575건 · 관련도순
Education Cloud (EDA — Education Data Architecture) setup: student success hub, advisor workflows, enrollment management, academic data model. NOT for the EDA object model in depth (Affiliations, Course_Connection__c) — use data/eda-data-model-and-patterns. NOT for SIS enrollment sync — use integration/sis-integration-patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Author and own ONE system's design document (the A–K SYSTEM GDD) at .unikit/gamedesign/systems/SYS-<slug>.md — the depth layer of the GDD, single-system scope. Create the skeleton and walk the collaborative section-cycle to write its parameters, rules, formulas, and acceptance criteria ("detail the combat system", "write the GDD for the inventory system", "fill in the rest of this system doc", "spec out the parameters of X"), AND revise it after approval under the delta discipline ("raise the damage by 10%", "tune the economy", "rework the status system", "nerf X", "change this rule") — a number change is Tuning, a small rule a Tweak, restructuring a Rework; each bumps the version and logs the delta. To add a NEW system or restructure the system map use /unikit-gd-spec; to edit GAME.md content use /unikit-gd-spec; the moment an edit also touches a second system, a content type, a flow, or GAME.md (two or more zones) use /unikit-gd-apply; to invent a brand-new game concept use /unikit-gd-brainstorm.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Research complex questions and synthesize relevant evidence into a clear, cited answer. Useful for competitive and market analysis, company research, prospect discovery, enrichment, historical comparisons, and evaluating conflicting claims. Adapt the depth and format to the question; use when a focused search or single-page read is insufficient.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences. Use when: 3D website, three.js, WebGL, react three fiber, 3D experience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in Jungian analytical psychology, depth psychology, shadow work, archetypal analysis, dream interpretation, active imagination, addiction/recovery through Jungian lens, and the individuation process - grounded in primary sources and clinical frameworks. Activate on 'Jung', 'Jungian', 'shadow work', 'archetypes', 'dream interpretation', 'active imagination', 'individuation', 'anima', 'animus', 'collective unconscious', 'addiction', 'recovery', 'spiritus contra spiritum'. NOT for therapy or diagnosis (only licensed analysts diagnose), active psychosis, severe dissociation, or replacing the relational container of actual Jungian analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prepares for hiring manager rounds at Staff+ (L6+) level — scope of impact, influence without authority, ambiguity navigation, mentorship, strategic thinking. Use when practicing HM rounds or calibrating story depth for target level. Activate on "hiring manager round", "HM screen", "staff level", "scope of impact". NOT for coding interviews, system design, behavioral/values rounds, or resume writing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Diagnose and eliminate the GraphQL N+1 resolver explosion using Facebook DataLoader's per-request batching, plus the second-order defenses (persisted queries, depth limits, cost analysis). Use when a GraphQL query that returns N items in a list triggers N+1 database round trips, when designing nested resolvers that load associations, when a list query times out under load, or when you need to budget query cost before resolvers run. NOT for REST-style endpoint design (use api-versioning-strategy), generic database query optimization (use database-query-optimizer), or schema design (use graphql-server-architect).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate consensus FASTA sequences by applying VCF variants onto a reference with bcftools consensus, or build viral/amplicon consensus with iVar. Use when reconstructing a sample-specific reference or haplotype, deciding -H haplotype vs IUPAC vs all-ALT projection, masking no-coverage sites so a consensus does not manufacture false reference calls, or setting iVar min-depth/min-frequency policy for surveillance genomes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fast parallel codebase scouting. Spawns multiple Explore subagents to search directories simultaneously, returning a consolidated file map with architecture fingerprint, complexity estimates, and routing suggestions. Use before planning, debugging, or any task spanning multiple directories. NOT for reading a single known file in depth (use Read directly); NOT for semantic find-usages or go-to-definition.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Practical UI refactoring and visual design review inspired by Refactoring UI by Steve Schoger and Adam Wathan. Use when an AI coding agent needs to improve an existing interface, audit visual polish, redesign a rough UI, review frontend code for hierarchy/spacing/typography/color/depth/image issues, or turn a plain implementation into a cleaner product-quality screen. Do not use for creating UI from scratch, scaffolding a new app, building a new landing page, or designing a new design system unless the user explicitly asks to review or refactor an existing interface.
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Multi-layer validation to catch bugs before they escape
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Research a technical topic thoroughly by exploring the codebase and summarizing findings. Use when asked to investigate how something works, research an area of the codebase, trace a data flow, or understand a component in depth.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.