「termin」の検索結果

Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.

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Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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Queries ClinVar for variant pathogenicity classifications, ClinGen VCEP curations, and somatic-vs-germline interpretations via REST API, weekly VCF, or bulk XML. Use when determining clinical significance, triangulating conflicting interpretations, or aggregating evidence against the ACMG/AMP framework with ClinGen SVI specifications.

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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.

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Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-seq, STORM-seq) and single-cell long-read (MAS-Iso-seq, scISOr-Seq2) are the chemistries that give per-cell isoform structure. Tools include MARVEL (R, Smart-seq integrated), BRIE2 (Bayesian PSI with regulatory features and ELBO_gain test), scQuint (junction-cluster, plate-based; not for 10X), SpliZ (annotation-free Z-score), Psix (graph-smoothness regulated AS), and Sierra (alternative polyadenylation, often confused with AS). Use when analyzing isoform usage in scRNA-seq, identifying cell-type-specific splicing, or determining whether scRNA-seq chemistry supports splicing analysis at all.

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Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.

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Build and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a GLOSSARY.md, or recording or editing an ADR.

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。

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

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。

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.

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Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

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Interactive Flutter Widget implementation assistant that guides you through widget implementation decisions by asking structured questions about state management, widget type (screen/component), and state sharing. Use this skill when you need help determining the appropriate Flutter widget architecture for your implementation.

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Rebuild the vault dependency graph (_graph.md) via the deterministic generator script. /graph forces a full rebuild; /graph last (run after every /sync) rebuilds only if the vault changed; /graph [N] is treated as last. The generator reads the vault and writes _graph.md directly, so the file never streams through a model response.

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。

Rebuild the vault dependency graph (_graph.md) via the deterministic generator script. $graph forces a full rebuild; $graph last (run after every $sync) rebuilds only if the vault changed; $graph [N] is treated as last. The generator reads the vault and writes _graph.md directly, so the file never streams through a model response.

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。

Entry point for new work. Determines the best action path or work decomposition (update existing spec, create new spec, mixed decomposition, or no spec needed) and refines ideas through structured dialogue.

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。

Produce a deterministic, read-only evidence map of the repository for SDD Composy (languages, manifests, commands, modules, boundaries, staleness) and publish the context bundle only on request. Use before a PRD or TechSpec, or when the map is stale — 'mapear o repositório', 'atualizar o contexto do código', 'what does this codebase look like for SDD'. Do NOT use for architecture decisions, code review, or repository exploration without an SDD workspace.

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。

Design deterministic CI quality gates that read the normalized manifest verdict independently from report-export process status.

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Use when an approved PRD must become an executable roadmap — 'gerar roadmap do PRD', 'quebrar o PRD em épicos e features', 'cadeia de dependências', 'roadmap with dependencies' — Phase/Epic/Feature/Task with explicit dependencies, waves and critical path, validated by a deterministic checker, with an approval gate. Do NOT use for a PRD that is not approved (prd- create/prd-refine first).

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。

Use when the user, inside an Orca terminal, asks Hermes to resolve, dispatch, orchestrate or supervise GitHub issues with Orca workers — 'orquestrar as issues 12, 15 e 18 no Orca', 'despachar as issues com label ready', 'coordenar workers do Orca para estas issues', or to resume such a Run after compaction. Do NOT use for a single unsupervised hand-off to another agent (Orca CLI directly), for working on one issue in this session (pwdev-feat feat-loop), or outside Orca.

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。

Use when the user wants the existing codebase analyzed for planning — 'mapear o código', 'analisar o projeto', 'atualizar o contexto', 'map codebase' — or when a planning skill reports a missing or stale map. Runs a deterministic read-only evidence scan and writes codebase.md plus the architecture, conventions, testing and concerns context documents under .planning/feat/. Do NOT use for code review (feat-review), architecture decisions, or writing the project instructions file (feat-setup).

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Revisa e melhora copy existente através de 7 passes focados (clareza, voz, e daí, prove, especificidade, emoção, risco zero) somados à detecção determinística de padrões de escrita de IA. Use quando o usuário disser "revisar copy", "melhorar esse texto", "revisão", "está parecendo IA", "polir", "dar uma olhada nesse texto", "feedback de copy", ou logo após qualquer skill copy-* gerar um rascunho. Não reescreve do zero — aprimora preservando a mensagem central.

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。

Quality gates determinísticos para PostgreSQL. Use quando o usuário pedir "quality gate PostgreSQL", "quality gate Postgres", migrations ou schema drift PostgreSQL, constraints ou índices PostgreSQL, EXPLAIN, plano de consulta ou baseline para PostgreSQL.

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。

Quality gates determinísticos para PHP e Laravel. Use quando o usuário pedir "quality gate PHP", "quality gate Laravel", "PHPStan", "Larastan", "Pest", "PHPUnit", "PHPMD", "Composer Audit", cobertura PHP ou baseline PHP.

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Estratégia de quality gates determinísticos e adoção incremental para CI/CD. Use quando o usuário pedir "quality gate", "gate de qualidade", "política de bloqueio", "baseline", "ratchet", "qualidade no CI" ou "SonarQube".

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。

Quality gates determinísticos para Vue.js, JavaScript e TypeScript. Use quando o usuário pedir "quality gate Vue.js", ESLint, vue-tsc ou Vitest em projeto Vue.js, cobertura, complexidade, bundle, acessibilidade ou baseline para Vue.js.

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。