'binding' 검색 결과

Expert-level nuclear physics knowledge. Use when working with nuclear structure, radioactive decay, nuclear reactions, fission, fusion, binding energy, nuclear models, radiation, or nuclear applications. Also use when the user mentions 'radioactive decay', 'half life', 'binding energy', 'fission', 'fusion', 'alpha decay', 'beta decay', 'gamma decay', 'nuclear reaction', 'cross section', 'Q value', 'nuclear reactor', or 'radiation dose'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Cloudflare Workersのコードを本番運用品質のベストプラクティスに照らして作成・レビューする。新しいWorkerの実装、Workerコードのレビュー、wrangler.jsoncの設定、streaming・floating promises・global state・secrets・bindings・observabilityなどのanti-pattern確認で使用する。事前学習の記憶よりCloudflare公式ドキュメントからの取得を優先する。

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Cloudflare Email Service（Email Sending + Email Routing）でトランザクションメールの送受信を実装するスキル。Workers bindingまたはREST APIによるメール送信、Email Routing、Agents SDKのemail handler、Workers・Node.js・Python・Go等への組み込みで使う。到達性、SPF/DKIM/DMARC、wrangler email設定、MCP email tools、coding agentからのメール送信も対象。「Workerにメール機能を追加」のような依頼でも、重要な設定要件があるため必ず使用する。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

大きな自然文構想を feature・architecture・機能間依存へマクロ分解したいとき、ready feature の system-dev-planner package を atomic 登録・binding 別投影したいときに使う。

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tracker_binding に従い dev-graph と Beads を同期したいとき、GitHub Issues/Projects/PR lifecycle を冪等収束したいときに使う。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

在授权测试中核验 Node.js 权限模型（--permission / --experimental-permission，及旧 policy.json）能否被内部 API 绕过。当目标依赖 Node.js Permission Model 做沙箱隔离、需要判断给定 Node 版本下是否存在已知逃逸（inspector 断点、process.binding、fs.statfs/openAsBlob、process.mainModule.require、Module._load、path.resolve 覆盖、Uint8Array 路径、符号链接重命名）时使用。适用目标类型 Node.js 运行时 / 依赖权限模型的服务或 CLI。触发场景包括用户说"这个 Node 权限模型靠谱吗""--permission 能被绕过吗""测下 policy.json 沙箱逃逸""inspector/process.binding 能突破权限吗"。输出：给定 Node 版本下各绕过原语的命中矩阵 + 建议升级版本（含 killed 记录）。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.

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Build or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, create individual components with proper variant sets and variable bindings, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. Also use when the user asks to create or generate any component in Figma — even a single one — since components require proper variable foundations, variant states, and design token bindings to be production-quality. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Document-code change notification system: traceable doc-code binding via git tree hash, git-diff-driven reconciliation plans with confidence/risk metadata, and idempotent execution. Use when maintaining documentation freshness, detecting stale docs, or binding docs to source code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use Cloudflare KV, R2 and D1 from a Worker. Binding config, Worker code, cf commands, local vs remote data, D1 migrations. Use when adding or changing storage bindings or schemas.

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End-to-end CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when the user asks about Household Goods (HHG) movers — 49 CFR 375 + 376 consumer protection rules, FMCSA HHG-specific authority, mover certification, binding vs non-binding estimates, dispute resolution, weight ticket requirements, valuation coverage, and how HHG operations differ from general freight. Cite 49 CFR 375.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenClaw에 전달한 자연어 요청을 Claude Code 같은 외부 코딩 에이전트로 연결합니다. ACP나 acpx로 작업을 시작하거나 이어가며 대화 맥락을 유지합니다.

• 스레드에서 Claude Code 시작
• 외부 에이전트의 기존 작업 계속
• acpx로 직접 지시 전달

Build and review SwiftUI interfaces for iOS and macOS with guidance on state management, reusable views, navigation, and rendering performance. Examples show how to organize data flow and reduce unnecessary screen updates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing state management for SwiftUI views
• Designing navigation with NavigationStack
• Improving list rendering performance