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cccskills

「standard」の検索結果

2,339 件 ・ 関連度順

概要と使いどころ

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。

huang-sh/DeepScience42026年7月15日 更新

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

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。

huang-sh/DeepScience42026年7月15日 更新

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

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。

huang-sh/DeepScience42026年7月15日 更新

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

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。

huang-sh/DeepScience42026年7月15日 更新

aeon

無料

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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。

huang-sh/DeepScience42026年7月15日 更新

Drug-combination synergy analysis — quantify whether two drugs together are synergistic, additive, or antagonistic using the standard reference models (Bliss independence, HSA / highest single agent, Loewe additivity, ZIP, and the Chou-Talalay Combination Index). Use when you have measured single-drug and combination effects (inhibition/viability) and need a synergy score. Explains which model to use, what data each one needs, and how to read the score. NOT for looking up pre-computed synergy in a database (use the SYNERGxDB tool / cell-line-profiling skill).

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。

huang-sh/DeepScience42026年7月15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。

huang-sh/DeepScience42026年7月15日 更新

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard 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.

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。

huang-sh/DeepScience42026年7月15日 更新

Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.

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。

huang-sh/DeepScience42026年7月15日 更新

rdkit

無料

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

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。

huang-sh/DeepScience42026年7月15日 更新

dapp

無料

Stellar dApp / frontend development. Covers the JavaScript stellar-sdk (browser + Node.js), Freighter wallet, Stellar Wallets Kit (multi-wallet), Wallet Standard, smart accounts with passkeys, transaction building / signing / submission, smart contract invocation from the client, simulation, and error handling. Use when building a React/Next.js/Node.js app that talks to Stellar — classic operations or smart contracts.

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。

stellar-zk/stellar-zk42026年10月10日 更新

Guide academic writing with standards. TRIGGERS - Use when user needs help with academic-writing-guide related tasks.

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。

Winbda/claude-skills-collection42026年4月6日 更新

Enhance and standardize a property listing photo with auto-contrast, sharpening, and consistent sizing.

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。

iterationlayer/skills42026年6月9日 更新

Generate a non-disclosure agreement PDF with party names, effective date, and standard confidentiality terms.

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。

iterationlayer/skills42026年6月9日 更新

Expert security auditor specializing in comprehensive security assessments, compliance validation, and risk management. Masters security frameworks, audit methodologies, and compliance standards with focus on identifying vulnerabilities and ensuring regulatory adherence.

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。

aibot88/sec_skill_store42026年5月27日 更新

Deploys infrastructure on Oracle Cloud Infrastructure (OCI) with ARM64 instances (Always Free tier eligible). Handles compartments, VCNs, subnets, security lists, and compute instances. Use when: setting up Oracle Cloud infrastructure, deploying ARM64 instances, troubleshooting OUT_OF_HOST_CAPACITY errors, optimizing for Always Free tier. Keywords: oracle cloud, OCI, ARM64, VM.Standard.A1.Flex, Always Free tier, OUT_OF_HOST_CAPACITY, oci compartment, oci vcn

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。

aibot88/sec_skill_store42026年5月27日 更新

Use when deeply validating an ADR's format, approval chain, linkage to standards, or deciding the correct ADR status

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。

aibot88/sec_skill_store42026年5月27日 更新

Validate digital outputs for compliance with accessibility standards (e.g., WCAG, Section 508). Use when reviewing reports, dashboards, documents, or interfaces from pilot projects to identify and remediate accessibility barriers.

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。

aibot88/sec_skill_store42026年5月27日 更新

Web accessibility specialist for WCAG compliance, ARIA implementation, and inclusive design. Use when auditing websites for accessibility issues, implementing WCAG 2.1 AA/AAA standards, testing with screen readers, or ensuring ADA compliance. Expert in semantic HTML, keyboard navigation, and assistive technology compatibility.

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。

aibot88/sec_skill_store42026年5月27日 更新

Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols. USE THIS SKILL WHEN: - You need to audit ad revenue performance or diagnose yield drops - Someone asks about header bidding configuration or Prebid.js optimization - You are analyzing fill rates, eCPM trends, or ad unit performance - A project involves programmatic advertising, SSP integration, or ad serving - You need to verify ads.txt, sellers.json, or supply chain compliance - Someone mentions CPM floors, bid density, or auction dynamics - You are evaluating ad quality controls, viewability, or ad-related UX impact - A codebase integrates Google Ad Manager, AdMob, Prebid, or any SSP TRIGGER PHRASES: "ad yield", "eCPM", "fill rate", "header bidding", "Prebid", "programmatic ads", "ad monetization", "CPM optimization", "ad revenue", "ads.txt", "ad serving", "SSP", "ad unit performance", "viewability"

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。

aibot88/sec_skill_store42026年5月27日 更新

Simulate Joint Commission tracer methodology to assess compliance with accreditation standards across patient care processes and support systems

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。

aibot88/sec_skill_store42026年5月27日 更新

Detects content similarity, verifies standardized citations and abbreviations, and flags potential academic integrity risks; use it before submission, during academic writing QA, or for compliance reviews.

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。

aibot88/sec_skill_store42026年5月27日 更新

PHP Standards Recommendations (PSR) overview knowledge base. Provides comprehensive reference for all accepted PSRs including PSR-1,3,4,6,7,11,12,13,14,15,16,17,18,20. Use for PSR selection decisions and compliance audits.

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。

aibot88/sec_skill_store42026年5月27日 更新

SSL/БСП subsystems guidance and search via 1c-mcp_ssl_server MCP. Use when working with standard library subsystems - users, files, print forms, background jobs, email, common utilities.

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。

aibot88/sec_skill_store42026年5月27日 更新