「poi」の検索結果

Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.

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Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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Implements missing-data sensitivity analyses for confirmatory clinical trials including MMRM under MAR (with Kenward-Roger correction), reference-based multiple imputation (J2R, CR, CIR, LMCF per Carpenter-Roger 2013), Permutt delta-adjustment / tipping-point analysis, pattern-mixture identifying restrictions (CCMV, NCMV, ACMV), and the Cro vs Bartlett variance debate. Use when handling missing primary or secondary endpoint data in regulatory submissions following NRC 2010 and ICH E9(R1).

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Predicts ADMET properties using ADMETlab 3.0 (119 platform features, including 77 prediction models with modeled-endpoint uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

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Pick up the bugs a human flagged by pointing at them in the running app, each arriving with the element, the note they typed, and the source file and line. Use when the user says they marked or flagged something, when starting a session on an app someone has been clicking through, when a designer or PM has left feedback in the UI, or when the user describes a problem as "that button there" without saying which file.

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Implement, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, donut, or iOS 26 3D charts; when adding chart selection, scrolling, annotations, axes, scales, legends, or foregroundStyle grouping; when plotting functions with BarPlot, LinePlot, AreaPlot, PointPlot, Chart3D, or SurfacePlot; or when creating heat maps, Gantt charts, grouped bars, sparklines, threshold lines, or spatial visualizations.

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スライド設計規約 slide-rules.md を核に、経営会議品質のスライドを作るスキル。作成前に規約を読み、HTMLパーツ集（基本27＋追加35の62型）から該当パーツをコピーして組み、規約の範囲で型に囚われず調整し、check_deck.py の機械チェック FAIL 0 で仕上げる。型カタログはレイアウトの発想帳であり、合わせる対象ではない。既存の PowerPoint 資料を渡され、そこへ差し込むページを PPTX で求められたときだけ、その資料のマスターの上に直接組む（slide-rules §8.7）。トリガー例:「コンサル品質のスライドを作って」「規約に沿ったデッキで」「型カタログから選んで」「この pptx に足すページを同じ書式で」。

Plan manipulator motion and generate trajectories in MATLAB. Use when the task involves moving a robot arm between configurations or poses — whether the user says "trajectory," "motion," "path," "move from A to B," or "plan." This skill applies regardless of whether the user explicitly mentions collisions or obstacles; if geometry exists in the workspace, collision safety is implicit. Triggers on: manipulatorRRT, manipulatorCHOMP, dlCHOMP, contopptraj, trapveltraj, collision-free path, motion planner, RRT, CHOMP, TOPPRA, path shortening, obstacle avoidance for manipulators, time-optimal trajectory, velocity limits, acceleration limits, occupancyMap3D, meshtsdf, collision objects as environment, trajectory between waypoints, joint interpolation, move between poses, constant velocity, trapezoidal profile, feed rate. Also triggers when a user has IK solutions or waypoints and needs to connect them — whether for time-optimal motion, constant-speed welding/cutting, or any other trajectory parameterization.

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Amazon product differentiation strategy tool. Analyze competitor weaknesses, extract pain points from negative reviews, identify unique selling points from positive reviews, and generate actionable differentiation strategies. Progressive L1-L4 analysis depth. No API key required.

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eBay product differentiation strategy tool. Analyze competitor weaknesses, extract pain points from negative feedback, identify unique selling points, and generate actionable differentiation strategies. Includes eBay-specific seller feedback analysis. No API key required.

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スライド設計規約 slide-rules.md を核に、経営会議品質のスライドを作るスキル。作成前に規約を読み、HTMLパーツ集（基本27＋追加35の62型）から該当パーツをコピーして組み、規約の範囲で型に囚われず調整し、check_deck.py の機械チェック FAIL 0 で仕上げる。型カタログはレイアウトの発想帳であり、合わせる対象ではない。既存の PowerPoint 資料を渡され、そこへ差し込むページを PPTX で求められたときだけ、その資料のマスターの上に直接組む（slide-rules §8.7）。トリガー例:「コンサル品質のスライドを作って」「規約に沿ったデッキで」「型カタログから選んで」「この pptx に足すページを同じ書式で」。

Generate API endpoint code and documentation from specifications. Use when the user wants to create new API endpoints, generate route handlers, scaffold REST APIs, or produce OpenAPI/Swagger specs from code.

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Scaffold Hono API routes for Cloudflare Workers. Produces route files, middleware, typed bindings, Zod validation, error handling, and API_ENDPOINTS.md documentation. Use after a project is set up with cloudflare-worker-builder or vite-flare-starter, when you need to add API routes, create endpoints, or generate API documentation.

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Build responsive, mobile-first layouts using fluid containers, flexible units, media queries, and touch-friendly design that works across all screen sizes. Use this skill when creating or modifying UI layouts, responsive grids, breakpoint styles, mobile navigation, or any interface that needs to adapt to different screen sizes. Apply when working with responsive CSS, media queries, viewport settings, flexbox/grid layouts, mobile-first styling, breakpoint definitions (mobile, tablet, desktop), touch target sizing, relative units (rem, em, %), image optimization for different screens, or testing layouts across multiple devices. Use for any task involving multi-device support, responsive design patterns, or adaptive layouts.

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。

Apply pre-composed aesthetic archetype defaults to jumpstart brand design work covering color, typography, layout, voice, and imagery direction. Use this skill whenever the user wants a starting point for brand visual and verbal direction, references a brand they want to design near (for example 'something like Stripe' or 'editorial like Linear'), needs to converge faster from many directions to one, or wants known-good defaults for a specific industry vertical. Triggers on archetype, design archetype, brand archetype, near to (brand), in the style of, similar aesthetic, design starting point, brand template, vertical-typical, industry-typical. Also triggers when the user has a brief and wants to map it to a known aesthetic family rather than design from first principles.

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Builds high-performance async Python APIs with FastAPI and Pydantic V2 including REST endpoints, authentication flows, async SQLAlchemy, and WebSocket endpoints.

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Clinical study operations — protocol structure, endpoint selection, eligibility design, sample-size and power planning, site feasibility, and documentation readiness. Use when planning, auditing, or costing a study.

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This skill should be used when the user asks to "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".

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。

Test 3-8 price points on CRM-grounded synthetic panels by script, find the optimum. "test these price points"

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Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.

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Detect whether an API endpoint is backed by genuine Claude (not a wrapper, proxy, or impersonator) using 9 weighted rule-based checks that mirror the claude-verify project. Also extracts injected system prompts from providers that override Claude's identity. Fully self-contained — copy the code below and run, no extra packages beyond httpx. Use when the user wants to verify a Claude API key or endpoint, check if a third-party Claude service is authentic, audit API providers for Claude authenticity, test multiple models in parallel, or discover what system prompt a provider has injected.

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Verifies UK case-law citations, pinpoint references and quotations against the official public register — The National Archives' Find Case Law — and statutory references against legislation.gov.uk, before a document that cites them is relied on, served or filed. Every check returns a graded verdict: VERIFIED, MISMATCH (the citation resolves to a different case — the classic AI miscitation), NOT ON REGISTER, OUTSIDE COVERAGE (the register cannot answer — absence proves nothing), or UNCHECKABLE (a law-report citation). The grading exists because there are two ways to get this wrong: citing a case that does not exist, and accusing a real case of not existing. Use when asked to check citations, verify a case exists, confirm a quotation is verbatim, check a pinpoint paragraph, audit a draft's authorities, screen a document for hallucinated cases, or check a statutory provision is in force. Not a substitute for reading the judgment: existence is not authority.

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Collaboration platform configuration methodology for legal matter sites. Site architecture, workflow identification, dashboard design, data quality governance, and user adoption for SharePoint, Teams, and equivalent platforms. M365 is the reference implementation — outputs are platform-agnostic enough to brief IT or build simple automations without becoming a Power Automate manual. Use when setting up a matter site, identifying workflows to automate, designing reporting dashboards, managing platform data quality, or driving user adoption. Trigger on: 'set up the matter site', 'configure SharePoint', 'build a dashboard', 'what should we automate', 'brief IT on this workflow', 'nobody is using the platform', 'data quality is poor', 'set up Teams channel', 'matter site structure', 'alerts and notifications', 'user training', 'platform governance', 'status dashboard', 'what workflows can we automate', 'matter site template'.

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Audit de conformité RGPD complet d'un site internet. Réalise une observation systématique du site selon une checklist de 10 sections (mentions légales, hébergeur, formulaires, newsletter, politique de confidentialité, cookies et bandeau, mots de passe, trackers et mesure d'audience, sous-traitants et transferts hors UE, accessibilité du recueil des droits) plus une annexe 22 items reproduisant les exigences des articles 13 et 14 RGPD. Produit un rapport structuré avec niveau de conformité global, points bloquants (risque 3), points de vigilance (risque 2), recommandations prioritaires et notes techniques. Le skill est tool-agnostique : il fonctionne avec un outil de navigation automatique (Claude in Chrome, Cowork ou équivalent) ou en mode dégradé copier-coller. Triggers : "audit RGPD site", "audit site internet", "vérifie ce site", "scanne ce site", "audit conformité site", "audite la conformité de [URL]", "audit cookies site", "audit politique de confidentialité site".

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。