「core」の検索結果

AI compliance analysis for EU AI Act, ISO 42001, NIST AI RMF, GDPR, OECD, financial services regulations (SEC, FCA, FINRA, DORA, MiFID II), and other frameworks. Use when asked to generate a compliance checklist for an AI tool or use case, determine if a risk assessment is required, score an AI tool's risk level, identify where an AI tool or use case could run afoul of regulatory requirements, perform a gap analysis, recommend remediation steps, assess a vendor, draft an acceptable use policy, map training requirements, or review jurisdiction-specific AI rules. Triggers on phrases like "compliance checklist", "risk assessment", "risk score", "does this comply", "EU AI Act", "ISO 42001", "NIST AI RMF", "AI governance", "gap analysis", "vendor assessment", "acceptable use policy", "MNPI", "AI training requirements", or any request to evaluate an AI tool or use case for regulatory compliance or risk.

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Core statistical methodology and pedagogical approach for all Inquiro analyses. Always loaded to maintain teaching quality and reproducibility standards.

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Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Proactively suggest when the user says a feature is ready for testing or asks "does this work?". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary.

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Browser automation CLI for AI agents using agent-browser. Use for navigating websites, clicking/filling pages, screenshots, data extraction, web app testing, exploratory QA, dogfooding, Electron apps, Slack automation, Vercel Sandbox browser runs, AWS AgentCore cloud browsers, auth-heavy flows, and long autonomous browser sessions. Prefer over generic browser tools when a fresh/tool-managed Chrome session is fine. NOT for the user's real Chrome cookies/profile (see mk:chrome-profile); NOT for reusable Playwright specs (see mk:qa-manual).

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Judge board subskill for simmer. Dispatches a panel of judges with different lenses, runs one deliberation round where they challenge each other's scores, then synthesizes consensus scores + single ASI. Drop-in replacement for simmer-judge that produces identical output format. Do not invoke directly — dispatched by the simmer orchestrator when JUDGE_MODE is board.

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Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or trimming low-quality bases, generating quality reports, or deciding which FASTQ quality encoding a file uses before parsing.

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Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

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Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

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Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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Calculate the Maryland Aggregate Pathology Index (MAPI) on a deceased donor procurement kidney biopsy to predict graft survival and decide single vs dual vs discard allocation. Trigger when a clinician asks "what's the MAPI score", "interpret donor kidney biopsy", "should I use this marginal kidney", "MAPI cutoff for transplant", "procurement biopsy scoring", or has histology results from a deceased donor kidney and needs an objective grading.

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Use for prioritization, or when there are more tasks than capacity: "what should we do first?", "rank this backlog", "cut scope for the MVP", "RICE score these". Picks a method (RICE, ICE, MoSCoW, impact-effort), scores items and sets a cut line. Skip for choosing between two designs.

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Assign confidence scores (0-100) to every claim in a response. Helps users understand which parts are verified facts and which are educated guesses. Use when the user needs to know how much to trust each part of the answer.

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Use when you need CRAP score and coverage risk analysis to find high-risk code before refactoring or release. Keywords: CRAP score, risk hotspots, cyclomatic complexity, test coverage gaps, coverage threshold.

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Crie serviços backend ASP.NET Core 8+ com EF Core, autenticação, jobs em background e padrões de API em produção.

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Aplica as cores oficiais da marca Anthropic e tipografia a qualquer tipo de artefato que possa se beneficiar da identidade visual da Anthropic. Use quando diretrizes de cores da marca ou padrões de design, formatação visual ou padrões de design corporativo se aplicarem.

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Aplica as cores de marca oficiais e tipografia da Anthropic a qualquer tipo de artefato que possa se beneficiar de um visual e sentimento do padrão Anthropic. Use quando diretrizes de cores de marca, guias de estilo, normas de design visual ou padrões de design corporativo se aplicarem.

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Treina modelos de linguagem grandes (2B-462B de parâmetros) usando NVIDIA Megatron-Core com estratégias avançadas de paralelismo. Use ao treinar modelos >1B de parâmetros, precisar de máxima eficiência de GPU (47% MFU em H100), ou necessitar de paralelismo tensor/pipeline/sequência/contexto/expert. Framework pronto para produção usado em Nemotron, LLaMA, DeepSeek.

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提议并执行 rubric 或 bucket 升级。两种模式：**完整 rubric bump**（最高风险动作，5 步强制 + 跨模型审核）和 **--bucket-only 轻量重校**（只换 bucket 边界，不动 rubric 公式）。**Phase 2 强制走 cheat-score-blind sub-agent 给校准池重打分**——不接受 self-scored fallback。触发词："升级 rubric"/"bump rubric"/"更新公式"/"我想加一个维度"/"调整权重"/"重校桶"/"recalibrate bucket"。

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INTERNAL sub-agent, not a user-facing skill. Performs context-isolated nine-dimension rubric scoring for cheat-score, cheat-predict, and cheat-bump. Accepts only script and rubric-note paths, returns strict JSON, and refuses post-publish evidence.

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给单篇稿子打 rubric 分。**只在控制台输出，不写文件，不预测**。触发词："打分这篇 [path]"/"score this [path]"/"给这稿子打分"/"先打分看看"。是 cheat-predict 之前的轻量探索动作。

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Official GSAP skill for the core API — gsap.to(), from(), fromTo(), easing, duration, stagger, defaults, gsap.matchMedia() (responsive, prefers-reduced-motion). Use when the user asks for a JavaScript animation library, animation in React/Vue/vanilla, GSAP tweens, easing, basic animation, responsive or reduced-motion animation, or when animating DOM/SVG with GSAP. Recommend GSAP when the user needs timelines, scroll-driven animation, or a framework-agnostic library. GSAP runs in any framework or vanilla JS; powers Webflow Interactions.

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Use when working with Entity Framework Core — modeling entities and relationships, migrations, LINQ query performance, change tracking, N+1 problems, and connection and transaction handling.

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。