46건 · 관련도순
Integrate or upgrade cache-dit in SGLang diffusion, the image generation system. Follow guidance for resolving code conflicts, adding cache settings, testing feature combinations, and comparing generated images using quality measurements and logs.
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Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR/DQ/DP. For CD8/class I see mhc-binding-prediction.
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Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.
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Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR/DQ/DP. For CD8/class I see mhc-binding-prediction.
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Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR/DQ/DP. For CD8/class I see mhc-binding-prediction.
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Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.
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Create, edit, or debug parametric 3D CAD parts in CadQuery (Python) that export to STEP for SolidWorks. Use when the request involves CadQuery, .step/.stl export, or modelling a physical part — enclosure, bracket, PCB, motor mount, cam, gear. Also use when a STEP will not open in CAD, or geometry looks correct in STL but is invalid as a solid. Applies a verification discipline before export and documented OCC pitfalls.
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CadQuery (Python 製 3D CAD スクリプティングライブラリ) リファレンス。 Workplane API (lineTo / extrude / revolve / sweep / loft / fillet / chamfer / shell)、 Sketch API、Assemblies (Constraint solver)、Free Function API (hollow / prism / draft / offset2D, History)、 Selectors、Shape / Vertex / Edge / Wire / Face / Shell / Solid、 Import / Export (STEP / DXF / STL / glTF / SVG)、Visualization (VTK, cadquery.fig 非ブロッキング, Jupyter)、CQGI。
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Use this skill when buying another carrier and inheriting their drivers' DQF library. Covers the legal transfer of DQ records, what new owner must verify, and gaps that commonly require remediation.
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Use this skill when sorting paperwork between a driver's personnel file and the regulated DQ file. Covers FMCSA-required documents vs employer documents, audit-scope vs HR-scope, retention differences.
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Use this skill when the user asks about Driver Qualification Files (DQF) — what must be in a DQ file under 49 CFR 391, retention periods, what's required pre-hire vs ongoing, document expiration tracking, audit-readiness for driver files, common DQF deficiencies that auditors find, or how to handle missing documents on existing drivers. Always cite the specific subsection of 49 CFR 391.
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Route DRL-Pytorch reinforcement-learning workflows for standalone PyTorch Q-learning, DQN-family, PPO, DDPG, TD3, SAC, Atari DQN, and Actor-Sharer-Learner scripts.
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amazon-listing-doctor是由buluslan（公众号：新西楼.AI）研发的亚马逊Listing质检Skill，他会基于 CDQ算法 / A9 / COSMO / Alexa 四大底座知识，来给Listing进行全身体检和打分，输出一份诊断报告，帮你揪出"流量加倍、弯道超车"的机会点。 更多跨境电商 AI 实战内容，请关注公众号「新西楼.AI」。 Audits Amazon listing health across CDQ / A9 indexability / COSMO intent-coverage / Alexa discoverability, plus 2026 compliance rules (title 75-char, highlights 125-char, image defects). Pure diagnosis, zero dependencies, works offline. Trigger whenever the user mentions checking/auditing/scoring/diagnosing an Amazon listing's quality — in any language; 中文卖家说 listing 体检 / 质检 / 打分 / 诊断 / 健康度 / 质量分 时同样适用。
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Coordinates DDQ completion with standardized responses, data room preparation, and reference call management for prospective LPs. Use when responding to LP DD, completing DDQs, or managing investor onboarding processes.
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CadQuery でパラメトリックな 3D モデルを設計し、cad-coworker のブラウザ GUI でスライダー微調整 → STL 出力 / パラメータ上書き保存するスキル。ロボットフレーム・ケース・マウント等の設計に。「3D設計して」「CADで作って」「STL出力して」「cad-coworker で調整」「スタックチャンのケース」で使用。
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用户咨询腾讯云产品资源、AWS、阿里云等多云资源时，查看智能顾问架构图、架构目录、架构详情、架构评估结果、绘制架构图、开通智能顾问时、AI智能巡检、AI容量监测、AI混沌演练、AI云诊断、主动预警、架构健康度、云运维问答、云资源查询、云成本优化、安全合规、云资源盘点、闲置资源检查、云产品最佳实践等AIOps、ChatOps、CloudOps操作时使用。
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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 to understand how current a driver's employment application must remain in the DQF. Covers update triggers, when to require re-application, and 3-year retention.
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Use Parallel from the terminal to search the web, extract page content, and conduct detailed research. Add researched fields to tables, discover matching companies or other entities, and monitor sources for changes.
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Manage Obsidian notes through its Local REST API. Search a vault, create or update notes, append content, and execute Obsidian commands from the command line, with JSON output for agent workflows.
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Amazon product detail page scraper: extract full product data from any open Amazon product detail URL (any /dp/{asin} or /gp/product/{asin} page across all Amazon regional TLDs) — returns asin, url, title, brand, price, listPrice, stars, reviewsCount, starsBreakdown (5/4/3/2/1 star percentages), answeredQuestions, inStock, inStockText, delivery, fastestDelivery, returnPolicy, breadCrumbs, features (bullet points), description, bookDescription, thumbnailImage, highResolutionImages, galleryThumbnails, productOverview (Brand/Model/etc.), attributes (tech spec table), attributesMapped (flat key-value), bestsellerRanks (rank + category + url), variantAttributes (currently selected color/size/style), variantAsins, seller (name + id + url), isAmazonChoice, amazonChoiceText, monthlyPurchaseVolume, hasAPlusContent, hasBrandStory, aiReviewsSummary, reviewsLink, productPageReviews (sample), videosCount, locationText, loadedCountryCode. Works on amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg. Use when user mentions Amazon product page, Amazon /dp/, Amazon dp URL, Amazon ASIN scraper, Amazon product detail, Amazon PDP, Amazon product data, Amazon product info, Amazon product fields, Amazon product attributes, Amazon full field extraction, Amazon per-ASIN enrichment, Amazon rating breakdown, Amazon stars breakdown, Amazon bestseller rank, Amazon BSR, Amazon variants, Amazon variant ASINs, Amazon color size options, Amazon feature bullets, Amazon A+ content, Amazon brand story, Amazon AI review summary, Amazon bought in past month, Amazon monthly sales volume, Amazon Amazon's Choice badge, Amazon seller info, scrape Amazon product, enrich Amazon ASIN, Amazon ASIN details, Amazon product review data. Also applies to bulk ASIN enrichment from a list of URLs, competitive product research, brand catalog audits, price and stock monitoring per ASIN, and building a normalized product dataset from a list of Amazon URLs.
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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
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Use when you need data access with Quarkus Hibernate ORM Panache — including PanacheEntity / PanacheEntityBase, PanacheRepository, named queries, JPQL, native SQL, DTO projections (project(Class)), pagination (Page.of()), N+1 avoidance (JOIN FETCH), optimistic locking (@Version / OptimisticLockException), @NamedQuery for validated reusable queries, transactions, @TestTransaction for test isolation, and immutable-friendly patterns. This is the Quarkus analogue to Spring Data for relational persistence. This should trigger for requests such as Review Panache entities or repositories in Quarkus; Improve Hibernate ORM data access with Panache; Add DTO projections, JOIN FETCH, pagination, or optimistic locking to Panache queries; Fix N+1 query problems or add @Version concurrency control in Quarkus Panache; Improve Panache active record versus repository design. Part of Plinth Toolkit
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Route and use the repository's PyTorch reinforcement-learning examples across tabular control, DQN, on-policy actor-critic, and off-policy continuous control.
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