackageReference first) or repositories that already have CPM fully enabled.

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, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including the generic Vector type, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.

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 to run a four-panel adversarial advisory review on any pull request that touches the OpenAPM specification artifact (docs/src/content/docs/specs/openapm-*.md), its inline / sidecar JSON Schemas (docs/src/content/docs/specs/schemas/*.schema.json), or the conformance fixture seed (tests/fixtures/spec-conformance/**). The panel fans out to four spec-ecosystem reviewers (swagger-openapi-editor, oci-distribution-editor, pkgmgr-registry-contract-editor, w3c-tag-architect), each running in its own agent thread, and a spec-editor synthesizer that produces a fold-now / defer-v0.1.1 / defer-v0.2 / reject list plus a ship decision keyed off a 1..10 shocked_meter scale. The orchestrator is the sole writer to the PR: ONE consolidated comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes folds, and renders a ship recommendation that the maintainer weighs.

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。