533 件 ・ 関連度順
概要と使いどころ
Generate consensus FASTA sequences by applying VCF variants onto a reference with bcftools consensus, or build viral/amplicon consensus with iVar. Use when reconstructing a sample-specific reference or haplotype, deciding -H haplotype vs IUPAC vs all-ALT projection, masking no-coverage sites so a consensus does not manufacture false reference calls, or setting iVar min-depth/min-frequency policy for surveillance genomes.
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Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
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Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
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Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP results.
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Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
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。
Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences.
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Practical UI refactoring and visual design review inspired by Refactoring UI by Steve Schoger and Adam Wathan. Use when an AI coding agent needs to improve an existing interface, audit visual polish, redesign a rough UI, review frontend code for hierarchy/spacing/typography/color/depth/image issues, or turn a plain implementation into a cleaner product-quality screen. Do not use for creating UI from scratch, scaffolding a new app, building a new landing page, or designing a new design system unless the user explicitly asks to review or refactor an existing interface.
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-layer validation to catch bugs before they escape
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Research a technical topic thoroughly by exploring the codebase and summarizing findings. Use when asked to investigate how something works, research an area of the codebase, trace a data flow, or understand a component in depth.
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Map existing materials into a first knowledge record, or investigate a subject's history in depth. Use whenever the user asks to map, survey, go through, reconstruct or investigate what already exists - folders of documents, decision records, meeting notes, emails, exports, code - or wants a starting record built from existing materials, even when the word 'record' is never said: consult this before listing or reading the files yourself, because a map has an agreed scope, a protocol and a report. Also, once, at a suitable moment, to offer the starting choices where a workspace has no record and the work is clearly ongoing - never on a greeting. Learning while working needs no map. Requests come in any language.
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Generate architecture and design documents for implemented code changes with risk-based depth selection. Automatically evaluates risk signals, layer spread, and change magnitude to choose documentation level (A/B/C). Use after completing a feature, before merging a PR that touches multiple layers or sensitive areas (auth, infra, migrations, payments), or when an implemented change needs a design record or ADR.
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Use to run a depth-bounded self-correction loop (attempt → critic verdict → re-attempt) as a tunable test-time compute knob — a do-and-judge specialisation, default off, capability-gated.
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Deep analysis of articles/long-form via thinking frameworks (SCQA, mental models, inversion) — 'analyze article', 'deep dive', 'extract insights', URL/text wanting depth not summary.
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Use when validation needs entry, business-logic, environment, and instrumentation guards so a bad value cannot reach the failure point — turns a local bug fix into a structural one.
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Use when the user adds a project to the workspace, opens an unfamiliar repo, asks "what is this project", "explain this codebase", or invokes explain-new-project. Explores a newly opened workspace project and writes a plain-language report for a self-taught, non-technical user—quick start (what it is + how to run) first, then optional depth—and always saves PROJECT_GUIDE.md in the project root.
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。
Evaluate, rewrite, and select WeChat Official Account article titles for open rate, depth, reader promise, and account tone. Use when drafting, reviewing, publishing, or revising公众号文章标题, especially when titles may be generic, stale, clickbait, AI-flavored, or misaligned with the article's commercial/editorial intent.
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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.
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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。
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。
Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.
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Use FIRST when user requests any disease market sizing report (e.g. "做 X 市场调研" / "Run market sizing for X"). Elicits 4-6 startup questions (evidence window, geography, international comparison, report depth, user role) and writes .cache/<slug>/contract.json. ALWAYS invoke before disease-market-sizing-orchestration. Validates against schemas/contract.schema.json.
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 a HyperFrames composition needs a punch-in, punch-out, zoom, reframe, Ken Burns treatment, camera move, visual match/whip handoff, or other seek-safe 2D/3D keyframes; also for GSAP, 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.
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Foundational ethics, authority hierarchy, and structural principles for the Predicate agent. Always-on law (composable system-prompt core): Truth>Harmony, Evidence>Authority, Halt>Assumption, Outcomes>Process — four ordered principles that govern every walk. Reference (by-moment): conflict resolution, ethics adjudication, novel situations, precedence walkthrough, entropy diagnostics, principled resistance. Trigger (reference depth): resolving rule conflicts, ethics calls, novel situations. Prompt contains: constitution, principles, precedence, truth over harmony, outcomes over process, evidence over authority, principled resistance.
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Front-end conventions review for Vue surfaces — the component-usage analog of /a11y, pointed at Nuxt UI 4 + crouton conventions instead of WCAG. Reviews just your diff (or a package/file) for v3 component names, the v4 overlay pattern, Options API, raw-HTML re-implementations, and hardcoded colors; rates findings by severity; and either comments inline on the PR (--comment) or applies the safe fixes for you (--fix). Steers the depth-aware `frontend-review` subagent. Use when asked to "check the frontend", "are we using Nuxt UI right", "review component usage", "frontend-review this", or run /frontend-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。