「ses」の検索結果

Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CTCF-only/DNase-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.

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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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Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.

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Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.

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Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.

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Use when a driver has been involved in a crash — regardless of preventability or injury — and the carrier needs to handle the immediate, post-investigation, and return-to-work phases. Covers trauma response, post-accident testing under 49 CFR 382.303, paid time off, retraining, light duty, and re-entry planning.

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Use this skill when the user asks how to build + manage direct shipper relationships — moving from spot market to dedicated lanes, customer scoring + credit assessment, contract terms, dedicated capacity agreements, KPI reporting, and how to handle customer disputes. Reference standard customer-carrier contract terms.

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Use this skill when the user asks about the financial differences between being an owner-operator vs company driver — gross pay, expenses, net pay, capital requirements, risk tolerance, lifestyle, and how to evaluate the path. Reference industry pay surveys.

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Use this skill when a driver has vision in only one eye or significantly reduced vision in one eye. Covers § 391.41 vision standards, monocular driver variance, and depth-perception assessment.

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Use this skill to understand which MVR violations disqualify a driver under federal rules. Covers § 391.15 disqualifications, varying state rules, and CSA impact.

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Use this skill when conducting a self-audit of your DQF library before an FMCSA compliance review. Covers the systematic approach to verifying all 12 documents are current, complete, and properly maintained.

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Use this skill when the user asks about CDL driver wellness, retention strategies, what causes driver turnover, common health issues (obesity, hypertension, sleep apnea, mental health), benefits packages, home-time policies, and how to build a culture that keeps drivers. Reference ATA Workforce Development + FMCSA Wellness Initiative.

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Use this skill when the user asks about driver discipline — when to coach vs warn vs suspend vs terminate, progressive discipline frameworks, at-will employment in trucking, documenting violations, separating "willful violation" from "human error," and how to handle a driver who refuses corrective action. Reference state employment law + ATA SMP.

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Use this skill when the user asks how to interpret a DOT medical examiner's findings — what restrictions mean (corrective lenses, hearing aid, no insulin, etc.), what conditions trigger short-term vs 24-month certificates, vision/hearing thresholds, blood pressure categories, sleep apnea screening, when to seek an exemption program, and how to handle a downgrade or denial. Cite 49 CFR 391.41-49 and the FMCSA Medical Examiner Handbook.

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Use when a carrier asks about disaster recovery (DR) and business continuity planning (BCP) — cyber incident, natural disaster, terminal loss, key-person departure, financial liquidity event, major customer loss, insurance crisis, or fuel supply disruption. Covers risk assessment, continuity strategy, communication plans, and recovery time objectives.

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 this skill to run an internal audit before FMCSA does. Covers what to check, common gaps, and remediation.

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。

Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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Architects the encryption, codec/transport, and premium-feature layer of a group video-calling product, on top of an already-chosen SFU topology. Covers E2EE patterns (Zoom leader-fan-out, Signal pairwise-fan-out, MLS/TreeKEM as in Discord's DAVE, Telegram's ledger consensus), the SFrame (RFC 9605) + WebRTC Insertable Streams mechanism letting an SFU relay encrypted frames it can't read, codec/transport choice (AV1/VP9 SVC vs. simulcast, Opus, congestion control, Media-over-QUIC, WHIP/WHEP), and which "premium" features (virtual backgrounds, live captions, recording, breakout rooms, spatial audio) survive E2EE vs. structurally can't. Use when adding E2EE to an SFU-based call, choosing a 2026 codec/SVC strategy, or deciding which features to disable under E2EE mode. NOT for call topology/platform choice (see webrtc-adhoc-video-chat, which this composes with). NOT for one-way broadcast (see managed-video-streaming-pipeline). NOT for the DM/group-chat text backend (see realtime-messaging-backend-architecture).

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Chooses and designs developer surfaces for an agentic product: SDK, CLI, MCP, GUI, API, webhooks, and pd tube-style listener/sender workflows. Use when deciding why a user belongs in an SDK versus CLI/MCP/GUI, planning Python SDK parity, or making agent invocation easy across languages. NOT for implementing the full SDK, generic API docs, or UI visual design.

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Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

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Schema design, normalization decisions, indexing strategies, and migration safety for relational databases. Use when designing tables, choosing between normalize vs denormalize, selecting index types, planning zero-downtime migrations, or setting up connection pooling. Activate on "schema design", "normalization", "denormalization", "foreign key", "index strategy", "migration", "connection pooling", "composite key", "surrogate key", "soft delete", "polymorphic association". NOT for PostgreSQL-specific query tuning or EXPLAIN analysis (use postgresql-optimization), NoSQL or document database design, or cloud database provisioning.

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Master of text-based visual communication using ASCII art, Unicode box-drawing, and structured diagram notation. Creates clear, maintainable diagrams for systems, processes, hierarchies, relationships, and psychological structures. Proactively generates diagrams to enhance understanding. Activate on visualization needs, system architecture, process flows, psychological mapping, or when complex concepts would benefit from visual representation. NOT for photo editing, vector graphics, or GUI-based design tools.

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Audit or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).

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。