“r”的搜索结果

制作React视频时，可参考Remotion的实现规则与代码示例，处理动画、音频、字幕、图表、三维内容和转场，并调整素材时长与播放安排。

• 制作文字动画与场景转场
• 为视频添加音频和字幕
• 在视频中展示图表与三维内容

Prepare a Cherry Studio release by checking changes against the published version, writing English and Chinese release notes, updating version metadata, and creating a release branch after review to start the build workflow.

中文简介正在准备中。当前显示英语简介。

• Preparing a patch, minor, or major release
• Preparing a beta or release candidate
• Writing release notes from final user-visible changes

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

中文简介正在准备中。当前显示英语简介。

• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Prepare a professional reference list for job applications, choosing people who can discuss your work. Draft permission requests and briefing materials, organize contact details, and prepare for questions employers may ask your references.

中文简介正在准备中。当前显示英语简介。

• Creating a reference list for a job application
• Choosing former managers or colleagues as references
• Drafting permission requests and briefing emails

Adapt machine learning training to run across multiple GPUs or machines with Ray Train. Covers changes to existing code, comparing training settings with Ray Tune, and saving checkpoints so training can resume after failures.

中文简介正在准备中。当前显示英语简介。

• Adapting existing PyTorch code for multiple GPUs
• Training HuggingFace models across multiple machines
• Comparing learning rates and batch sizes with Ray Tune

Build 3D scenes in React from JSON specifications using json-render. The skill covers shapes, lighting, model loading, materials, cameras, and view controls, plus connecting component definitions to their rendering implementations.

中文简介正在准备中。当前显示英语简介。

• Build a 3D scene from JSON
• Display GLTF or GLB models
• Configure lighting, materials, and shadows

Build 3D browser games and interactive scenes inside React apps with React Three Fiber. Organize gameplay logic, coordinate scene and interface state, and connect the 3D experience with standard web menus, settings, and status displays.

中文简介正在准备中。当前显示英语简介。

• Embed a 3D game in an existing React app
• Connect a 3D scene with menus and settings
• Build a 3D product configurator

Interpret MSBuild performance summaries when ResolveProjectReferences appears unusually expensive. Separate time spent waiting for dependent projects from actual task work, then use task self-time rankings to identify a concrete optimization target.

中文简介正在准备中。当前显示英语简介。

• Investigating a high ResolveProjectReferences time
• Separating dependency wait time from task work
• Finding the actual bottleneck in a build log

Investigate API requests that fail to replay because of client-generated signatures or anti-bot tokens. Start with captured traffic, identify the blocking inputs or signing function, and validate a reproducible request for further security testing.

中文简介正在准备中。当前显示英语简介。

• Investigate signature errors in replayed API requests
• Trace signing functions and their inputs
• Validate a reproduced signer against browser output

Turn feedback about the TRES Claude plugin into a clear submission. The skill gathers relevant details, organizes bugs, ideas, or praise, and previews the report and conversation excerpt for approval before sending.

中文简介正在准备中。当前显示英语简介。

• Report a plugin bug with steps to reproduce it
• Suggest a feature or a new skill
• Report incorrect data returned by TRES MCP

Review academic manuscripts for argument quality, causal evidence, statistical methods, citations, and presentation. Receive a structured report with strengths, major concerns, suggested improvements, and likely referee objections tied to specific parts of the paper.

中文简介正在准备中。当前显示英语简介。

• Reviewing a manuscript before submission
• Checking the assumptions behind causal claims
• Anticipating referee objections and possible responses

Turn records from a stopped NVFLARE Auto-FL optimization campaign into a reproducible Markdown report and JSON summary. Summarize improvements, retained candidates, failed ideas, and literature outcomes, with a refreshed progress plot when plotting dependencies are available.

中文简介正在准备中。当前显示英语简介。

• Create a final report after an optimization campaign stops
• Summarize baseline scores and retained improvements
• Review successful and unsuccessful literature-based ideas

Helps developers offload remote memory operations from HPC, UCX, or MPI applications to a BlueField DPU, a processor for infrastructure tasks. Covers host-side DOCA UROM setup, supported-operation discovery, completion handling, and error investigation.

中文简介正在准备中。当前显示英语简介。

• Offloading MPI communication work to a BlueField DPU
• Checking supported operations through plugin discovery
• Investigating errors from host-side UROM calls

Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

中文简介正在准备中。当前显示原文说明。

Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.

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无需浏览器即可读取 Reddit 帖子、评论和用户活动，用于搜索话题、总结讨论及整理社区反应。匿名访问无需账号，持续使用或获取详细数据可配置应用凭据。

• 调查社区对某话题的反应
• 总结讨论串及评论
• 查看用户最近发布的帖子

Read current or previous Muse Code session logs to recover context, review decisions and tool results, or reconstruct lost files from recorded changes. It also helps locate the relevant session for a workspace.

中文简介正在准备中。当前显示英语简介。

• Summarizing a previous Muse Code session
• Recovering lost files from recorded changes
• Reviewing earlier requests and tool results

Assess potential data breach impact by reviewing sensitive data and its movement through a codebase. Produce an inventory, exposure rankings, financial estimates, and prioritized safeguards, distinguishing legal source figures from planning estimates.

中文简介正在准备中。当前显示英语简介。

• Inventory sensitive data before a security review
• Trace where customer data could leak
• Estimate breach impact and costs for planning

Retrieve current Azure retail prices and estimate monthly or annual costs by service, region, and configuration. Compare payment options and calculate Copilot Studio credit usage and estimated costs from expected agent activity.

中文简介正在准备中。当前显示英语简介。

• Estimate monthly and annual Azure costs
• Compare prices across regions and configurations
• Compare reservations, savings plans, and spot pricing

Prevent and investigate excessive rendering in Lite React screens. It guides data transformations, context use, and store subscriptions, then checks compiler output and measures rendering time before and after changes to verify their effect.

中文简介正在准备中。当前显示英语简介。

• Investigate lag when selecting rows in Lite
• Review data transformations during rendering
• Design narrower context and store subscriptions

Turn penetration test and red team results into reports for executives and technical teams. Structure findings, explain severity scores and business impact, organize evidence, and document remediation steps and retest plans.

中文简介正在准备中。当前显示英语简介。

• Preparing a final penetration test report
• Rewriting an executive summary for nontechnical readers
• Creating a reusable security reporting template

Investigate how credentials can be exposed in CI/CD, the systems that build and deploy software. Covers environment variables, logs, credential stores, cloud trust settings, and runner tokens, with methods to assess access scope and defensive controls.

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• Review secret exposure in build environments
• Search build logs and caches for leaked credentials
• Assess trust settings between pipelines and cloud services

Collect Trustpilot reviews for a company domain, with filters for ratings, language, dates, and reply status. Get structured review text, reviewer details, and company responses across pages to support reputation research and customer service analysis.

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• Collect low-rated reviews to study customer complaints
• Gather reviews for competitor reputation comparisons
• Collect company replies and their publication dates

Develop a research question, search and verify literature, combine findings, and produce a structured report. Seven modes cover guided research discussions, briefs, literature reviews, fact-checking, and systematic reviews, with statistical pooling of study results when appropriate.

中文简介正在准备中。当前显示英语简介。

• Clarify a research topic through guided questions
• Build an annotated bibliography and synthesize findings
• Check claims against verified sources