'dto' 검색 결과

Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window searches, strand-aware promoter construction, and interval extension. Covers the closest -d/-D a/b/ref/-t/-k/-io/-iu/-id flags, the -D ref strand sign-flip, silent chromosome-end clipping in slop/flank, -t all tie double-counting, and the critical distinction between a geometry answer (nearest TSS) and a biology answer (which gene an element regulates). Use when assigning peaks or variants to genes, defining promoters from a gene model, building distance-to-TSS distributions, finding features within a window, or extending intervals - and when deciding whether nearest-gene is a fair prior (GWAS locus) or a trap (distal enhancer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert knowledge of REST endpoint patterns in melosys-api's frontend-api module. Use when: (1) Creating new REST endpoints for melosys-web, (2) Understanding controller patterns and Swagger documentation, (3) Working with DTOs and request/response objects, (4) Understanding exception handling and HTTP status codes, (5) Adding access control to endpoints. Triggers: "new endpoint", "REST controller", "@RestController", "add a DTO", "ResponseEntity", "Aksesskontroll", "access control on endpoint", "Swagger/@Operation", "return 404/403/401/400", "@Protected", "frontend-api", "search endpoint", "/sok".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates DTO (Data Transfer Object) for PHP 8.5. Creates immutable objects for layer boundaries, API requests/responses, and data serialization. Includes unit tests.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Specialized skill for handling complex object comparison and deep validation. Use when you need to compare deep objects, exclude specific properties, handle circular references, or validate DTOs/Entities. Covers BeEquivalentTo, Excluding, Including, custom comparison rules, etc. Keywords: object comparison, deep comparison, BeEquivalentTo, DTO comparison, Entity validation, excluding properties, circular reference, Excluding, Including, ExcludingNestedObjects, RespectingRuntimeTypes, WithStrictOrdering, ignore timestamp, exclude timestamp

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실패한 테스트를 기준으로 테스트 내용을 유지하면서 구현 코드를 자동 수정합니다. 예산과 시도 횟수를 정하고 재실행 결과와 사용 비용을 확인할 수 있습니다.

• 커밋 후 발생한 CI 테스트 실패 수정
• 먼저 작성한 테스트를 통과하는 구현 준비
• 정해진 예산 안에서 회귀 문제 조사

Simulate device-code and app-consent phishing in an explicitly authorized Microsoft Entra ID environment. Assess whether authentication and consent controls prevent token misuse, map reachable Microsoft 365 resources, and document findings with remediation recommendations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run authorized Microsoft 365 phishing exercises
• Validate Conditional Access and MFA controls
• Test restrictions on app consent

Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing large text collections for language models
• Training a tokenizer on domain-specific text
• Mapping model outputs back to original text

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Build CARLA for Unreal Engine 5.8 using CMake, the tool that configures its build process. The skill guides engine setup, optional ROS 2 support, Python API rebuilding, editor builds, and checks that the expected files were actually produced.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building CARLA for Unreal Engine 5.8
• Building and verifying native ROS 2 support
• Rebuilding the Python API after client changes

Run CARLA routes with a driving agent through ScenarioRunner, including scenarios available only in route mode. Inspect route contents, execute one or all routes, and obtain reports on completion, collisions, traffic lights, and other criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test a driving agent on a specific route
• Find routes containing accident or obstacle scenarios
• Run Leaderboard scenarios without its scoring

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Migrate legacy .NET project files to SDK-style format, reducing repetitive XML and explicit file lists. Convert package references, move assembly metadata into project settings, and consolidate shared build and package settings across multiple projects.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert legacy .csproj or .vbproj files to SDK-style
• Migrate packages.config to PackageReference
• Replace AssemblyInfo.cs with generated assembly metadata

Build or update ASP.NET Core API endpoints with defined request and response types, appropriate HTTP status codes, OpenAPI documentation, and centralized error handling. Also creates .http request files for manually checking endpoint behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add API endpoints following existing project conventions
• Document API inputs and responses with OpenAPI
• Standardize error responses and exception handling

Investigate unexpected tool blocks and repeated permission prompts in Kode. The skill checks approved tools, command restrictions, inherited permissions, and session records, then verifies behavior by repeating the blocked action while preserving required user approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating an unexpectedly blocked tool
• Diagnosing repeated permission prompts
• Comparing permissions for main agents and subagents

Connect Agentica SDK agents to Claude Code CLI through a proxy, an intermediary server. Use setup and debugging guidance to address file permissions, streamed responses, result formatting, and agents reporting success without performing the requested operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting Agentica agents to Claude Code tools
• Investigating file write and edit permission errors
• Debugging streaming and REPL response formats

Apply Martin Fowler-inspired enterprise architecture rules when choosing layers, service layers, transaction scripts, domain models, repositories, mappers, or DTOs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Arbitrary write to RCE playbook. Use when you have an arbitrary write primitive (from heap exploitation, format string, or OOB write) and need to convert it into code execution by targeting GOT, hooks, _IO_FILE vtable, exit_funcs, TLS_dtor_list, modprobe_path, .fini_array, or C++ vtables.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Import data into the Simulation Data Inspector (SDI) from MAT, CSV, or Excel files, from workspace variables, or from a Simulink simulation. Use when the user wants to view a logged file in SDI, load a bench-test log, or verify what a just-completed `Simulink.sdi.createRun` or `sim(model)` produced. Covers the `'file'`, `'vars'`, and `'namevalue'` sources of `createRun`, the auto-populate behavior after `sim()`, and post-import verification. Not for comparing existing runs (use `Simulink.sdi.compareRuns`); not for streaming data live during a running simulation (use `Simulink.sdi.createRunOrAddToStreamedRun` — a separate API); and not for authoring Gherkin or Simulink Test test cases, assertions, or regression tests (use `testing-simulink-models`). Do not activate on test-authoring or verification prompts even when no test-authoring skill is available — say so and stop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.