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Daily per-section review cycle driving a section toward the smallest, clearest form that still does everything it does today. Selects its section live each run (scripted), inventories every file, derives the target shape before any scan, works parallel reading threads into one ranked list and strikes it on a 2-3h budget. One PR per run; each run's report and Needs-Peter queue live in its own docs/health/runs/ file; 'resume' applies Peter's answers later. Use for the morning section-improvement run, 'doctor <section>', or 'run section doctor'.

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React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.

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nuqs (Type-safe URL search params 状態管理, React / Next.js) リファレンス。 useQueryState, useQueryStates, parseAsString / parseAsInteger / parseAsArrayOf 等 parsers、 NuqsAdapter (Next.js / React Router / TanStack Router / Remix)、createSerializer、 createStandardSchemaV1, inferParserType、shallow routing、history 制御、SSR、 URL 長さ制限 (limits)、Zod / Effect / Sury 連携。

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TanStack Query (React Query) v5 (@tanstack/react-query) の API リファレンス。 useQuery, useQueries, useInfiniteQuery, useMutation, useSuspenseQuery, QueryClient, QueryClientProvider, queryOptions, HydrationBoundary, invalidateQueries, prefetchQuery, skipToken, keepPreviousData, placeholderData, staleTime, gcTime, optimistic updates, SSR / hydration, persistQueryClient, eslint-plugin-query。 kubb（フック生成側）とは別。

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TanStack Start (TanStack Router ベースのフルスタック React フレームワーク、 @tanstack/react-start) の API リファレンス。Next.js とは別フレームワーク。 createServerFn (validator / handler), createMiddleware, server routes, createServerOnlyFn / createIsomorphicFn、SSR / selective SSR / SPA mode / static prerendering / ISR、streaming、hydration、server entry / client entry、 TanStack Query 連携 (setupRouterSsrQueryIntegration)、RC → stable 移行、 hosting (Cloudflare Workers / Netlify / Vercel / Node / Bun / Docker)、 production checklist、authentication / session、Tailwind、SEO、 environment variables。ルーティングは tanstack-router スキルを参照。

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Kubb (OpenAPI / Swagger コードジェネレーター) リファレンス。 kubb.config.ts、TypeScript 型・TanStack Query フック・Zod・Faker・MSW モック生成プラグイン、 Fetch / Axios / SWR / React Query クライアント、plugin-client (自前クライアント)。 CLI 8 コマンド、拡張レジストリ (kubb add)、Kubb Studio、Kit (kubb/kit)、 parser-ts / parser-md、adapter-oas、diagnostics、プラグインベース、モノレポ対応。 TanStack Query 本体の API は tanstack-query スキル。

Chakra UI v3 (React コンポーネントライブラリ) リファレンス。 別名: Chakra, chakra, chakra-ui。 コンポーネント (Button, Input, Modal, Drawer, Toast, Menu, Tabs, Tooltip 等)、 レイアウト、フォーム、オーバーレイ、チャート、テーマ、レシピ、 スタイルプロップ、レスポンシブ、ダークモード、Chakra UI Pro ブロック。

Turn vague preferences about employers into criteria that can actually be used to choose between them: for each one, what would have to be seen to confirm it, whether it comes from firsthand experience or from an untested assumption, whether it is a must, a nice-to-have, or something to avoid, and which pairs of criteria pull against each other while both being treated as required, then test the set against real candidates. Use when a new graduate is working out what to select employers on, when someone who has decided to move needs criteria for where to apply, or when a stated axis like "a place I can grow" needs to be made checkable; do not use to recommend an industry, job, or employer, to score or rank candidates, or to judge someone's stated preferences.

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Inventory a person's work history as facts before any document is written, listing each period of employment and what they actually did, splitting every experience into situation, their own actions, outcome, and stated role, and recording what each claim rests on (public record, their own records, someone who was there, or memory alone). Use when a user is starting a job search, needs material for a rirekisho, shokumu-keirekisho, or entry sheet, or wants to find which periods of their history they have never written down; do not use to judge their strengths, aptitude, or market value, to name a job they are suited for, or to write finished application copy.

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Scan Gmail (default: today's inbox), produce an anonymized summary with action points, then export each email to Linear as an issue with a sensible priority. No personal details — names, email addresses, or company identifiers — ever leave the summary or reach Linear. Use when the user types /gmail-linear-exports, or asks to turn their inbox into Linear issues / export emails to Linear.

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Identify the first beachhead market segment for a product launch. Evaluates segments against burning pain, willingness to pay, winnable market share, and referral potential. Use when choosing a first market, targeting an initial customer segment, or planning market entry strategy.

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Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.

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Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.

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Infers person-to-person transmission from pathogen genomes using outbreaker2 (Campbell 2018), TransPhylo (Didelot 2017), phybreak (Klinkenberg 2017), BadTrIP (De Maio 2018), SCOTTI (De Maio 2016), BEASTLIER (Hall 2015), and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs / Walker 2013; MRSA <=15 / Coll 2017; C. difficile <=2 / Eyre 2013; Klebsiella <=21 / Snitkin 2012), models within-host diversity and transmission bottlenecks (Worby-Lipsitch-Hanage 2014; McCrone 2018; Sobel Leonard 2017; Lythgoe 2021 SARS-CoV-2), integrates contact-tracing data, distinguishes generation interval from serial interval (Britton & Scalia Tomba 2019; Ali 2020), attributes source via Bayesian source attribution (Mather 2013 DT104; islandR), and reconciles transmission-network reconstruction with epi metadata. Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB / chronic HIV.

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or flagging problematic samples before clustering or differential analysis.

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Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.

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Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.

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Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

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Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk/Aldridge filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.

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Generates Spring Boot 3.x configurations, creates REST controllers, implements Spring Security 6 authentication flows, sets up Spring Data JPA repositories, and configures reactive WebFlux endpoints. Use when building Spring Boot 3.x applications, microservices, or reactive Java applications; invoke for Spring Data JPA, Spring Security 6, WebFlux, Spring Cloud integration, Java REST API design, or Microservices Java architecture.

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Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering. Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.

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Implement React Server Actions in Next.js 14+ for secure, progressively enhanced form handling and server mutations without API routes. Use when handling form submissions, performing server-side mutations, implementing progressive enhancement, creating actions that work without JavaScript, validating data on the server, revalidating cached data after mutations, handling file uploads server-side, implementing optimistic UI updates, or building forms that gracefully degrade without client-side JavaScript.

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