「cro」の検索結果

Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.

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Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.

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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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Decompose systems into microservices and apply canonical distributed-systems patterns. Use this skill whenever the user wants to split a monolith into services, design service boundaries, choose between microservices and a modular monolith, implement sagas or the outbox pattern, set up CQRS or event sourcing, configure an API gateway or BFF, add resilience (circuit breakers, retries, timeouts), or implement distributed tracing with OpenTelemetry. Also trigger for distributed transactions, service discovery, event-driven architecture, contract testing with Pact, canary releases, and idempotent consumers.

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Quando o usuário quer otimizar qualquer formulário que NÃO seja de inscrição/registro — incluindo formulários de captura de leads, formulários de contato, formulários de solicitação de demo, formulários de aplicação, formulários de pesquisa ou formulários de checkout. Também use quando o usuário menciona "otimização de formulário", "conversões de formulário de lead", "atrito em formulário", "campos de formulário", "taxa de conclusão de formulário" ou "formulário de contato". Para formulários de inscrição/registro, consulte signup-flow-cro. Para popups contendo formulários, consulte popup-cro.

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Quando o usuário quer otimizar fluxos de inscrição, registro, criação de conta ou ativação de teste gratuito. Também use quando o usuário mencionar "conversões de inscrição", "atrito de registro", "otimização de formulário de inscrição", "inscrição em teste gratuito", "reduzir abandono de inscrição" ou "fluxo de criação de conta". Para onboarding pós-inscrição, consulte onboarding-cro. Para formulários de captura de leads (não criação de conta), consulte form-cro.

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Quando o usuário deseja criar ou otimizar popups, modais, overlays, slide-ins ou banners para fins de conversão. Use também quando o usuário menciona "exit intent," "conversões de popup," "otimização de modal," "popup de captura de leads," "popup de email," "banner de anúncio" ou "overlay." Para formulários fora de popups, consulte form-cro. Para otimização geral de conversão de página, consulte page-cro.

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[Planning] Use when you need intelligent plan creation with prompt enhancement. Flag: --mode={ci|cro} (default none — standard planning); --mode=ci plans a fix from a GitHub Actions CI run/log, --mode=cro plans conversion-rate optimization (25-item CRO framework).

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Translate motion, interaction, and visual experience into implementable constraints while preserving accessibility and performance. Apply when working on animations, transitions, scroll effects, or micro-UX.

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Render a crouton collection schema (the field-definition JSON) into a human-readable field table + relationships sketch (HTML + PNG + a terse Markdown table), so a human can sign off on the DATA MODEL before any code is generated. Use when an agent drafts or changes a collection schema (before `crouton config` / `generate_collection`), or when asked to "review a schema", "show the data model", "check these fields".

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Cleanly tear down a crouton poc/app — the inverse of /deploy. Removes the code, the Cloudflare Worker + D1 + KV (both env scopes), the app's stray branches, and closes its epic/sub-issues — with a --dry-run first and a typed confirm for anything destructive. Use when asked to "remove an app", "tear down a poc", "delete app X and its resources", or to clean up after an abandoned proof-of-concept. Counterpart to /deploy, /poc-deploy, crouton init.

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The catalog of every config-as-data surface in nuxt-crouton — which file controls what, who actually reads it, its drift hazards, and how to change it safely. Use when asking "where is X configured", "who reads this file", "what happens if I edit harness.config.mjs / routing.json / digests.yml / labels.yml / a deploy.config.json / an e2e.manifest.json", when a setting change had no effect (silent no-op), when adding a new config surface, or when a gen-*.mjs --check fails. Trigger phrases: "where do I configure", "what reads this config", "config drift", "the setting didn't do anything", "stale generated doc", "add a new config-as-data surface".

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The map of nuxt-crouton's GitHub workflows and the deploy-pipeline anatomy — what each workflow gates, how a merge reaches Cloudflare, which secrets/tokens exist where, and how the agent-pipeline workflows chain. Use when a CI check fails and you need to know what it is, when asked "which workflow deploys X / why did staging update / what does this check gate / where is that secret / why didn't my label trigger anything", when wiring a new app into CI, or when debugging a deploy that never fired. Trigger phrases: "what runs on merge to main", "map of the workflows", "WORKER_SECRETS_JSON", "why is the daily sweep not running", "deploy-apps vs deploy-app", "which token does this workflow use".

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How to actually run every kind of test in nuxt-crouton (unit/e2e/typecheck/lint, verified commands) plus the HONEST coverage picture — which packages have real tests vs zero, the dormant skipped/todo cases behind three mocking walls, and what CI truly gates vs what CLAUDE.md claims. Use when asked "how do I run the tests", "is X covered by tests", "what does CI actually check", "why is this suite skipped", "where do I add a test", or before trusting a green build as evidence that something works.

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Day-to-day operation runbook for nuxt-crouton apps — boot any app/POC locally with auth working, seed and inspect the local database (including the miniflare-vs-.data split that makes seeded data "disappear"), turn on the review overlay / eruda, observe deployed staging (tail, smoke, row counts), and capture screenshots. Use when asked to "run the app", "boot velo/fanfare/a poc locally", "why can't I log in locally", "seed the database", "the seed data doesn't show up", "look at the local db", "check what's on staging", "screenshot the app", or "where do the artifacts land".

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The case-law chronicle of this repo's major failures — symptom, root cause, evidence, status, and the rule each battle minted — so nobody re-fights a settled battle or re-proposes a rejected fix. Use when a bug/incident looks familiar ("didn't this happen before?"), when you're about to propose an approach that may have been tried and rejected (e.g. shipping a migration from a package), when someone asks "why does rule X exist" / "why is done-by-lgtm a thing" / "what was incident 988", or when a bug-archaeology run hits the shallow-clone wall and needs the pre-June-2026 history. Trigger phrases: "has this broken before", "why can't packages ship migrations", "why did graduation fail", "why is the green-build lesson a rule", "what did we learn from incident NN". (For a live "build is green but the app is broken" symptom, start at crouton-diagnostics-index. NB keep this description free of any space-then-hash sequence — an unquoted YAML scalar treats space-hash as a comment start and silently truncates.)

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Hunt AADGraphActivityLogs and MicrosoftGraphActivityLogs in Microsoft Sentinel/Log Analytics for fingerprints of offensive Entra ID tools such as ROADtools, AADInternals, and AzureHound.

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Configures Microsoft Defender for Endpoint (MDE) advanced protection settings including attack surface reduction rules, controlled folder access, network protection, and exploit protection. Use when hardening Windows endpoints beyond default Defender settings, deploying enterprise-grade endpoint protection, or meeting compliance requirements for advanced malware defense. Activates for requests involving Windows Defender configuration, ASR rules, MDE tuning, or Microsoft endpoint security.

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Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.

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Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared indicators for improved detection. Use when multiple incidents exhibit overlapping indicators, when sector-wide attack campaigns require cross-organizational analysis, or when building campaign-level intelligence products. Activates for requests involving campaign analysis, incident clustering, cross-organizational IOC correlation, or MISP correlation engine.

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This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

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Run OAuth 2.0 device-code and illicit-consent phishing against Microsoft Entra ID to steal access and refresh tokens, bypass MFA, and pivot across Microsoft 365 services.

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