「frame」の検索結果

Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.

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。

Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

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。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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。

Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017.

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。

Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

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。

Design performant animation systems with Framer Motion, GSAP, View Transitions API, and spring physics. Activate on: page transitions, micro-interactions, scroll animations, layout animations, spring physics. NOT for: CSS-only hover effects (use css-in-js-architect), video/canvas rendering (use creative-coding-expert).

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。

Plan, evaluate, and adopt agent infrastructure for a named workload. Use for agent runtime/framework selection, agent evaluation or observability plans, memory and tool boundaries, infrastructure cost controls, and organizational adoption. Covers workload-first comparison, risk and recovery planning, held-out evaluation, cost accounting, and staged rollout. Not a vendor ranking or a production certification. NOT for implementing a specific agent behavior (agentic-patterns), building a specific agent (ai-engineer), prompt optimization (prompt-engineer), or conventional ETL workflow design (jury_rig-architect).

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。

FLOW framework integration: evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "seo flow", "evidence-led SEO", "find leverage optimize win", or wants stage-specific SEO prompts.

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。

pytest-djangoによるDjangoテスト戦略、TDD方法論、factory_boy、モック、カバレッジ、Django REST Framework APIのテスト。

9D product development framework

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。

[02] META. Create new skills when existing ones don't cover the task. Analyze unique requirements, build framework (Frame → Research → Plan → Execute), integrate risks, and declare new skill. Use when facing novel problems that existing skills can't address.

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。

AVFoundation (AVPlayer / AVPlayerLayer / AVPlayerItemVideoOutput / AVAsset) の落とし穴・文書化されていない実装挙動・debugging チェックリストを集約した reference skill。Swift / Objective-C で AVPlayer を使った動画再生 / seek / scrub / frame stepping を実装・debug するときに発火。VLCKit ではなく **AVFoundation 系** の問題に特化。

Develops iOS/iPhone applications with XcodeGen, SwiftUI, and SPM. Triggers on XcodeGen project.yml configuration, SPM dependency issues, device deployment problems, code signing errors, camera/AVFoundation capture debugging, iOS version compatibility, or "Library not loaded @rpath" framework errors. Use when building iOS apps, fixing Xcode build failures, or deploying to real devices. Not for AVPlayer playback issues (seek/scrub/frame stepping → avfoundation-reference 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。

/ai-fluency-check と入力されたときだけ起動するAIフルーエンシー自己評価スキル。Anthropicの4D AI Fluency Framework（Delegation / Description / Discernment / Diligence）に基づく18項目のルーブリックを使い、対話的に自己評価する。過去の評価記録と比較して成長を可視化し、研究根拠にもとづく改善アドバイスと参考文献を提供する。ユーザーが /ai-fluency-check と明示的に入力した場合のみ使用すること。自動起動・推測起動は一切しない。

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

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。

Presents a risk framework for every configurable security control in NemoClaw. Use when evaluating security posture, reviewing sandbox security defaults, or assessing control trade-offs. Trigger keywords - nemoclaw security best practices, sandbox security controls risk framework, nemoclaw credential storage, credentials.json, api key security, openclaw security controls, nemoclaw security boundary, prompt injection, tool access control.

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。

根据DataFrame中的日期字段创建季节字段，将月份映射为春夏秋冬。

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。

Consumer-Driven Contract Testing (CDC) using Pact framework and OpenAPI-based contract validation. Covers microservice-to-microservice contract verification, provider contract testing, consumer contract testing, contract publishing pipelines, and contract breaking-change detection. USE WHEN: designing microservice APIs, establishing service boundaries, verifying service-to-service contract compatibility, integrating with Pact framework, preventing contract-breaking changes, or setting up a contract testing pipeline in CI/CD. Triggers on "contract test", "Pact", "consumer- driven contract", "CDC", "service contract", "API contract", "breaking change".

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。

Framework-directable information security risk assessment. Identifies threats, evaluates likelihood/impact via a 3x3 matrix, maps findings to any compliance framework, and recommends risk treatment options with prioritization guidance.

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 a named behavioral science framework to any task — persuasion, nudge design, habit formation, conversion, influence, negotiation, cognitive bias, choice architecture, decision framing. The framework backbone every other Maxim skill loops into for psychological validation.

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。

Create, update, refactor, explain, or review Microsoft Agent Framework solutions using shared guidance plus language-specific references for .NET and Python.

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。

Containerize an ASP.NET .NET Framework project by creating Dockerfile and .dockerfile files customized for the project.

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。

Use when (1) building an AI agent that needs to communicate with other agents over the Agent2Agent (A2A) protocol, (2) publishing an AgentCard for discovery, (3) acting as an A2A client or server, or (4) integrating A2A capabilities into an existing agent framework. Solves agent isolation — enabling discovery, capability negotiation, task delegation, and structured data exchange across different frameworks and vendors.

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。

Create HTML-based video compositions, animated title cards, social overlays, captioned talking-head videos, audio-reactive visuals, and shader transitions using HyperFrames. HTML is the source of truth for video. Use when the user wants a rendered MP4/WebM from an HTML composition, wants to animate text/logos/charts over media, needs captions synced to audio, wants TTS narration, or wants to convert a website into a video.

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。