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cccskills

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3,102 件 ・ 関連度順

概要と使いどころ

Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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。

peacezha/HPClaw32026年10月11日 更新

Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.

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。

peacezha/HPClaw32026年10月11日 更新

Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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。

peacezha/HPClaw32026年10月11日 更新

Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.

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。

peacezha/HPClaw32026年10月11日 更新

Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.

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。

peacezha/HPClaw32026年10月11日 更新

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.

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。

peacezha/HPClaw32026年10月11日 更新

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).

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。

peacezha/HPClaw32026年10月11日 更新

Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

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。

peacezha/HPClaw32026年10月11日 更新

bioskills

無料

Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.

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。

peacezha/HPClaw32026年10月11日 更新

Analyze a Coval audio-quality testing report from runs across different voice, speaking-style, volume, interruption, and background-noise scenarios. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or metric summary and wants evidence-backed next steps such as prompt changes, tool handling fixes, STT/TTS adjustments, trace setup, or expanded audio-scenario coverage.

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。

coval-ai/coval-external-skills32026年9月16日 更新

Build a compact Coval test set from product requirements, observed conversation failures or a coverage gap. Design realistic scenarios with separate expected behaviors, provenance and controlled voice/persona variation.

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。

coval-ai/coval-external-skills32026年9月16日 更新

The TTS/STT/audio-playback system behind the one `speak()` entry point. Use when working on read-aloud, the Listen panel, voice/speed settings, transcription, mic capture, SpeakerButton, iOS playing silence, audio API routes, or files in features/audio/, features/tts/, hooks/tts/, app/api/audio/.

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。

armanisadeghi/ai-matrx32026年10月14日 更新

Elite mobile app image-generation skill for creating premium, app-native screen concepts and flows. Designed for iOS, Android, and cross-platform mobile products. Prioritizes clean hierarchy, comfortably readable text, strong multi-screen consistency, controlled color palettes, non-generic creative direction, textured surfaces, image-led composition, tasteful custom iconography, and clean phone mockup framing. By default, screens should be shown inside a subtle premium iPhone or similar phone mockup with a visible frame, while the main focus stays on the app content itself. This skill generates images only. It does not write code.

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。

waynesutton/waynesutton-ai22026年9月8日 更新

Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.

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。

waynesutton/waynesutton-ai22026年9月8日 更新

Raw mechanical interfaces fusing Swiss typographic print with military terminal aesthetics. Rigid grids, extreme type scale contrast, utilitarian color, analog degradation effects. For data-heavy dashboards, portfolios, or editorial sites that need to feel like declassified blueprints.

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。

waynesutton/waynesutton-ai22026年9月8日 更新

Guide for writing high-quality acceptance criteria and acceptance tests using industry-standard BDD (Behavior-Driven Development) and ATDD (Acceptance Test-Driven Development) practices. Use this skill when creating acceptance criteria for user stories, writing Gherkin scenarios, or implementing acceptance test specifications following Given-When-Then format.

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。

MikeCheng1208/BattleTree22026年7月22日 更新

Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps. Invoke when user mentions abstraction levels, making concepts concrete, or explaining at different depths.

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。

MikeCheng1208/BattleTree22026年7月22日 更新

Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing ...

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。

MikeCheng1208/BattleTree22026年7月22日 更新

Documents minimum SDK/OS version requirements for all OneKey platforms. Use when checking platform compatibility, understanding deployment targets, verifying version requirements, or when user asks if their device can run the project. Triggers on minimum version, SDK version, API level, deployment target, platform requirements, iOS version, Android version, Chrome version, Electron version, can I run, environment check, device compatibility, check environment.

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。

MikeCheng1208/BattleTree22026年7月22日 更新

Development commands for OneKey monorepo. Use when running dev servers, building apps, linting, testing, or troubleshooting build issues. Triggers on yarn, dev, build, lint, test, desktop, mobile, web, extension, ios, android, compile, bundle.

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。

MikeCheng1208/BattleTree22026年7月22日 更新

Cross-platform development patterns for OneKey. Use when writing platform-specific code, handling platform differences, or working with native/web/desktop/extension platforms. Triggers on platform, native, web, desktop, extension, iOS, Android, Electron, platformEnv, .native.ts, .web.ts, .desktop.ts, .ext.ts, cross-platform, multi-platform.

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。

MikeCheng1208/BattleTree22026年7月22日 更新

Develops watchOS applications for Apple Watch with SwiftUI, WatchKit, and WatchConnectivity. Triggers on watchOS app development, Apple Watch complications, watch-iPhone communication, HealthKit integration, workout tracking, and Watch app optimization. Use when building Apple Watch apps, Watch extensions, or companion iOS apps.

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。

jiikko/dotfiles22026年10月13日 更新

SwiftUI architecture patterns, state management with @Observable, view composition, navigation, performance optimization, and modern iOS/macOS UI best practices.

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。

sayasaya8039/ZWG_Terminal22026年6月8日 更新

iOS 26 Liquid Glass design system — dynamic glass material with blur, reflection, and interactive morphing for SwiftUI, UIKit, and WidgetKit.

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。

sayasaya8039/ZWG_Terminal22026年6月8日 更新