「transfer」の検索結果

Assesses the quality and completeness of a genome annotation with BUSCO (conserved single-copy ortholog recovery), OMArk (proteome completeness, consistency, and contamination), CheckM2 (prokaryotic completeness/contamination), and a gene-set sanity panel (gene count, mono-exonic fraction, protein-length distribution, mRNA:gene ratio, coding density). Covers the assembly-BUSCO-vs-proteome-BUSCO diagnostic, what BUSCO-Duplicated really means, why gene count is a vanity metric, and the QC of transferred annotations. Use when judging whether an annotation is good enough to publish or submit, diagnosing a suspect annotation, or comparing annotation completeness across pipelines.

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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.

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Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.

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Use when stuck on a hard problem, decomposition is unclear, a related problem is needed, or explicit looking-back review will improve transfer. NOT for routine execution with a known procedure, rote checklist work, or domain questions that need specialized subject matter expertise instead of heuristic search.

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Deep analysis of knowledge distillation techniques for compressing large models into smaller efficient ones

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Publishes HTML or Markdown as display.dev URLs with public, company, or private access. Supports signup, claiming, sharing, browsing, bounded reads, exact edits, and comment iteration. Use when the user asks to "publish this", "share this", "share with the org", "post this online", "make a private link", "share with [email]", "publish a report", "list my artifacts", "find an artifact", "publish a large file", "publish this generated HTML file", "read this artifact", "search inside this artifact", "change this passage", "make a copy of this artifact", "duplicate version", "create a display.dev account", "claim this publish", "add a company email domain", "transfer an email domain", "change the email domains add-on", "show my referral link", "show referral rewards", "watch comments on this artifact", "respond to comments", or "resolve this comment thread". Use whenever a request contains a display.dev artifact URL (`*.dsp.so/*`). Anonymous publishing returns a 30-day preview and browser claim URL.

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Use when classifying data, setting retention, judging cross-border transfer, or shaping DSR workflow. Triggers on 'how long do we keep this', 'can this data go to the US'.

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Fornece orientação para treinamento RL de nível empresarial usando miles, um fork pronto para produção do slime. Use ao treinar grandes modelos MoE com FP8/INT4, necessitando alinhamento treino-inferência ou exigindo RL especulativo para máxima taxa de transferência.

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Modelo de fundação para segmentação de imagens com transferência zero-shot. Use quando precisar segmentar qualquer objeto em imagens usando pontos, caixas ou máscaras como prompts, ou gerar automaticamente todas as máscaras de objetos em uma imagem.

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Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.

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Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

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Business Email Compromise (BEC) is a sophisticated fraud scheme where attackers impersonate executives, vendors, or trusted partners to trick employees into transferring funds, sharing sensitive data,

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Knowledge base from the DoD Risk, Issue, and Opportunity (RIO) Management Guide for Defense Acquisition Programs (OUSD R&E, Sep 2023 incl. Change 2.2). Use for the defense-acquisition mechanics of program risk management: the RIO definitions (risk/issue/opportunity), the five-step risk process (plan-identify-analyze-mitigate-monitor), 1-5 likelihood/consequence scoring and the 5x5 risk matrix, the four mitigation options (accept/avoid/transfer/control), burn-down plans, issue management (probability=1), opportunity management toward should-cost, cross-program/interface risk, tailoring RIO to the six AAF acquisition pathways (UCA/MTA/MCA/Software/DBS/Services), specialized methods (RMF, MBCRA/Cyber Table Top, Agile metrics, FMECA, ITRA/DTRAM), and institutionalizing RIO via the PRMP, boards (RMB/JRMB/ROMB/RWG), tools, tiered roles, and WBS/IMP/IMS/EVM/TPM integration. This is the DoD-acquisition complement to nasa-risk (general CRM/RIDM theory) and dau-se-guidebook (the SE process frame). Does NOT teach general risk theory from scratch, and defers specialized risk regimes (system safety/ESOH, HSI, detailed cyber/RMF, spectrum) to their own DoD instructions and the relevant packs.

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Verdichtet gebündelte offene Entscheidungen zu EINEM zusammenhängenden, prosa-basierten Gesamtkonzept auf Basis aller empfohlenen Varianten. Zeigt, wie die Teilsysteme nach der Umsetzung konkret ineinandergreifen, statt den Nutzer durch Dutzende isolierte Einzelfragen zu zwingen. Der Nutzer nimmt das Bild als Ganzes ab oder gibt zielgerichtete Korrekturen, die automatisch in Einzelentscheidungen übersetzt und über decision-briefing Phase 4 ins Register zurückgeschrieben werden. Nutze diesen Skill bei "wie sieht das Ganze aus wenn wir allem zustimmen", "zeichne mir das Gesamtbild", "Gesamtkonzept der Entscheidungen", "decision draw", /decision-draw, "wie greifen die Systeme zusammen". NICHT nutzen, wenn Alternativen pro Einzelpunkt diskutiert werden sollen -- das macht decision-briefing; NICHT für den Abhängigkeitsgraphen -- das macht decision-path; NICHT für strukturierte Einzelblöcke mit Optionen und Pro/Contra (auch bei eng zusammengehörigen Gruppen) -- das macht decision-shot; NICHT zur Herleitung einer einzelnen Entscheidung -- das macht decide.

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Parallel geführte Sprachfassungen eines Dokuments (Paper DE/EN, README + README_de, SKILL.md + SKILL.en.md, Website-Texte) synchron halten: fehlende Fassung nachziehen, Abschnitts-Parallelität prüfen, Divergenzen beheben — mit klarer Leitsprache-Regel und kontrolliertem Rücktransfer, wenn die Nebenfassung etwas besser löst. Nutze diesen Skill bei „sind DE und EN synchron?", „zieh die englische/deutsche Version nach", „Übersetzung ist veraltet", bei zweisprachigen Papers/READMEs/Skills, oder als periodischen Check über einen Dokumentbestand. Enthält auch das Expansions-Audit: bewerten, ob ein Projekt/Dokument WEITERE Sprachen verdient (i18n-Eignung nach Zielgruppe, technische Vorbereitung, kein blindes Massenübersetzen).

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Local, private capture of medical data: diagnoses, symptom histories and examination plans. No BACH origin — custom design with its own SQLite store. Strictly local, no cloud transfer.

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Reads one export transaction, order or shipment description and returns a DRAFT export review pack: parties to screen with a screening checklist, an item classification worksheet, a red-flag review, licence-determination questions and open items. Use when the user asks to "prepare the export review for this order", "pre-check this shipment for export control", "who do we need to screen on this deal", "build the classification worksheet for this item" or "assemble the trade-compliance file for this technology transfer". Do not use for vendor security or due-diligence screening of a supplier's questionnaire answers; use vendor-risk-screening-brief instead. Never classifies, screens, clears, decides licence need or releases a shipment; trade compliance does. Drafts for human review; never approves, authorises or signs off.

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Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

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Review data retention, deletion, backup residency, and cross-border data transfer.

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Create an Estuary derivation that maintains a sliding time window of events (e.g. "transfers in the last 24h", "failed logins in the last 5 minutes") using internal SQLite state and readDelay for time-based expiration. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "windowing derivation", "derivation for last-24h events", "rolling-window derivation", "sliding-window transformation", "fraud-detection derivation", "rate-limit derivation", or "time-window derivation".

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在授权渗透测试中系统性挖掘 HTTP 请求走私（HTTP Request Smuggling / desync）。当目标前面有反向代理/CDN/负载均衡（前端）转发给后端应用，且怀疑前后端对 Content-Length 与 Transfer-Encoding、畸形头、chunked trailer 的解析不一致时使用——典型场景：想毒化后端 socket 劫持他人请求、批量窃取会话 Cookie/令牌、缓存投毒、绕过前端访问控制。适用目标类型 Web / REST API（有分层代理）。触发场景包括用户说"测下有没有 HTTP 走私/desync""这个 CDN 后端解析一致吗""能不能毒 socket 偷别人 cookie""CL.TE / TE.CL 打一下"。输出：带 desync 时序证据与原始报文的 finding 报告（含 killed 记录）。

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。