'case' 검색 결과

Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.

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Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when the user asks about Americans with Disabilities Act (ADA) accommodation for CDL drivers — interaction between ADA + FMCSA medical certification, when DOT-disqualifying conditions trigger ADA analysis, reasonable accommodation case examples, employer obligations, and how to handle a driver with a condition that may affect CDL status. Cite Title I ADA + 49 CFR 391.41.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Long-running design anthropologist that builds comprehensive visual databases from 500-1000 real-world examples, extracting color palettes, typography patterns, layout systems, and interaction design across any domain (portfolios, e-commerce, SaaS, adult content, technical showcases). This skill should be used when users need exhaustive design research, pattern recognition across large example sets, or systematic visual analysis for competitive positioning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design bounded applications and workflows for an always-on AI agent with episodic memory. Covers product ideas, persistence-dependent workflows, and constructed examples that make delegation, interruption, evidence, and opt-out explicit. Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Safety-case design for always-on agents with episodic memory. Covers data hygiene, privacy and security risk surfaces, cost controls, scope, user dependency concerns, retention, incident containment, and responsible deployment evidence. It identifies hazards and asks for named controls, tests, owners, and residual uncertainty; it does not provide legal, clinical, youth-safety, or jurisdictional determinations. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Execute a bounded Coval evaluation on explicit test cases, then audit conversation, recording and metric evidence. Use when the user requests a run; does not automatically rerun, tune an agent or schedule evaluations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compare before/after Coval runs using matched cases, stable metric versions and inspected conversation evidence. Use to assess a regression or proposed improvement; does not automatically launch or tune anything.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and create a simulation persona for testing an AI agent. Guides through use case selection, voice and language configuration, behavior prompt crafting, and interruption calibration. Use when user says "create a persona", "design a persona", "set up a test persona", "configure simulation persona", or "build a caller profile".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end Coval adversarial / red-team testing workflow. Builds one adversarial test set (12 core attack vectors plus legitimate controls, each with an expected-behavior checklist), creates a persistent "Adversarial User" persona and a Composite Evaluation metric that scores each scenario against its own expected behaviors, launches a multi-iteration run against the agent (voice or chat), polls for completion, builds a per-scenario pass/fail scorecard, and creates a saved report grouped by Test Case. Use when a user wants to follow the Adversarial & Red-Team Testing cookbook (https://docs.coval.dev/guides/adversarial-red-team-testing) without doing each step by hand. Triggers: "adversarial test set", "red team my agent", "jailbreak / prompt-injection testing", "test my agent against bad actors".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Import datasets from HuggingFace and convert them to Coval test sets. Use when the user wants to create test cases from HuggingFace dataset or repository.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Launch a specific Coval evaluation with explicit cases and execution bounds. Use when resources are already selected; for result analysis use quick-eval.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Follow-up sweep for pending third-party provider access campaigns. Use when the recurring 30-minute Provider Access Launch dispatcher wakes, or for a manual status check of due email threads, developer portals, support cases, or review deadlines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Writes, fixes and red-teams prompts — system prompts, templates, meta-prompts, agent or bot instructions — as copy-paste-ready artifacts, and picks the model tier for a prompt, task or plan's subtasks. Trigger to write, improve, debug or rewrite a prompt; which model should run this; a prompt that costs too many tokens, to cut with behavior held; or say promptwright (model, optimize — tune against test cases, slim, refresh). Not for a fan-out's units (dispatchwright), installed local models (lmstudiorunner), skills or their slim (skillwright), sourced comparisons (researchscribe), handoffs (handoffwright) or a pre-build interview (grillwright).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds, audits and packages Agent Skills, with their evals. Trigger to build, audit (security too), score or package a skill or SKILL.md; to design or integrate a pack; whether a skill beats incumbents; for trigger evals, test cases or assertion suites (skill, prompt card, agent spec); to slim a skill; when a skill did not fire or a run cost too much; for a prose pass on a skill's own files; to port or sanitize a skill set; to build or contract-test the MCP tools a plugin or mod ships; or say skillwright (evals, slim, diagnose, mcp, refresh, port, pack, integrate, upkeep). Prompt tuning is promptwright's; running a suite, claude plugin eval's; config slims, rigwright's; other docs, commscribe's; brands, brandscribe's; runtime grants, gatewarden's.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prove a Convex feature works — seed, drive as multiple mocked users via convex-test, assert behavior including the negative authz cases (wrong user refused, data-scope enforced).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes PHP codebase for test coverage gaps. Detects untested classes, methods, branches, exception paths, and edge cases. Provides actionable recommendations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps. Invoke when user mentions abstraction levels, making concepts concrete, or explaining at different depths.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[52] EXECUTE. Three-stage refactoring workflow: (1) iterative research of refactor/modularization options, (2) plan + risk/edge-case analysis + Scope150 validation, then implement with tests after user confirmation, and (3) apply Scout105 cleanup protocol. Use when asked to refactor, modularize, or restructure code safely.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.