“annotation”的搜索结果

Create an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".

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Add redaction annotations to sensitive fields in schemas to block or hash PII. Use when configuring GDPR/HIPAA compliance, protecting credentials, or blocking internal fields from materializations. Use when user says "protect PII", "hide sensitive data", "hash field", "redact field", "GDPR compliance", "don't capture field", "remove field from Estuary", or "prevent Estuary from capturing field".

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Use when working with Buildkite — buildkite CI/CD pipeline and agent management. Covers build pipeline status, agent pool health, artifact management, build annotations, and cluster queue monitoring. Use when checking build status, investigating failures, managing agents, or reviewing pipeline configurations.

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Use when working with Aws Xray — aWS X-Ray trace analysis, service map generation, fault and error analysis, sampling rule management, and latency investigation. Covers trace summaries, segment analysis, annotation-based filtering, and group configuration.

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Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks.

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Build a throwaway prototype to answer a design question, as UI variations or a state-model walkthrough. Use when the user types /prototype, wants to sanity-check whether a state model or logic feels right, wants to explore what a UI should look like, or as the prototype step of the web design flow with brainstorm or grill before the final plan goes to plan-canvas (annotate the running prototype with the plan-canvas annotation tool).

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Implement, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, or donut charts; when adding chart selection, scrolling, or annotations; when plotting functions with vectorized BarPlot, LinePlot, AreaPlot, or PointPlot; when customizing axes, scales, legends, or foregroundStyle grouping; or when creating specialized visualizations like heat maps, Gantt charts, stacked/grouped bars, sparklines, or threshold lines.

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Display and manipulate PDF documents using PDFKit. Use when embedding PDFView to show PDF files, creating or modifying PDFDocument instances, adding annotations (highlights, notes, signatures), extracting text with PDFSelection, navigating pages, generating thumbnails, filling PDF forms, or wrapping PDFView in SwiftUI.

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Add drawings, shapes, and a consistent markup experience using PaperKit. Use when integrating PaperMarkupViewController for markup editing, adding shape recognition, working with PaperMarkup data models, embedding markup tools in document editors, or building annotation features that need the system-standard markup toolbar. New in iOS 26.

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Implement, review, or improve maps and location features in iOS/macOS apps using MapKit and CoreLocation. Use when working with Map views, annotations, markers, polylines, user location tracking, geocoding, reverse geocoding, search/autocomplete, directions and routes, geofencing, region monitoring, CLLocationUpdate async streams, or location authorization flows. Also use when working with maps, coordinates, addresses, places, directions, distance calculations, or location-based features in Swift apps.

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Add Apple Pencil drawing, canvas views, tool pickers, and ink serialization using PencilKit. Use when building drawing apps, annotation features, handwriting capture, signature fields, or any Apple Pencil-powered experience on iOS/iPadOS/visionOS.

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Swift Concurrency review and remediation for Swift 6.2+. Use when asked to review Swift Concurrency usage, improve concurrency compliance, or fix Swift concurrency compiler errors in a feature or file. Concrete actions include adding Sendable conformance, applying @MainActor annotations, resolving actor isolation warnings, fixing data race diagnostics, and migrating completion handlers to async/await.

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Use when you need to design, review, or improve REST APIs with Micronaut — including @Controller routes, HTTP status codes, DTOs, Bean Validation, exception handlers, pagination, idempotency, ETag/If-Match, caching headers, versioning, contract-first OpenAPI (OpenAPI Generator), optional runtime OpenAPI via micronaut-openapi, and security annotations. This should trigger for requests such as Review or improve Micronaut @Controller REST APIs; Add validation, error handling, or align controllers with the OpenAPI contract on Micronaut HTTP layer. Part of cursor-rules-java project

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Use when you need to review, improve, or build Spring Boot 4.0.x applications — including proper usage of @SpringBootApplication, component annotations (@Controller, @Service, @Repository), bean definition and scoping, configuration classes and @ConfigurationProperties (with @Validated), component scanning, conditional configuration and profiles, constructor injection, @Primary and @Qualifier for multiple beans of the same type, bean minimization, graceful shutdown, virtual threads, Jakarta EE namespace consistency, and scheduled tasks. This should trigger for requests such as Review Java code for Spring Boot application; Apply best practices for Spring Boot application in Java code. Part of cursor-rules-java project

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Diagnose why a product metric changed (dropped, spiked, or plateaued) by orchestrating breakdowns, actors, paths, lifecycle, retention, and annotations queries. Use when the user reports an anomaly, asks "why did X change?", or needs root-cause analysis for a trend, funnel, retention, stickiness, or lifecycle metric.

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Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/scaling choices, robust color mapping, optimal leaf ordering, and ComplexHeatmap/pheatmap/seaborn rendering. Covers the ward.D vs ward.D2 trap, the row-vs-column scaling decision, multi-track annotations, oncoPrint, and raster rendering for large matrices. Use when visualizing expression patterns across samples or identifying co-regulated clusters.

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End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data.

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Orchestrates an end-to-end MeRIP-seq / m6A-seq analysis from raw FASTQ to differential m6A peak calls and metagene plots, chaining fastp adapter trimming, STAR splice-aware alignment (NO deduplication for non-UMI MeRIP), deepTools replicate-concordance + IP-enrichment QC, PreSeq saturation curves, exomePeak2 (transcript-aware Poisson GLM) peak calling, optional MACS3 broad-peak cross-check, DRACH motif confirmation as a sanity check (NOT a per-peak filter), exomePeak2 differential calling via the four-BAM-vector interface (bam_ip + bam_input control; bam_treated_ip + bam_treated_input treatment), ChIPseeker annotation, and the canonical Guitar metagene with stop-codon enrichment as the biological QC anchor. Use when running a complete MeRIP analysis from raw reads, when chaining the constituent epitranscriptomics skills (merip-preprocessing -> m6a-peak-calling -> m6a-differential -> modification-visualization), or when wrapping the pipeline in Snakemake / Nextflow.

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End-to-end copy number variant detection workflow from BAM files. Covers CNVkit analysis for exome/targeted sequencing with visualization and annotation. Use when detecting copy number alterations from sequencing data.

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End-to-end CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.

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Orchestrates the untargeted LC-MS metabolomics pipeline end-to-end (xcms 4.x feature extraction, QC/drift/normalization, confidence-stratified annotation, permutation-validated statistics, background-aware pathway mapping), naming what each stage decides and where it silently fails. Use when running a full LC-MS metabolomics study from raw mzML to enriched pathways and needing the honest handoffs between stages. Each stage defers to its component skill for parameters and traps; for stable-isotope flux (a separate branch, not this untargeted flow) see metabolomics/isotope-tracing.

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Orchestrates the end-to-end germline short-variant pipeline from FASTQ to a filtered, normalized, benchmarked VCF, chaining QC/trim, BWA-MEM2 alignment, duplicate marking, optional BQSR, calling (bcftools/GATK HaplotypeCaller/DeepVariant/DRAGEN), normalization, site+genotype filtering, annotation, and hap.py/vcfeval benchmarking. Use when deciding the pipeline-wide reference-genome commitment (GRCh38 analysis set vs T2T, ALT/decoy handling), sequencing the steps in the defensible order (normalize BEFORE annotate, filter site- then genotype-level), choosing the calling engine and single-sample vs cohort joint-calling, picking a filtering strategy by cohort size, or benchmarking stratified within GIAB confident regions. Hands off mechanism to the variant-calling and read-alignment component skills; not a re-teach of any single step.

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Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabolomics/normalization-qc; for metabolite identification see metabolomics/metabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics/msdial-preprocessing; for downstream statistics see metabolomics/statistical-analysis.

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Biological knowledge: query curated assertions and reference databases for genes, variants, pathways, regulation, cell types, reference atlases, perturbation-derived relationships, phenotypes, therapeutic targets, drugs, interactions, annotations, signatures, compounds, and predicted structures. Use for identifiers, marker sets, gene sets, associations, ontologies, database facts, and cross-database coverage.

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