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Build cross-platform .NET applications with Uno Platform targeting WebAssembly, iOS, Android, macOS, Linux, and Windows from a single XAML/C# codebase. USE FOR: building cross-platform apps from a single C# and XAML codebase; targeting WebAssembly, iOS, Android, macOS, Linux, and Windows simultaneously; migrating WPF or UWP. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Plan complex Blazor UI features by decomposing them into focused components. USE FOR: building a complex Blazor page with multiple sections, planning component decomposition, designing a multi-section dashboard or layout, breaking down a large UI feature into composable components, pages with sidebars and content panels, any page with 3+ distinct visual sections or multiple interacting sub-features, identifying parent-child relationships and data flow. DO NOT USE FOR: creating new Blazor projects or apps from scratch (use create-blazor-project), implementing a single individual component (use author-component), writing component code with parameters and EventCallback (use author-component), or simple single-component pages.

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Triage a single security finding — from a scanner, audit, advisory, or report — to a defensible disposition with a mitigation plan, false-positive justification, or accepted-risk writeup. Use when the user mentions 'triage this finding,' 'is this a real vulnerability,' 'mitigation plan,' 'false positive,' 'accept this risk,' 'compensating controls,' 'risk justification,' 'security ticket,' 'CVSS this,' 'should we fix this,' 'disposition,' 'sign off on,' or has a single security finding and needs to decide what to do.

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Best practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.

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Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.

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Python framework for single- and multi-objective optimization with evolutionary algorithms. Define vectorized objectives and constraints; solve with NSGA-II, NSGA-III, MOEA/D, GAs, or differential evolution. Analyze Pareto fronts, visualize trade-offs, customize operators and callbacks. For engineering design, hyperparameter search, and conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient), platypus, jMetalPy (Java).

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NEB-IRC activation energy pipeline for reaction barriers using GFN2-xTB and pysisyphus. Optimize reactant and product geometries, run CI-NEB path search, optimize the transition state with a Hessian, verify with IRC (one imaginary mode, endpoints matching reactant/product, single NEB maximum), and report the electronic and Gibbs barriers. Use when you need a transition state, reaction barrier, activation energy, minimum energy path, or intrinsic reaction coordinate. Covers reactant/product atom-ordering pitfalls, feasibility sizing for single-core runs, and thermochemistry corrections. Renders an IRC energy-profile plot and an animated TS imaginary-mode HTML viewer. For 2D reaction scheme drawing use rdkit-chemdraw-cdxml.

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Assemble multiple plots into ONE publication-ready multi-panel journal figure (e.g. Figure 1 with panels A, B, C). Use whenever the user asks to combine, compose, or lay out several plots as a single composite figure — newly plotted from data or from already-rendered panels the user supplies (PNG/PDF). Ask the user to pick one of two approaches: (1) redraw every panel into one unified figure using independent, tightly packed `subfigures` (each sized to its own labels, so axes need NOT align), consistent style, correctly placed panel letters, and per-panel legends/colorbars; (2) composite already-rendered PNG/PDF panels onto a mosaic canvas and add panel letters (image compositing, not plotting). Both export vector PDF + high-DPI PNG. For a SINGLE plot from a data table, use the sibling `omics-plotting` skill instead.

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Fix Flutter TabController crash when dynamically showing/hiding tabs. Use when: (1) TabController rebuild causes "SingleTickerProviderStateMixin but multiple tickers were created", (2) Tabs need to appear/disappear based on feature flags or async state, (3) TabController.length changes at runtime based on provider state. The fix is to use TickerProviderStateMixin instead of SingleTickerProviderStateMixin.

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Generate a concise 4-5 page equity research earnings preview for a single company. Analyzes the most recent earnings transcript, competitor landscape, valuation, and recent news to produce a professional HTML report.

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Produce full-length audiobooks and long-form narration with generative voice tools. Use when the task is to turn a manuscript or long text into hours of spoken audio: preparing the manuscript for narration (front/back matter, footnotes, tables, dialogue), casting a single narrator or full cast, controlling pronunciation and voice consistency across a whole book, running a proofing/QC listen, meeting a retailer's technical delivery specs (RMS, peak, noise floor, room tone, chapterized files, metadata), and choosing a distribution route under each platform's current AI-narration policy. Not for short TTS clips, single-line voiceover, podcast production, or captioning existing video.

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Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

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Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

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Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.

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Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data.

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Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.

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Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics, multi-omics, pathway, metagenomics, database queries, visualization, workflows): search the index, locate the best-matching skill, fetch its SKILL.md, and follow it.

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Produce a multi-quarter QA strategy document. Covers scope, risk-based prioritization, test levels (unit/integration/E2E), pyramid analysis, entry/exit criteria, quality KPIs, tool selection rationale, CI scaling levers, and timeline planning. Output is an actionable strategy document, not a shelf document. Use when: "test strategy," "QA strategy doc," "testing approach," "QA roadmap," "multi-quarter QA direction." Not for: a single-sprint or single-release plan — use test-planning. Not for: identifying which areas carry the most risk — use risk-based-testing first. Related: risk-based-testing, qa-metrics, release-readiness, test-planning, test-reliability.

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Composes single-file HTML artifacts (PR review writeups, status reports, incident postmortems, slide decks, design systems, prototypes, flowcharts, module maps, feature explainers, kanban boards, prompt tuners) from a small JSON spec instead of hand-written HTML/CSS/JS. Use when the user asks to "compare options side-by-side", requests an HTML version of a report or review or deck, asks for a flowchart, status update, postmortem, design system reference, interactive prototype, custom editor — or explicitly says "HTML artifact", "single HTML file", "self-contained HTML". Skip for ad-hoc HTML snippets (forms, emails, embedded widgets) where there's no template fit.

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Generate a weekly X (Twitter) content plan from a theme, audience, and content pillars. Produces per-day recommendations (single tweet vs thread, X hook formula, angle, posting time, primary goal), a single-to-thread mix, daily reply and quote-tweet targets, and a goal-mix balance check across replies, reposts, likes, and bookmarks. Use when the user wants to plan a week of X content instead of ad-hoc posting. Not for drafting one tweet (use x-post-writer) or one thread (use x-thread-builder).

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Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics). Use this skill whenever the user is dealing with experimental design, high-dimensional data analysis, integrating AI into scientific workflows, scaling biological research, or navigating noisy, complex systems. Trigger this for topics like single-cell genomics, drug discovery, biological atlases, interdisciplinary research strategy, or when deciding between depth vs. breadth in data collection.

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AI Overview (AI Mode) scraping via Google Search. Returns AI-summarized key points for a single keyword, ideal for deep research, technical Q&A, long-tail product selection, and cross-border consumer preference analysis using the latest web information. Single-round only; follow-ups require the agent to summarize context and issue a new request. Triggered by: Google AI, AI Overview, AI Mode, Google AI search, AI search, deep research, consumer preference analysis, web summary, long-tail product research, cross-border market insights.

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Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. What's inside: • 4-step research framework (website teardown → social media → traffic sources → ad spend) • 3-version website evolution analysis (V1 / Beta / Launch via Wayback Machine) • X/Twitter propagation chain mapping (4-phase model + single-post teardown template) • Growth flywheel 6-stage scoring system (Activation → Referral → Acquisition → Retention → Revenue → Product) • KOL identification framework (Traffic King / Topic Starter / Mindset Shifter) • Content effectiveness ranking (validated: Visual Demo > User Story > Competitor Comparison) • ICP deep analysis template + Freemium pricing teardown • Complete self-check checklist 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"

Twitter/X Agent Operations SOP v2 — Rebuilt from real dual-account operations. Real data: • CN account: grew from 1,150 → 3,679 followers (~14 months), 1 tweet/day, OAuth1 direct post • EN account: Buffer API • Real incidents: cron silent-failed for 2 weeks unnoticed, analytics data off by 17×, thread performance worse than single posts What's new in v2: • Step-by-step onboarding (Q1→Q6), multiple choice wherever possible — no big forms • Mandatory daily log after every tweet + weekly report every Monday • Pre-publish safety checklist (agent must pass before posting) • Content pool tracker (used/unused) + alert when ≤3 items remain • Dual-account credential separation (CN: OAuth1 / EN: Buffer API) • MASTER-STATUS.md as the single source of truth — read + write every session 🇨🇳 Twitter/X Agent 运营 SOP v2 — 基于真实双账号运营经验重写。 开局对话式收集 context、强制日报+周报、发推前红线自检、素材库追踪、双账号凭证分离、MASTER-STATUS.md 单一真相来源。 真实数据：中文账号从 1,150 粉增长至 3,679 粉（约14个月），1条/天。 Triggers: "twitter运营" | "推特agent" | "发推SOP" | "AI代运营" | "推文日报" | "素材库" | "Buffer API" | "OAuth1发推" | "twitter automation" | "tweet sop" | "推特涨粉"

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