「rl」の検索結果

Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.

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Use the open-source free `coverlet` toolchain for .NET code coverage. Use when a repo needs line and branch coverage, collector versus MSBuild driver selection, or CI-safe coverage commands. USE FOR: coverlet setup; CI line or branch coverage; choosing between collector and MSBuild drivers. DO NOT USE FOR: coverage report rendering by itself; repos that intentionally use a different coverage engine. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Design Microsoft Orleans systems from each primitive's purpose and failure model. USE FOR: grains, digital twins, state versus databases, transactions, messaging, streams, timers, reminders, Durable Jobs, stateless workers, grain services, startup, and hosting. DO NOT USE FOR: other actor stacks, batches, relational-only CRUD, or advice without an Orleans decision. INVOKES: inspect version and topology, choose the primitive, implement, and validate.

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视频分镜拆解技能（自包含，无需外部后端）。使用 FFmpeg 预处理视频并生成分镜数据。(1) 视频 URL 直接处理 `python scripts/process_video.py "<video_url>"`；(2) 本地文件先上传 `python scripts/video_upload.py "<file_path>"` 获取 URL 后再处理。需要本机安装 FFmpeg。

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使用内置 link_reader 函数读取网页、PDF或抖音视频内容。准备 URL 列表。运行脚本 `python scripts/link_reader.py "url1" "url2" ...`。

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byted-emr-skills提供管理火山引擎EMR（火山引擎 E-MapReduce（简称“EMR”）是开源Hadoop生态的企业级大数据分析系统，完全兼容开源）的技能，包括管理EMR on ECS集群、EMR on VKE集群、EMR serverless队列、计算组、作业模板/实例、日志、监控并提供 EMR Agent 智能诊断与知识问答能力。当用户提及“EMR on ECS集群”、“EMR on VKE集群”、“Serverless 队列”、“Serverless 作业”、“SparkSQL/PrestoSQL/Ray/PySpark/SparkJar 作业”、“作业日志”、“作业监控”、“作业诊断”等需求时，应优先使用此技能。

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Enhancement overlay for multi-agent / tool-using coder agents. Encodes the per-agent tool- scoping discipline that role-based frameworks (CrewAI, LangChain) document only as a passing best-practice: which agent gets which tool, and why blanket-sharing every tool to every agent is a correctness and blast-radius risk. Activates when an agent system has tools AND there is more than one agent (or one agent holding many tools). Treat a tool as a capability grant; scope by least-privilege. ENHANCE overlay — read alongside [[crewai]], [[agentsop-http-tool-wrapping]], [[agentsop-llm-tool-idempotency]]. Search keywords: which tools per agent, least-privilege agent, agent tool access, tool permissions, limit agent tools, scope tools to roles.overlay_type: enhancement

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Handles files in Convex: upload URLs, storing blobs from actions, serving with getUrl or an HTTP action, metadata from the _storage table, and deletion. Use when users upload images or documents, when an action fetches a file from a third party, or when a file URL expires unexpectedly.

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Use when user mentions fantasy, magic system, or world-building for fantastical settings - provides fantasy genre conventions, magic system design patterns, and world-building frameworks

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Progressive four-tier URL content scraping with automatic fallback strategy. USE WHEN user says "scrape this URL", "fetch this page", "get content from", "can't access this site", "use Bright Data", "pull content from URL", or needs to retrieve web content that may have bot detection or access restrictions.

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Provides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Creates Lambda handler classes, configures runtime layers, sets up SQS/SNS event triggers, implements warm-up strategies, and optimizes cold starts. Use when deploying PHP/Symfony applications to AWS Lambda, configuring API Gateway integration, implementing serverless PHP applications, or optimizing Lambda performance with Bref. Triggers include "create lambda php", "deploy symfony lambda", "bref lambda aws", "php lambda cold start", "aws lambda php performance", "symfony serverless", "php serverless framework".

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Applies serverless FaaS patterns for event-driven workloads. Use when designing bursty workloads with minimal infrastructure and pay-per-execution cost model.

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Use AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning.

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Route AI-Optimizer reinforcement-learning collection tasks across model-based RL, easy-MARL, offline RL, safe command builders, and repository limitations.

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AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows.

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This skill should be used when the user asks for a "sensitivity reader", "authenticity reader", "cultural review", "is this portrayal okay", "real people in my novel", "defamation", "can I use song lyrics", "epigraph permission", "permissions", "quote permission", "fair use", "AI disclosure", "do I need to disclose AI", "send to my editor", "editorial round", "Word file for my editor", "editor review copy", "co-author", "collaborate on a book", "shared world", "back up my book", "does this echo my source", "similarity check", "check overlap with my earlier books", or wants to run human editorial, ethics, permissions, or collaboration workflows around a story project. NOT for contracts or selling rights (use publishing), or reader feedback rounds and review copies for readers, including the GitHub review-copy setup (use feedback-triage).

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Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.

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Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.

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Fix empty results when paginating Wayback Machine CDX API with wildcard URL queries. Use when: (1) CDX query with url=foo/* and page=0 returns empty/0 bytes but works without page parameter, (2) CDX pagination returns no data for wildcard prefix searches, (3) Need to paginate large CDX result sets using wildcard URL matching. Covers showResumeKey, offset, and page parameter incompatibilities.

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按 scarletkc 本人的自然表达习惯代写、改写、润色和翻译文本，适用于推文、评论、聊天消息、模型或工具体验文、项目介绍、GitHub 文本和正式通信。用户要求撰写可直接使用的成稿、去除 AI 腔，或在翻译中保留本人语气和立场时使用，无需明确点名本 skill。单纯的事实问答、技术分析、代码审查和任务讨论不触发。

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Create, implement, or review captions overlay work with format, accessibility, performance, and visual-verification controls. Use when the requested deliverable depends on captions overlay.

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Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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Select, run, and govern NVIDIA Cosmos world-video generation across Cosmos 3 Generator, Predict2.5, Transfer2.5, downloadable checkpoints, self-hosted NIMs, and hosted preview surfaces. Use for text/image/video-to-world, controlled world transfer, multiview or action-conditioned physical-AI video, local checkpoint pinning, hardware planning, inference approval, and output validation; do not use for Cosmos Reason/Embed-only tasks.

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Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.

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