「eq」の検索結果

Generate a detailed Buy/Sell/Hold equity research report for Indian stock market (NSE/BSE listed companies). Use this skill whenever the user asks for a stock analysis, equity research, investment recommendation, target price, fundamental analysis, technical analysis, or buy/sell/hold opinion on any Indian company or stock. Triggers on phrases like "analyse [stock]", "should I buy [stock]", "research report for [company]", "target price for [stock]", "is [stock] a good buy", "NSE/BSE stock analysis", etc. Always use this skill for any Indian equity research request — even if the user just names a stock ticker or company and asks "what do you think?"

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 hunting Pwn Request vulnerabilities where pull_request_target workflows checkout attacker-controlled PR code and execute it in a privileged context with access to repository secrets. Trigger on: "pwn request", "pull_request_target", "checkout PR head", "npm install in CI", "lifecycle scripts in CI", "preinstall script", "postinstall script", "package.json scripts CI", "npm ci ignore-scripts false", "actions/checkout ref pull request head sha", privileged workflow running PR code, "Gato-X", supply chain via PR lifecycle scripts.

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。

Execute ENCODE ATAC-seq processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides stage-by-stage Nextflow execution with Docker containers and cloud deployment. Handles Tn5 transposase offset correction, mitochondrial read removal, and nucleosome-free fragment selection. Use when users need to process ATAC-seq data following ENCODE standards. Trigger on: ATAC-seq pipeline, run ATAC-seq, process ATAC-seq, chromatin accessibility, open chromatin, Tn5 shift, TSS enrichment.

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。

Creates GitHub Pull Requests with existing PR detection, branch pushing, and intelligent title/body generation. Use when user requests to create pull request, open PR, update PR, push for review, ready for review, send for review, get this reviewed, make a PR, share code, request review, create draft PR, submit for review, run /create-pr command, or mentions "PR", "pull request", "merge request", "code review", "GitHub PR", or "draft".

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。

Specify a FlexCard before a developer opens Card Designer: the card-state set and the condition that selects each state, the data source type behind each card (DataRaptor, Integration Procedure, Apex remote, SOQL), the action menu users get (navigate, launch OmniScript, Apex, DataRaptor), and every embedded child FlexCard or custom LWC. Produces the artifacts a BA hands to the builder. Trigger keywords: FlexCard requirements, FlexCard BA, FlexCard layout design, FlexCard data sources, FlexCard actions, FlexCard card states, OmniStudio FlexCard scoping. NOT for building the card in Card Designer, NOT for OmniScript step and branching requirements (use admin/omniscript-flow-design-requirements), NOT for standard Lightning record-page component requirements.

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。

Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.

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。

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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。

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

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。

Professional business analysis skill aligned with BABOK® Guide v3 standards. Use this skill when you need to elicit and document requirements, analyze business processes, conduct stakeholder analysis, develop business cases, perform gap analysis, or create business requirements documents (BRD). Ideal for requirements gathering, process mapping (BPMN), financial analysis (ROI, NPV), stakeholder engagement, and solution evaluation. Triggers: "gather requirements", "create business case", "analyze process", "stakeholder analysis", "gap analysis", "BRD", "functional requirements", or requests involving business analysis 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。

SDDワークフロー全体を統括するオーケストレーター。要求管理・タスク計画・実装・逆順レビューの一連のフローを管理する。複数フェーズにまたがるワークフロー管理、エラー・バグの体系的な分析と修正に使用する。要求は requirement-management、設計はPR本文が既定の記録先であり、requirements-defining と software-designing は非推奨。Do NOT use for 個別フェーズのみの作業（requirement-management、task-planningを直接使用すること）。

【非推奨】要求の整合性チェックは requirement-management の reqctl.py validate に統合された。既存の docs/sdd/ を保守する場合にのみ使用する。SDDドキュメントの整合性チェック、実装同期確認、アーカイブ（CLAUDE.md同期含む）、ファイル最適化を行う。Do NOT use for 要求の矛盾検出（requirement-management を使用すること）。

Guidance on writing high-quality requirements: the requirement quality characteristics and the EARS (Easy Approach to Requirements Syntax) patterns — ubiquitous, event-driven, state-driven, optional-feature, unwanted-behaviour — for unambiguous, singular, verifiable requirements, plus defect/anti-pattern review and verification & traceability. Use when authoring or reviewing requirements. Original synthesis citing EARS (Mavin et al.) and NASA public-domain guidance; not a reproduction of ISO/IEC/IEEE 29148 or the EARS paper.

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 the user asks to "build a win-back campaign", "re-engage lapsed subscribers", "run a re-permission / re-consent sweep", or "sunset my dead list"; produces a closed-loop reactivation program — a lapsed-cohort definition, a staged offer ladder, a re-consent (re-permission) capture step, and a sunset-confirm / suppression rule. Owns none of the SEND-N sub-item notes: engagement-decay / sunset is email-sequence-designer's and preference-center / frequency options is preference-frequency-manager's — this skill references those notes, it does not re-emit them. Not for the general lifecycle flows (welcome/cart/post-purchase) — use email-sequence-designer; not for the preference-center / opt-down ladder — use preference-frequency-manager; not for the consent record itself — use consent-registry; not for computing EQS or the N1 veto — use email-quality-auditor. 流失召回/重新授权/沉默用户清理

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 the user asks to "build a preference center", "set up a frequency opt-down ladder", "give people a step-down instead of unsubscribe", or "design a topic/cadence preference page"; produces a preference-center field spec, a frequency/topic opt-down ladder (down-tier paths that substitute for a hard unsubscribe), a preference-to-suppression mapping, and a SEND N-dimension sub-item note on preference-center / frequency options offered. Not for the lifecycle flow map or cadence governance — use email-sequence-designer; not for the consent/suppression record itself — use consent-registry; not for computing EQS or ruling the N1 unsubscribe veto — use email-quality-auditor. 邮件偏好中心/降频阶梯设计/退订替代降档

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 the user provides multiple options and wants a specific 16-minds (MBTI) personality to make the decision in that personality's judgment style. Triggers on Japanese phrases like 「ENTJで決めて」「INFPならどっち選ぶ」「INTJとして判断して」, English equivalents like "decide as ESTJ" / "which would ENFP pick", or 16-minds commands with a `--decide` flag. Requires both a personality and 2+ options to be present or extractable from the request.

MUST USE when source footage or a material folder is provided and material understanding or 03_analysis generation is needed. Trigger not only on explicit analysis requests ('分析して', '素材を見て') but also when the user points at footage and says things like 'この動画で', 'この素材で', or any request that requires understanding the provided media before planning or editing.

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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。

End-to-end alternative splicing analysis from FASTQ to differential splicing results for short-read bulk RNA-seq. Aligns with STAR 2-pass cohort-style, performs junction QC (RSeQC, MaxEntScan, SpliceAI), runs rMATS-turbo and leafcutter for concordant differential analysis, optionally MAJIQ V3 for complex events / heterogeneous cohorts, isoform-switching with NMD/ORF/domain consequences (IsoformSwitchAnalyzeR v2 + DRIMSeq+DEXSeq+stageR DTU), and sashimi visualizations. Use when performing comprehensive splicing analysis from raw bulk RNA-seq data; for variant-driven splice prediction see splice-variant-prediction; for rare-disease single-patient outlier detection see outlier-splicing-detection; for full-isoform PacBio/ONT analysis see long-read-splicing.

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。

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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。

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

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。

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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。

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

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。

Locate CCSBot::EquipBestWeapon in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSBot::EquipBestWeapon" (symbol CCSBot_EquipBestWeapon). This is a non-virtual function - emit a byte signature (func_sig), not an offset. Locate it via cross-references, distinctive constants/strings in its body, or callers of related symbols; verify by decompilation before committing to a candidate. Trigger: CCSBot_EquipBestWeapon, CCSBot::EquipBestWeapon

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。

Locate CCSBot::EquipPistol in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSBot::EquipPistol" (symbol CCSBot_EquipPistol). This is a non-virtual function - emit a byte signature (func_sig), not an offset. Locate it via cross-references, distinctive constants/strings in its body, or callers of related symbols; verify by decompilation before committing to a candidate. Trigger: CCSBot_EquipPistol, CCSBot::EquipPistol

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。