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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

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。

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

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。

Google-grade software engineering culture encompassing Code Readability, Design Doc Review, Blameless Postmortem, Testing Philosophy, Code Review standards, and engineering excellence practices distilled from Google's internal engineering culture documented in "Software Engineering at Google" and real Google/Brain/DeepMind experience. USE WHEN: establishing engineering standards, reviewing code quality, designing development workflows, onboarding engineers, setting code review guidelines, adopting Google-level engineering excellence, or building an engineering culture that promotes maintainability, readability, and quality.

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 engineering attack techniques beyond email phishing for authorized red team and physical penetration testing engagements. Covers pretexting methodology (persona creation, authority and urgency psychological triggers, rapport building), vishing (voice phishing via caller ID spoofing, IVR system exploitation, VoIP infrastructure setup with Twilio/Asterisk), smishing (SMS-based phishing, carrier gateway abuse, short code impersonation), physical social engineering (tailgating and piggybacking, RFID badge cloning with Proxmark3, lock picking and bypass, dumpster diving for sensitive documents), USB drop attacks (Rubber Ducky keystroke injection, Bash Bunny multi-vector payloads, O.MG cable covert implants, BadUSB firmware attacks), watering hole attack planning and execution, and OSINT-driven targeting (LinkedIn harvesting, organizational chart reconstruction, employee pattern analysis). Integrates with the Social Engineering Toolkit for attack automation, Proxmark3 for RFID/NFC cloning, USB Rubber Duc...

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機械学習のない既存アプリに、推薦・分類・予測などを追加するための手順を整理します。課題とデータの確認から、最初のモデルの学習・評価・組み込みまでを支援します。

• 既存アプリに推薦機能を加えたいとき
• 学習データの準備状況を確認したいとき
• 代替ルール付きで予測を組み込みたいとき

XやLinkedInのつながりを現在の仕事や関心に合わせて見直します。整理・維持・追加の候補を理由付きで示し、本人の文体に合う連絡文の下書きまでまとめます。

• Xのフォロー解除候補を確認したいとき
• LinkedInのつながりを見直したいとき
• 今の関心に合うフォロー先を探したいとき

Onchain OS onboarding & guide hub — the single entry for first-time, 'what is this / how do I use it', OKX.AI, and customer-support intents; classifies the intent and routes to the right sub-flow via its Intent Routing table. Covers: (1) Onchain OS onboarding + welcome banner — 'what is onchainos', 'what is onchain os', 'what can it do', 'what can onchainos do', 'what does onchainos do', 'how do I use this', 'how do I play', 'how to use onchainos', 'how to play onchainos', 'how does onchainos work', 'how do I start', 'getting started', 'tutorial', 'onboarding', 'first time', 'I just installed', 'now what', 'what do I do now', 'where do I start', 'who are you', 'what are you', 'introduce onchainos', 'tell me about onchainos', 'I'm new'; (2) OKX.AI intro & role-registration routing (the Agent economic system — roles User / ASP / Evaluator) — 'what is OKX.AI', 'OKX.AI 是什么', 'how to use OKX.AI', 'OKX.AI 快速开始', and any spelling / spacing / casing / typo variant (OKXAI, okx ai, okx-ai, lowercase okx.ai, 啥是okxai); (3) customer support / Help Center — 'contact support', 'talk to a human', 'customer service', 'file a complaint', 'give feedback', 'report a bug / system error', 'help center', 'FAQ', 'user guide', 'something is broken'. NOT for: direct on-chain actions (swap / wallet / balance / token) or Agent task lifecycle (publish / accept / deliver / dispute) — those have their own skills.

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。

Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use when you need the local install path of a tool / wordlist / clone for a hunt, or as the full-workflow variant when operating from this local toolkit; for general routing use the bug-bounty skill. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告

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。

Linting best practices and golangci-lint configuration for Golang projects — running linters, configuring .golangci.yml, suppressing warnings with nolint directives, interpreting lint output, and selecting linters. Use when configuring golangci-lint, asking about lint warnings or nolint suppressions, setting up code quality tooling, or choosing linters. Also use when the user mentions golangci-lint, go vet, staticcheck, or revive. Not for wiring a lint step into a GitHub Actions pipeline (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).

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 you need to apply Java exception handling best practices — including using specific exception types, managing resources with try-with-resources, securing exception messages, preserving error context via exception chaining, validating inputs early with fail-fast principles, handling thread interruption correctly, documenting exceptions with @throws, enforcing logging policy, translating exceptions at API boundaries, managing retries and idempotency, enforcing timeouts, attaching suppressed exceptions, and propagating failures in async/reactive code. This should trigger for requests such as Exception handling; Use try-with-resources in Java code; Create exception chaining in Java code; Apply fail-fast validation in Java code; Review Java exception taxonomy and propagation. Part of Plinth Toolkit

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。

The craft of weaving story into interactive systems where the player is both audience and author. This skill covers the unique challenges of game narrative: branching structures that respect player agency, environmental storytelling that rewards exploration, dialogue systems that feel alive, and the delicate balance between authored experience and emergent narrative. Unlike linear media, games don't tell stories TO players - they create the conditions for stories to emerge WITH players. The narrative designer's job is to build the scaffolding that supports infinite player-authored moments while maintaining coherent themes and emotional arcs. This skill synthesizes lessons from Disco Elysium's internal monologue as gameplay, Hades' death-as-narrative-engine, interactive fiction's branching mastery (Inkle, Twine), and procedural narrative pioneers like Dwarf Fortress. It addresses ludonarrative harmony, bark systems, cinematics that respect agency, and the "and therefore" test that separates plot from story. Use when "narrative design, game story, game writing, dialogue system, branching narrative, player choice, environmental storytelling, bark system, quest design, lore delivery, ludonarrative, interactive fiction, inkle, twine, dialogue tree, player agency, cinematic design, voice direction, localization, procedural narrative, narrative, game-writing, dialogue, branching, player-agency, environmental-storytelling, quest-design, voice-acting, localization, interactive-fiction, procedural-narrative, ludonarrative" mentioned.

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。

🇺🇸 GitHub README Writing System — Craft a README that converts visitors to stars in <3 seconds. Proven structure from AFFiNE's 0→60K star journey: tagline engineering, first-screen law, section-by-section copywriting guide, Claude Code integration section, anti-patterns, and a pre-publish checklist. Use when you need to write or rewrite a specific README file. 🇨🇳 GitHub README 写作系统 — 打造 3 秒内把访客转化为 star 的 README。来自 AFFiNE 0→60K star 实战：tagline 工程、首屏法则、逐节文案指南、Claude Code 集成板块、反模式、发布前自检清单。需要写或改一个具体 README 文件时使用。 🇯🇵 GitHub README 作成システム — 3秒以内にビジターをスターに変えるREADMEを作る。AFFiNE 0→60Kスター実績から: タグライン設計、ファーストスクリーン法則、セクション別ライティングガイド、Claude Codeインテグレーション、アンチパターン、公開前チェックリスト。 🇰🇷 GitHub README 작성 시스템 — 3초 안에 방문자를 스타로 전환하는 README 작성법. AFFiNE 0→60K 스타 실전: 태그라인 설계, 첫 화면 법칙, 섹션별 카피라이팅 가이드, Claude Code 통합 섹션, 안티패턴, 게시 전 체크리스트. Triggers: "write README" | "README template" | "GitHub README" | "project description" | "tagline" | "open source README" | "README structure" | "README review" | "fix README" | "rewrite README" | "README写作" | "写README" | "README模板" | "项目介绍" | "开源项目文案" | "改README" | "README检查" | "README 구조" | "README 작성" | "README 검토" | "READMEの書き方" | "READMEレビュー"

Update an EXISTING Tip in the ai-product-dev-tips repository — edit its README, add or fix code, refresh outdated information, fix mistakes — while keeping the repo's format conventions and syncing the root README link. Use this whenever the user wants to update, edit, fix, revise, refresh, correct, or complete an existing Tip in this repo — e.g. "nlp_processing/29 の README を更新して", "〇〇の Tip を最新仕様に直して", "image_processing/11 の説明を修正して", "あの Tip のリンク切れを直して", "この Tip を完成版にして". It locates the target Tip, studies the current format conventions (shared with the ai-product-dev-tips-create skill), applies consistent edits, verifies the content with web research (no fabrication), and syncs the root README link (title text / [In-progress]). This is for UPDATING existing Tips, not creating new ones — to add a brand-new Tip use the ai-product-dev-tips-create skill instead. Trigger this even if the user doesn't say "skill".

Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Grok Build, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 78 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "auto research", "switch model", "hand off a session", "session handoff", "switch engine", "continue on codex", "continue on claude", "move this session to another engine", "multi-agent coding", "coding session", "session inbox", "grok session", "grok build", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness", "clawo runs", "run ledger", "session cost", "coding-agent token usage", "spend cap", "budget limit", "maxBudgetUsd", "durable workflow", "resume a run", "verify a coding run", "acceptance contract", "evidence bundle", "did the agent's tests actually pass", "protected tests", "agent edited the tests", "human gate", "repair loop", "clawo workflow", "clawo verify", "clawo solve", "clawo fanout", "fix and verify", "solve from the terminal".

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。

Knowledge base from the DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).

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。

Google TypeScript Style Guide reference for writing, reviewing, and refactoring TypeScript code. Use this skill whenever you are writing TypeScript code, reviewing TypeScript code, refactoring TypeScript, configuring ESLint or linter rules for TypeScript, or when the user asks about TypeScript coding conventions, naming conventions, type system best practices, or import/export patterns. Also trigger when the user mentions 'Google style', 'gts', 'TypeScript style guide', or asks 'how should I name this', 'should I use interface or type', 'how to handle null/undefined' in a TypeScript context. TypeScriptを書く・レビューする・リファクタリングする、ESLintの設定をする、「命名規則」「型の使い方」「import/exportのルール」「interfaceとtypeどっち」「anyを使っていい？」「nullチェックどうする」「Google式」「コーディング規約」「スタイルガイド」などの質問にも対応します。TypeScriptのコードに関する質問や作業であれば積極的にこのスキルを使ってください。

GitHubの課題やプルリクエストを仕分けし、実行予定の作業をLinearにつなげて、公開状況と社内の担当・優先度・次の対応を整理するスキル。

• 未対応の課題やプルリクエストの仕分け
• Linearのタスクが必要か判断したいとき
• 進行中の作業と社内の担当・優先度の連携

Make sure to use this skill whenever the user wants to search for jobs in Denmark, find Danish job listings, look up a specific job posting, or asks anything about the Danish job market — even if they don't mention jobindex.dk explicitly. Invoke this skill for questions about open positions, job vacancies, hiring in Denmark, job opportunities in Danish cities or sectors, or when the user wants to find work in Denmark. Also trigger for phrases like "find me a job", "are there any jobs for X in Copenhagen", or "what jobs are available in Aarhus" when the context is Denmark. Trigger phrases include: jobindex, jobsøgning, job i Danmark, ledige stillinger, job opslag, find job, stillingopslag, jobannonce, job vacancy denmark, danish jobs, jobs in denmark, job search denmark, work in denmark, find work denmark, IT jobs denmark, engineer jobs denmark, developer jobs copenhagen, marketing jobs aarhus, jobs aarhus, jobs copenhagen, jobs odense, jobs aalborg, job openings denmark, hiring denmark, job listings denmark, python jobs denmark, grafisk designer job, data engineer job, softwareudvikler job, full stack developer job danmark.

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。

Comprehensive offensive methodology for software supply chain attacks covering the full kill chain from reconnaissance through exploitation. Addresses dependency confusion across npm, PyPI, and NuGet ecosystems where internal registry override allows an attacker to inject malicious packages that shadow private dependencies. Covers typosquatting techniques for popular packages, compromised package injection via maintainer account takeover or social engineering, and build system attacks through Makefile injection, setup.py install hooks, and npm postinstall scripts. Extends into CI/CD artifact tampering where build outputs are replaced or modified in transit, code signing abuse through stolen or self-signed certificates, upstream repository compromise via commit injection or force-push to trusted repos, and container image supply chain attacks including base image trojaning and registry confusion. Maps to MITRE ATT&CK T1195.001 (Supply Chain Compromise: Compromise Software Dependencies and Development Tools) and T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Integrates tooling such as confused for dependency confusion scanning and dependency-check for known vulnerable component detection. Each technique section provides reproducible proof-of-concept patterns, detection guidance for defenders, and engagement-safe execution notes for authorized red team operations.

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。

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.

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。

Terminal security analysis for shell environments. This skill should be used when checking commands for supply-chain attacks before execution, scanning repositories for hidden content or config poisoning, scoring URLs for homograph attacks, setting up AI tool protection, inspecting Python package artifacts, governing untrusted tasks or Web3 commands, auditing browser extensions, creating provenance receipts, downloading and executing scripts safely, investigating why tirith blocked a command, managing trusted patterns, running security audits, configuring MCP gateway proxies, or working with threat intelligence databases. Also use when the user mentions "tirith", "pipe-to-shell", "homograph", "ANSI injection", "zero-width", "punycode", "terminal security", "shell hook", "cloaking detection", "supply chain attack", "bidi override", "invisible unicode", or "config poisoning". Even if the user does not explicitly name tirith, use this skill when they ask about protecting shell environments, intercepting dangerous commands, or hardening AI agent tool execution.

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。

Mainland-Chinese TV-series craft, documents and production chain (国产剧创作体系与制片链) — the China layer parallel to the Japanese and Korean/French skills. Distilled from Yao Kougen 姚扣根《电视剧写作概论》(2016; series taxonomy, four Chinese script formats, the 3-minute/15-minute/episode-end suspense law, 36 situations), Zhang Wei 张巍《电视剧改编教程》(2014; five source types, sell-point migration, six case studies), Zhang Mingzhi & Song Peiyi《电视剧出品人与制片人教程》(2016; running-time rules, two-stage censorship, scene-breakdown sheet, contracts), Zhao Binbin《影视剧片段改编教程》(2016; film to stage fragment), plus official Chinese renderings of US TV terms from the translated Douglas and Blum textbooks. Use when writing or judging a 国产剧, turning a novel or IP into a 30–50 episode drama, writing a 策划书 / 分集梗概 / 人物小传, formatting a Chinese teleplay, sizing episode count and word count, checking content red lines and the 立项/备案/审查 chain, reading a Chinese screenwriter contract, or mapping US series vocabulary onto Chinese usage.

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。

Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.

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。

Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.

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。