「spr」の検索結果

Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medchem query language (MATCHRULE, HASALERT, HASPROP, HASGROUP) for filtering a library at scale. Also trigger on medchem, `import medchem as mc`, RuleFilters, NIBRFilters, CommonAlertsFilters, NamedCatalogs, QueryFilter, PAINS filtering, or structural alerts.

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Query the FDA's public openFDA APIs for post-market drug data — FAERS adverse-event reports, Drugs@FDA approval and submission history, Structured Product Labels including boxed warnings, the National Drug Code directory, recall enforcement reports, and drug shortages. Use this skill to check what a regulator has already concluded about a molecule or its class, to date an approval and count its efficacy supplements, to read an approved indication or boxed warning, and to score a drug-event pair for disproportionate reporting with PRR, ROR, and chi-squared. Also trigger on openFDA, api.fda.gov, FAERS, Drugs@FDA, SPL, NDC, pharmacovigilance, boxed warning, adverse event report, drug recall, or safety signal.

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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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Use when explaining branch predictors, mispredict penalties, speculative execution, Spectre or Meltdown mitigations, or branchless code. Not for pipeline stage theory: use cpu-pipelines-and-hazards.

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Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading. Not for mispredict cost: use branch-prediction-and-speculation.

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Use when asked to build spring, easing, gesture, and choreographed animations with correct physics and reduced-motion support. Not for auditing existing motion or finding animation opportunities.

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Cyclical dogfooding loop: test AI-AGENT-STORIES.md against the live graph, discover new stories, analyze gaps, fix root causes, re-test, write report. Use when: (1) user says "/gap-loop", (2) periodic dogfooding session, (3) after a release or major feature to re-validate stories, (4) before sprint planning to prioritize product gaps.

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Etsy SEO optimization for existing and new listings. Auto-detects REWRITE (when user pastes a current title + tags + description) or CREATE (when user gives a short product context). Triggers on "rewrite this listing", "optimize my Etsy listing", "fix my title and tags", "create new listing", "make a new Etsy listing". Uses live Etsy autocomplete + competitor SERP scraping for evidence-driven keyword research. Enforces 2026 Etsy rules — tag ≤20 chars, primary keyword in first 40 chars, indexing spread across title/tags/attributes/description/hero alt, NLP-aware writing, trademark stoplist scan. When the user's problem isn't SEO (CTR, conversion, photos, price, saturated niche), it says so directly and routes to concrete next steps. Silently maintains a markdown listing database at ~/etsy-listings/ for cross-session memory on Claude/Cowork. Catches duplicate primary keywords across the user's own listings.

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Use when running Plans.md tasks on a scheduled cadence (fresh context per wake-up, sprint-contract flow, plateau detection, flock guard). Do NOT load for: single-task work (harness-work), planning, review, release.

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Build React animations with Motion (Framer Motion) - gestures (drag, hover, tap), scroll effects, spring physics, layout animations, SVG. Bundle: 2.3 KB (mini) to 34 KB (full). Use when: drag-and-drop, scroll animations, modals, carousels, parallax. Troubleshoot: AnimatePresence exit, list performance, Tailwind conflicts, Next.js "use client".

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分布式训练梯度尖刺 (Gradient Spike) 根因定位。基于梯度监控数据 (trend.db/CSV)、 msprobe dump 统计数据，三阶段渐进式从异常坐标定位逐层深入到算子级根因。 当用户明确指明分析梯度尖刺问题时使用此 skill。 即便用户提供以上数据，但未指明分析梯度尖刺问题时，不可使用此skill。

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多卡分布式训练中的 loss/gnorm 精度溢出检测与根因追溯。基于 MSProbe dump 数据，先跨 rank 定位首次出现 NaN 的源卡，再在源卡上追溯具体的溢出根因算子。 当用户需要：(1) 多卡分布式训练场景下的 NaN/Inf 溢出检测 (2) 找出首先出现 NaN 的源卡 (3) 追溯根因计算算子 (4) loss/gnorm NaN 问题定位 时使用此 skill。

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分析 msProbe compare 精度比对结果文件（CSV/XLSX），定位整网精度问题与数值差异。支持统计量模式，输出分析报告。支持辅助信息协助定位：辅助信息（历史结论/已知根因/已排除算子/嫌疑区域等）建议在请求中附带（不依赖交互工具）。若请求中未附带且运行环境无 AskUserQuestion，则跳过辅助信息收集、照常完成分析，报告中省略辅助信息章节并注明原因。

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当用户提供 MindStudio profiler 采集的性能数据（框架 profiler、msprof 命令行）时，对数据完整性、采集状态及关键配置进行校验，确保后续分析工具能正常运行。

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Compares two Prof datasets (GPU vs NPU / NPU vs NPU) by auto-installing and calling msprof-analyze compare, then parses the result xlsx into HTML report and Chinese xlsx. Invoke when user provides two prof dirs to compare, or has a compare result xlsx to analyze/translate.

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运行程序采集 Profiling 数据，再用 msprof-analyze 分析TFLOPS 与 MFU。

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。

Ascend cluster comparison tool. Invoke when user asks to compare two cluster datasets (DB or TEXT), or to generate cluster_analysis_output from raw profiling data via msprof-analyze and then compare.

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Set up the Expo/EAS account layer for NowStack Mobile: eas-cli, Expo login, EAS project link, easProjectId, and expo-doctor. Required before Windows/Linux cloud builds.

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Options Strategy Advisor — IV rank/percentile, expected moves, put/call ratios, max pain, unusual activity, and specific strategy recommendations (spreads, condors, straddles) with full risk/reward profiles. Triggered by "trade options <TICKER> [bullish/bearish/neutral]".

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Designs viral referral programs with incentive structures, sharing mechanics, and tracking. Covers one-sided, two-sided, tiered, and milestone models with viral coefficient calculations. Use when someone wants word-of-mouth growth, viral loops, invite programs, or says 'referral', 'viral loop', 'word of mouth', 'invite program', 'refer a friend', 'growth loop', 'referral incentive', 'affiliate program', 'ambassador program', 'get users to invite friends', or 'organic growth'. Even casual mentions like 'how do I get my users to spread the word' or 'can users invite others' should trigger this. Includes anti-fraud measures and copy templates for both referrer and referee. Use this whenever user-driven acquisition or viral mechanics are discussed.

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Programmatic React video best practices for Remotion. Use this skill whenever the user is writing, debugging, or rendering Remotion code — compositions, useCurrentFrame animations, interpolate, spring, Sequence timing, <Img>/<Video>/<Audio> from @remotion/media, staticFile, calculateMetadata, the Studio preview, `npx remotion render`, captions, subtitles, voiceover, audio visualization, ffmpeg, transitions, light leaks, transparent video, Lottie, 3D scenes, Mapbox flyovers. Triggers especially on HtmlInCanvas / "html in canvas" requests, CRT terminal effects, glitch effects, video shaders, WebGL video, and any request to scaffold a new programmatic React video. Owns `remotion render` and everything about Remotion code. Do NOT trigger for finished MP4 ad assembly from existing slides (that is `/video-content`) or for the inspiration-to-MP4 reference-video pipeline (that is `/cmo-video`).

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Use this skill when the user wants to download paper PDFs by DOI(s). Triggers: "下载这个/这几个 DOI的 PDF/全文", "把这个 DOI 列表全部下下来", "批量下 dois.txt", "download paper(s) for DOI(s) …", "bulk-download from this Web of Science / WoS savedrecs export". Routes each DOI through publisher TDM APIs (Wiley/Elsevier/Springer) then OA fallbacks (OpenAlex/Crossref/Unpaywall), and — when --use-browser-fallback is set — a final Playwright pass that reuses institutional cookies for ACS/RSC/IEEE/AIP/IOP/APS/Science. DO NOT use for paper *search* (use Semantic Scholar / arXiv instead), Zotero import / metadata sync, or downloading *all references of* a single paper (use the ref-downloader skill).

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Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx) or Word templates (.dotx). Triggers include: any mention of Microsoft Word Documents, such as 'Word doc', 'word document', '.docx', '.dotx', 'microsoft doc'. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a deliverable as a Word or .docx file (to download, email or print), use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or coding unrelated to document generation.

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Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

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。