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Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot with 5'UTR / CDS / 3'UTR scaling; MetaPlotR; deepTools `computeMatrix scale-regions`), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (bigWig of log2 IP/input via deepTools `bamCompare`; ggcoverage; pyGenomeTracks; Gviz; IGV / UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), 5'UTR / CDS / 3'UTR stacked-bar feature-distribution summaries, and volcano / MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012 *Nature* 485:201; Meyer 2012 *Cell* 149:1635 — concurrent founding papers from different labs both showed this independently). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features for figure 1, generating DRACH motif logos as sanity checks on the peak set, rendering volcano plots of differential m6A, or reproducing the Dominissini 2012 / Meyer 2012 stop-codon enrichment plot.

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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

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Constructs and validates polygenic risk scores using LDpred2-auto, SBayesRC, MegaPRS, PRS-CS, PROSPER, MUSSEL, BridgePRS, JointPRS, PRSmix, or PGS Catalog Calculator with ancestry-aware reference panels (HapMap3, UKB-LD), Pejaver-style calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying Whiffin-style absolute-risk transformation, assessing cross-ancestry portability (Martin 2017 / Ding 2023 continuous ancestry), or auditing PRS manuscripts against the 22-item PRS-RS reviewer checklist.

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Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Filters AI-docking outputs (DiffDock, EquiBind, NeuralPLexer) where ~50% of poses fail physical-validity tests. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis.

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Select, document, download, and checksum-verify the most appropriate NCBI reference genome on an HPC cluster. Use whenever an analysis needs a reference genome, genome FASTA, genome annotation, assembly accession, or genome index and no exact approved assembly is already available; when a user asks to fetch a species genome from NCBI; or when a pipeline must resolve and cache a reproducible reference assembly on a remote cluster.

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Evaluate deliverable quality — scorecard, outcome tracking, regression checklist, and aggregate stats.

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Provides patterns to configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services. Use when setting up monitoring, health checks, or metrics for Spring Boot applications.

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Guides agents through scorecards, outcomes, model profiles, and eval suites. Use when running /cc-scorecard or /cc:scorecard, measuring a deliverable, or checking workflow gates with suite-run.

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Integrate and maintain Robelest Convex Auth in apps by always checking upstream before implementation. Use when adding auth setup, updating auth wiring, migrating between upstream patterns, or troubleshooting @robelest/convex-auth behavior across projects.

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docs (editable docs people share and comment on; the default for any document, named as a doc or not: a document, report, proposal, resume, cover letter, letter, contract, policy, form, template, worksheet, essay, handbook, guide, how-to, cheat sheet, SOP or other writing to keep, share, collaborate on, send, submit, print or sign; a doc exports to Word, PDF, Markdown or Google Docs, so needing a file to send, attach, upload, submit or print is no reason to pick Word, and a file nobody asked for is a doc, not Word; a plan, comparison, summary or notes asked in chat stays in chat; a pasted claude.ai artifact link may be a doc: check with docs tools first; Word or another file format named, tracked changes wanted, or a .docx to change or use as a template → that format's skill): making one → if no docs-connector instructions are in context, call its `guide` (topic.instructions) first; then create the doc (headings only, no body) before any search, file read or plan, even with files attached.

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Read this skill before the first step that uses Claude in Chrome, the browser extension whose tools are named mcp__claude-in-chrome__* (also called Chrome, the browser extension, or the external browser) and which acts in the person's real Chrome with their own sign-ins; before those tools are turned on there may be a single enable__mcp__claude-in-chrome tool instead. It covers loading the tools in one ToolSearch call, checking the person's open tabs and working in a new tab, site permissions, GIF recordings, console logs, dialogs to avoid, and when to stop and ask. It is not for the built-in browser (mcp__Claude_Browser__* or mcp__remote-devices__Claude_Browser__* tools), which has its own skill, and it does not decide which browser to use.

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Validates animation durations, enforces typography scale, checks component accessibility, and prevents layout anti-patterns in Tailwind CSS projects. Use when building UI components, reviewing CSS utilities, styling React views, or enforcing design consistency.

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。

Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.

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Goal-integrity skill. Use for backend/API/persistence, preserve/do-not-change, tests/validation, mocks, rework, multi-part requests. Emits Goal Contracts, Deviation Notices, Phase Checks, Final Audits. Skip for Q&A or trivial edits.

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Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.

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Build AI phone agents with AgentPhone API. Use when the user wants to make phone calls, send/receive SMS, manage phone numbers, create voice agents, set up webhooks, or check usage — anything related to telephony, phone numbers, or voice AI.

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6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.

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Earnings-season triage across ALL active theses (not just Live Portfolio holdings) — every earnings call reported inside the window gets a Quartr event summary, a Conviction-Trigger/Outstanding-Questions cross-reference, and a coverage check against existing Research notes, producing a ranked deep-read queue for /transcript. Use when user says "earnings sweep", "who reported", "triage earnings", "catch up on earnings season", or after any earnings-heavy stretch. Default window 14 days; `/earnings-sweep 90` covers a full season.

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Estimate calcium-normalized Gapon selectivity coefficients from paired exchange and solution chemistry, or initialize depth-explicit EcoSIM Gapon inputs from the closest bundled ecosystem template when paired chemistry is unavailable. Use when deriving or checking EcoSIM GKC4, GKCH, GKCA, GKCM, GKCN, or GKCK values; matching a target ecosystem, climate, vegetation, and management regime to a starting profile; remapping template values to target soil layers; or documenting that template-based values require calibration or tuning.

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Convert AmeriFlux ERA5 half-hourly meteorological CSV files into EcoSIM hourly climate forcing NetCDF files, compare generated forcing against overlapping measured tower meteorology when a FULLSET file is available, and report large biases or errors. Use when preparing or validating EcoSIM climate inputs for AmeriFlux sites, checking ERA5 against in situ records, diagnosing forcing discrepancies, or writing climate-quality reports. For non-AmeriFlux paper sites that need a fresh Copernicus CDS ERA5 point download by longitude/latitude, use era5-cds-point-download first.

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。