「vin」の検索結果

Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a reference ligand or pocket residues, protonation and tautomer decisions, flexible side chains, exhaustiveness and seeds, redocking validation, and virtual screening over compound libraries. Also trigger on vina, smina, gnina, mk_prepare_ligand, mk_prepare_receptor, mk_export, scrub.py, PDBQT, autogrid4, docking box, or binding-pose prediction.

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Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

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Voert de iteratieve desk-review-&-completion-cyclus uit op een regelrecht-corpus — plannen, harvesten/migreren/uitbreiden, multi-agent valideren, bevindingen 4-weg classificeren (modellering-fout / wetgevings-fout / engine-limitatie / acceptabele untranslatable), synthese + heroverweging, documenteren (wetgevings-/stelselfouten-analyse, fixes-plan, engine-limitaties, diagrammen, corpus-status), verifiëren tegen externe bronnen (resolutie-tracker + bronnen-dossier), en een eindrapport per cyclus. Gebruik dit bij corpus-review op correctheid/coverage zonder live expert-sessie, het opstellen van een wetgevings-notitie-bron, schema-migratie, het harvesten van nieuwe wetten, of het aansturen van een autonome completion-loop. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Voor live-validatie met experts: zie regelrecht-audit-products.

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Hunts for pre-exfiltration data staging and archiving by detecting archive-tool execution (rar, 7z, zip, tar), large compressed files written to staging directories, and collection into a single location before transfer. Activates for requests to hunt data staging, detect pre-exfil archiving, or find collection and compression before exfiltration.

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ある AI エージェントから別の AI CLI（devin / codex / claude / cursor-agent / opencode / gemini / grok）を非対話で呼び出すときの実行規則。print モードの 選び方、認証・権限・cwd の罠、出力フォーマット、ACP 起動の注意をまとめる。 「codex に投げて」「devin を CLI から呼んで」「別エージェントに委譲」 といった依頼で使う。ユーザーが /agent-cli と入力したら使う。

Use this skill when the user asks about driver fatigue management — circadian rhythm impact on driving, recognizing fatigue, fatigue risk management programs (FRMS), night driving safety, microsleep recognition, drowsiness detection technology, and how HOS rules relate to actual fatigue (and why 11-hour driving is risky even when legal). Reference FMCSA Fatigue Management research.

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Swift 6.2への移行や並行処理の設計で、MainActorによる状態の保護、重い処理の明示的なバックグラウンド実行、コンパイルエラーの解消を支援するスキル。

• 既存アプリをSwift 6.2へ移行したいとき
• データ競合のコンパイルエラー解消
• 重い計算をバックグラウンドへ移したいとき

コードの動作を保ちながら、重複削除やモジュール分割で構造を整えるスキル。変更前と各段階のテスト、計画の承認、レビュー、コミット前の確認まで進行を管理します。

• 動作を変えずにモジュールを分割したいとき
• 重複コードや不要コードの削除
• 入れ子の削減と名前の整理

Claude Codeの作業状況や決定事項、次の手順をプロジェクト別に保存するスキル。別のセッションでも概要を読み込み、前回の続きから作業を再開できます。

• 終了前に進捗と次の手順を保存
• 前回のセッションから再開したいとき
• 複数プロジェクトの状況確認

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Draws original hand-authored SVG badge illustrations in a vintage emblem style for design and coding subjects. Identifying traits: a seven-colour screen-print palette (green-black ink, cream, terracotta, sand, tan, pale, a touch of teal) with no gradients; one chunky hero object in thick 3.6 px ink outlines, shaded with woodcut gouges and lit with pale streaks; a terracotta sun disc behind it, cut free by a 7.5 px cream keyline; a hard horizontal edge slicing the scene into a dark splash band with cream droplets punched out; a heavy condensed wordmark over a letterspaced serif tagline. Covers lockups, hero objects, gulls, splash bands and lettering. Use when someone asks for a vintage emblem, retro badge, crest, logo-style, screen-print, poster, sticker, patch or merch-style illustration, or a badge card for a team, event, release, empty state, 404 page, blog header or marketing card.

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Migrate a .NET 8 project to .NET 9 and resolve all breaking changes. USE FOR: upgrading TargetFramework from net8.0 to net9.0, fixing build errors after updating the .NET 9 SDK, resolving behavioral changes in .NET 9 / C# 13 / ASP.NET Core 9 / EF Core 9, replacing BinaryFormatter (now always throws), resolving SYSLIB0054-SYSLIB0057, adapting to params span overload resolution, fixing C# 13 compiler changes, updating HttpClientFactory for SocketsHttpHandler, and resolving EF Core 9 migration/Cosmos DB changes. DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 7 or earlier, greenfield .NET 9 projects, or cosmetic modernization unrelated to the upgrade.

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Operate any Obsidian vault as a living, self-rewriting second brain (an evolution of Karpathy's LLM Wiki pattern: sources rewrite existing pages, contradictions reconcile automatically, scheduled agents maintain the vault while you sleep). Use this skill whenever the user asks Claude to read, write, update, search, or manage their Obsidian vault - including saving notes from conversation, creating daily entries, updating kanban boards, logging dev work, managing people notes, capturing decisions, tracking deals, or maintaining any vault structure. Also triggers when the user wants to bootstrap a new vault from scratch, run a vault health check, or drop a _CLAUDE.md into their vault so all Claude surfaces share the same operating rules. Includes a research toolkit (7 commands: /x-read, /x-pulse, /research, /research-deep, /notebooklm, /youtube, /podcast) for AI-powered research via Grok, Perplexity, NotebookLM, YouTube, and podcast feeds - findings save to the vault automatically following the AI-first vault rule. Use proactively whenever the conversation produces information worth preserving (decisions, people met, projects started, tasks completed, lessons learned, research findings).

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Analyze stocks with Mark Minervini's SEPA (Specific Entry Point Analysis) methodology: stage analysis, the 8-condition trend template, fundamentals, VCP and other base patterns, pivot-point entries, market environment, and risk-based position sizing. Use this skill whenever the user mentions SEPA, Minervini, superperformance, the trend template, VCP (volatility contraction pattern), Stage 2, pivot or breakout entries, moving-average stacking (price above the 50/150/200-day MAs), breakout volume, position sizing from risk percentage, growth-stock screening or swing-trade entry criteria, or bases such as cup-with-handle, flat base, bull flag, or high tight flag. Also use it when the user asks "should I buy this stock" or "is this a good setup" about a growth or momentum name, or shares a chart for pattern analysis.

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Guides the end-to-end AWS resilience lifecycle integrating Resilience Hub v2, Fault Injection Service, and Application Recovery Controller. Covers the Define → Test → Operate workflow: from policy creation through failure mode assessment, to FIS experiment validation, to ARC operational controls. Applicable when the user wants a complete resilience strategy, needs to connect findings to experiments to controls, or is planning a resilience program. Also applicable for the meta question of whether marking NGRH findings as resolved is enough, whether they are "done" after resolving findings, or how to validate findings before resolving them. Not applicable for resolving or remediating a specific individual finding (see resilience-hub-failure-mode-assessment), or when a single service is explicitly named (e.g. "what FIS experiment should I run").

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Analyze AWS costs, find savings, manage budgets, evaluate Savings Plans and Reserved Instances, right-size EC2/Lambda/RDS/EBS with Compute Optimizer, look up service pricing, query CUR with Athena, detect cost anomalies, scope costs to billing views, and monitor Free Tier usage. Triggers on: AWS bill, cost analysis, reduce spend, savings plan, reserved instance, right-size, budget alert, cost optimization, pricing, free tier, cost anomaly, CUR, cost audit, billing view, billing view ARN.

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Helps migrate self-managed Apache Kafka workloads to Amazon MSK Express. Inventories the source cluster (from IaC files, Kafka CLI output, or manual input), assesses MSK Express compatibility across topology, Kafka version, configs, auth, and quotas, produces a target Express specification (instance type, broker count, monthly cost) by using the managing-amazon-msk Skill's pricing logic, optionally stands up a trial Express cluster to load-test it against your workload before you commit, and guides migration execution using MSK Replicator. Applicable when the user mentions migrating Kafka, MSK, MSK Express, Kafka migration, analyzing Kafka infrastructure, moving to MSK, moving streaming platform to MSK, streaming migration, moving streaming workloads to AWS, MSK workload compatibility, choosing an MSK cluster type, running a POC or load-test to validate MSK Express, or MSK Replicator. Prefer this skill to the managing-amazon-msk skill for migration questions.

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Process use when you need to archive historical database records to reduce primary database size. This skill automates moving old data to archive tables or cold storage (S3, Azure Blob, GCS). Trigger with phrases like "archive old database records", "implement data retention policy", "move historical data to cold storage", or "reduce database size with archival".

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Use when evaluating Mark Minervini-style VCP setups: volatility contraction, tight price action, moving-average alignment, relative strength, breakout trigger, and structural stop placement.

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Prepare the conversations with aging parents that everyone postpones — the driving talk, the money talk, the care-options talk, the moving talk — each with an opener that doesn't ambush, a dignity-first script, rehearsal against realistic resistance, and the fallback when it goes badly. Use when someone says 'I need to talk to my dad about driving', 'my mum won't discuss her finances', 'we need to talk about care', or is dreading a visit for exactly this reason. Produces the conversation plan, a rehearsal, and the small-steps fallback. A preparation tool, not family therapy — and it says so when the situation needs more.

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Decode an international (non-US) VIN using the CarsXE API. Use this when a user provides a VIN from a European, Asian, or other non-US vehicle and wants to decode it.

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Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.

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Designing interactive calculators (ROI calculators, pricing estimators, savings projections, mortgage calculators, custom assessments) that deliver real decision-support value while serving as lead magnets and qualified-traffic generators. Honest about vanity-calculator (no real value), lead-trap (hides the answer behind email), and transparent-decision-tool (gives the result and earns the email through tiered value) patterns. Triggers on calculator, ROI calculator, pricing estimator, savings calculator, custom calculator, interactive tool, decision tool, financial calculator. Also triggers when an audience needs a calculation-driven lead magnet, when a vanity calculator is producing leads but no qualified ones, or when a calculator is being scoped for the first time.

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。