Design an ESP-ready email sequence: subjects, copy, timing, branching, deliverability. "build a welcome sequence"
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Design an ESP-ready email sequence: subjects, copy, timing, branching, deliverability. "build a welcome sequence"
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。
Rédaction de requêtes devant le Conseil de prud'hommes pour contester un licenciement pour faute grave. Utiliser ce skill quand l'utilisateur demande de rédiger une requête CPH, contester un licenciement pour faute grave, préparer une saisine du CPH pour un litige lié à un licenciement, ou créer un document de contestation de licenciement en droit du travail français.
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。
Navigate privacy regulations (GDPR, CCPA), review DPAs, and handle data subject requests. Use when reviewing data processing agreements, responding to data subject access or deletion requests, assessing cross-border data transfer requirements, or evaluating privacy compliance.
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。
Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689). Use when asked to determine GPAI model provider obligations, assess Code of Practice compliance, evaluate transparency or copyright requirements for GPAI models, check systemic risk obligations, build a GPAI compliance roadmap, or prepare for AI Office requests. Covers all three Code chapters (Transparency, Copyright, Safety & Security), the 12 commitments and their measures, systemic risk classification (Article 51), provider obligations (Articles 53 and 55), open-source exemptions, enforcement timeline, Commission/AI Office supervision, and DACH-specific overlays such as BSI cybersecurity and works council implications. Triggers on phrases like "GPAI compliance", "Code of Practice assessment", "GPAI model obligations", "systemic risk evaluation", "AI Act transparency requirements", "GPAI copyright policy", "general-purpose AI compliance".
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Elite incident response and legal compliance guidance for data breaches under GDPR Articles 33 & 34. Use when: (1) User reports a data breach or security incident — including "is this even a personal data breach?" triage, (2) User asks about breach notification obligations or deadlines, (3) User mentions "72 hours", Art. 33, Art. 34, or notification requirements, (4) Discussion involves security incidents affecting personal data, (5) User needs breach risk assessment using ENISA methodology, (6) User mentions "Data Breach" or "Incident" or "Data Leakage" or "Ransomware" or "Exfiltration", (7) User needs to determine Controller vs Processor obligations, (8) Cross-border breach scenarios requiring Lead SA determination, (9) User needs a mitigation playbook or immediate response recommendations, (10) User needs audit-ready breach documentation (.docx) or an EDPB-template-aligned breach notification / evidence file — including follow-up and withdrawal notifications, (11) Breach involves an AI system requiring AI Act Art. 73 serious incident screening.
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。
Expert EU Cyber Resilience Act (CRA) advisor for Regulation (EU) 2024/2847 — mandatory cybersecurity and vulnerability handling requirements for all products with digital elements (PDEs) sold in the EU. Use this skill for gap analysis, product classification (Default / Class I / Class II), conformity assessment route selection, CE marking, SBOM requirements, vulnerability and incident reporting to ENISA/CSIRTs, support period obligations, and manufacturer/importer/distributor duties. Trigger for EU CRA, Cyber Resilience Act, PDE compliance, Annex I requirements, SBOM EU, CE marking cybersecurity, or connected product security EU.
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。
Generate templated responses for common legal inquiries and identify when situations require individualized attention. Use when responding to routine legal questions — data subject requests, vendor inquiries, NDA requests, discovery holds — or when managing response templates.
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。
Universal MCP client for connecting to any MCP server with progressive disclosure. Wraps MCP servers as skills to avoid context window bloat from tool definitions. Use when interacting with external MCP servers (Zapier, Sequential Thinking, GitHub, filesystem, etc.), listing available tools, or executing MCP tool calls. Triggers on requests like "connect to Zapier", "use MCP server", "list MCP tools", "call Zapier action", "use sequential thinking", or any MCP server interaction.
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。
Generate a personalized Second Brain PRD from a completed requirements template. Use when the user has filled out my-second-brain-requirements.md and wants to generate their build plan. Triggers on "create my second brain PRD", "generate my PRD", "build my second brain plan", "/create-second-brain-prd", or after completing the requirements template.
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。
Uses DESeq2 to estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.
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The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provi
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。
cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat
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。
TrailSnap 需求规格、Agent 编码交付、提交与 PR 工作流。Use when implementing TrailSnap requirements, managing platform requirements and acceptance criteria, preparing commits, pushing branches, creating pull requests, or monitoring PR CI in this repository.
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 high-quality pull requests via gh pr create. Use when the user wants to create a PR, submit a PR, open a pull request, submit for review, or push changes for review. Triggers on: create a pr, create-pr, submit a pr, open a pull request, submit for review, make a pr, gh pr create.
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。
Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.
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。
Tests whether a scene is structurally worth generating before any shot is prompted — a five-element engine of Goal, Obstacle, Tactic, Reversal, and Value Shift. Use when the user has a scene, sequence, beat outline, or script and wants it audited or strengthened; when a sequence generates cleanly but lands flat and nobody can say why; when shots look good individually but the run of them does not build; or when the user asks 'is this scene working', 'what's weak here', or 'why doesn't this land'. Upstream of prompting — it decides WHICH shots deserve the credits, not how to write them. Pairs with higgsfield-character-design (who the characters are) and higgsfield-shotlist-director (turning the settled scene into shots).
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。
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
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。
Standardize PRD discovery and drafting for product scope, user outcomes, requirement IDs, and acceptance criteria. Use when creating PRD, product requirements, feature specification, or acceptance criteria plan.
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。
This skill should be used only when the user explicitly asks to use `$ralph-specum-requirements`, or explicitly asks Ralph Specum in Codex to run the requirements phase.
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。
Prepare R packages for CRAN submission by checking for common ad-hoc requirements not caught by devtools::check(). Use when: (1) Preparing a package for first CRAN release, (2) Preparing a package update for CRAN resubmission, (3) Reviewing a package to ensure CRAN compliance, (4) Responding to CRAN reviewer feedback. Covers documentation requirements, DESCRIPTION field standards, URL validation, examples, and administrative 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。
Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
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Watches an open pull request's CI until green and fixes failing checks. TRIGGER when: the user wants to watch a pull request's CI, babysit a PR until it goes green, or fix failing CI checks on an open PR. DO NOT TRIGGER when: opening the PR in the first place → pr; rebasing the branch onto its base → rebase.
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。
Opens a pull request, publishing the current branch as a PR. TRIGGER when: the user wants to open a pull request, publish the current branch as a PR, or take the current work through to an open PR. DO NOT TRIGGER when: only committing changes → commit; babysitting CI after the PR is already open → monitoring-pull-requests.
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。
One-prompt autonomous product design and implementation. Use automatically for broad greenfield UI requests such as 'from scratch', '새 제품/화면을 알아서 만들어줘', or requests that delegate DESIGN.md creation. It decides whether to reuse, establish, refresh, or skip a project design system; asks at most one consequential question batch; then builds and verifies the real surface. Use omd:harness instead only when the user explicitly asks for guided checkpoints.
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。