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Use native WebMCP tools to edit the project open in Timeline Studio. Inspect clips and available assets, preview and apply multi-track, caption style/position and project framing edits; sample rendered media; run local voiceover, transcription, relighting analysis and depth-video jobs; insert media; undo; save; or export video with progress and cancellation. Use for the live browser editor, not local project-file editing.

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Add Temps analytics to React applications with comprehensive tracking capabilities including page views, custom events, scroll tracking, engagement monitoring, session recording, and Web Vitals performance metrics. Use when the user wants to: (1) Add analytics to a React app (Next.js App Router, Next.js Pages Router, Vite, Create React App, or Remix), (2) Track user events or interactions, (3) Monitor scroll depth or element visibility, (4) Add session recording/replay, (5) Track Web Vitals or performance metrics, (6) Measure user engagement or time on page, (7) Set up product analytics or telemetry. Triggers: "add analytics", "track events", "session recording", "web vitals", "user tracking", "temps analytics", "react analytics".

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。

Designing for visionOS — spatial layout and ergonomics (60pt eye targets, field-of-view placement, dynamic scale), eyes-and-hands input with hover rules, motion and visual comfort (the 0.2 Hz rule, vection, depth-cue agreement), immersion strategy, spatial sound, and the video-format decision guide. Use when designing or reviewing any visionOS app, window, volume, or immersive experience.

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。

BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value ca

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 at every layer data passes through to make bugs impossible

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 paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot tiering, lookalike seeds, exclusions, and ranked targeting hypotheses to test. Trigger on 'audience research', 'who should I target', 'interest targeting', 'Meta audience for this product', 'TikTok targeting ideas', 'my targeting is too broad'. Also use when launch is imminent and the ad set targeting is still empty. Not for — persona and JTBD depth for content, see `09-customer-insight-global`; warm retargeting tiers, see `56-retargeting-plan-global`; where the budget goes, see `54-media-plan-global`; the campaign hierarchy, see `52-account-structure-global`.

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Use when a HyperFrames composition needs seek-safe 2D/3D keyframes, GSAP timelines, CSS keyframes, Anime.js, WAAPI, FLIP, paths, masks, SVG morph/draw, text trails, 3D depth, or `hyperframes keyframes` diagnostics. Don't use for broad scene strategy, brand design, media sourcing, captions, or general video planning.

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 after a phase's code is written, or when the user asks to review changed files for bugs, security issues, or quality. Spawns the gsd-code-reviewer agent at quick, standard, or deep depth and writes the phase REVIEW.md.

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。

Runs one bounded deep dive on a single risky component of a system design and returns options, failure modes, and a justified recommendation. Use during a design session when a component needs expert depth beyond the main thread's budget.

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。

Right-size, ground, and gate SDLC decisions with SNC-sized depth, said-vs-assumed write-back, an evidence ledger, honest option cards, recorded approval, and self-review. Use when running brainstorm-feature, plan-feature, design-solution, or system-design-session.

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。

Scores a coding task on Spread, Novelty, and Centrality (SNC 0-6) and maps the tier to autonomy, verification depth, review mode, reviewers, model tier. Use when sizing a fix or feature before planning, choosing fast vs deep review, or deciding whether HARD STOP approval is required.

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。

Conducts investigative-grade research with primary source analysis, cross-verification, and trial-level depth. Use when an album needs factual research, source material, or verification of claims.

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。

Performs security-focused differential review of code changes (PRs, commits, diffs). Adapts analysis depth to codebase size, uses git history for context, calculates blast radius, checks test coverage, and generates comprehensive markdown reports. Automatically detects and prevents security regressions.

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。

Multi-page deep crawling - documentation sites, wikis, knowledge bases

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。

Security-focused differential code review with blast radius analysis, risk-adaptive depth (DEEP/FOCUSED/SURGICAL), git history correlation, and structured finding format. Adapted from Trail of Bits. Use when reviewing PRs, commits, or code changes for security implications.

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。

Meta-skill for internal codebase exploration at varying depths (quick/deep/architecture)

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 size-based review depth, performance review checklist, architecture conformance checks, and framework-specific review patterns.

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。

Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.

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。

Configure host-based firewalls (iptables, nftables, UFW) and cloud security groups (AWS, GCP, Azure) with practical rules for common scenarios like web servers, databases, and bastion hosts. Use when exposing services, hardening servers, or implementing network segmentation with defense-in-depth strategies.

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 comprehensive security architectures using defense-in-depth, zero trust principles, threat modeling (STRIDE, PASTA), and control frameworks (NIST CSF, CIS Controls, ISO 27001). Use when designing security for new systems, auditing existing architectures, or establishing security governance programs.

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。

Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.

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。

Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.

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。

Crawl websites and extract content from multiple pages via the Tavily CLI. Use this skill when the user wants to crawl a site, download documentation, extract an entire docs section, bulk-extract pages, save a site as local markdown files, or says "crawl", "get all the pages", "download the docs", "extract everything under /docs", "bulk extract", or needs content from many pages on the same domain. Supports depth/breadth control, path filtering, semantic instructions, and saving each page as a local markdown file.

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。

Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.

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。