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Use when preloading fonts, asset/network images, Lottie/Rive animations, local JSON/config, warming initial API data, or optimizing Flutter Web startup.

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 setting up firebase_ai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI errors.

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 scaffolding a project, refactoring into layers, creating view models/repositories, configuring dependency injection, or implementing unidirectional data flow (MVVM).

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 setting up Data Connect, writing GraphQL queries/mutations, configuring generated SDKs, handling offline, or applying security rules.

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 logging analytics events, setting user properties, configuring default event parameters, building funnels, or adding screen-view tracking.

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。

Daily cross-platform rankings of AI agent skills (skills.sh, ClawHub, Tencent SkillHub, GitHub, X/HN/Bluesky). Use when the user asks which agent skill to install, what is trending, whether a skill is popular or trusted, or wants to compare skills. Read-only public CSV data; it recommends and links, it does not install anything.

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。

Analyze and transform CSV data using bash tools

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。

Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell 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。

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

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。

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

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。

SQL and NoSQL query optimization techniques, indexing strategies, execution plan analysis, JOIN algorithms, cardinality estimation, and database-specific query patterns

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。

Shared artifact registry, common artifact patterns, and integration validation. Load when tracking data that flows across journey steps or validating horizontal coherence.

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。

Clojure language-specific patterns, data-first modeling, REPL-driven development, and spec

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。

Domain modeling with algebraic data types, smart constructors, and type-level error handling

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。

Evaluation criteria and scoring for data engineering artifact 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。

Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns

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。

Dung khi co data tho tu Meta, TikTok, GA4, CRM hay Google Sheet va can bien thanh insight ra quyet dinh duoc — kem decision log ghi quyet dinh, can cu, tac dong ky vong va ngay review lai. Kich hoat khi user nhac 'phan tich du lieu', 'doc so lieu nay', 'export data ra insight', 'so nay noi len dieu gi', 'lam decision log', 'cohort analysis'. Khong dung cho — chan doan tai sao ads kem thi dung skill 03-danh-gia-hieu-suat; viet bao cao trinh bay thi dung skill 07-bao-cao-marketing.

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。

Dung khi can lich dang bai cho ca thang — chot content pillar va ti le, xep bai theo ngay va khung gio, ma tran tai su dung noi dung, va gan muc tieu cho tung tru cot. Kich hoat khi user nhac 'lich noi dung', 'content calendar', 'thang nay dang gi', 'len lich dang bai', 'ke hoach content thang', 'phan bo noi dung'. Khong dung cho — brief tung bai truoc khi viet thi dung skill 36-content-brief; viet caption thanh pham thi dung skill 37-caption-social; lap plan ky sau tu data thi dung skill 40-next-content-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。

Dung khi can TAO RA anh hoac prompt anh bang AI — anh thuong hieu ca nhan, logo, key visual campaign, anh dang hang ngay, editorial, infographic, mockup web, quote graphic. Tu doc brand identity trong context roi dung prompt, gen anh hoac xuat prompt cho MidJourney, DALL-E, Imagen, Leonardo. Co che do nhan anh reference va tra ve master prompt copy-paste duoc. Kich hoat khi user nhac 'thiet ke anh', 'lam logo', 've banner', 'prompt MidJourney', 'tao key visual', 'lam anh giong anh nay', 'gen anh AI', 'doi mau thuong hieu tren anh'. Khong dung cho — viet brief giao designer lam thi dung skill 42-brief-hinh-anh; quy trinh asset ca campaign thi dung skill 41-campaign-asset-list; video avatar AI thi dung skill 24-ai-avatar-production; UI wireframe tuong tac thi dung web-prototype; animation thi dung motion-frames.

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 warm audiences need a plan — retargeting tiers by intent and recency, a different message per tier, frequency caps, attribution windows, lookalike seeds, exclusion rules, and a first-party data base that survives iOS ATT and cookie deprecation. Trigger on 'retargeting', 'remarketing', 'warm audience', 'custom audience setup', 'retarget cart abandoners', 'my retargeting pool is too small'. Also use when the user says people visit once and never come back. Not for — cold prospecting audiences, see `51-audience-research-global`; the pixel and events that feed these audiences, see `53-tracking-setup-global`; email win-back flows, see `14-email-marketing-global`; raising budgets, see `55-scaling-ads-global`.

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 raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for — diagnosing ad root cause, see `03-performance-eval-global`; writing the report a stakeholder reads, see `07-marketing-report-global`; auditing account setup, see `21-ads-audit-global`.

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 needs B2B pipeline work — ICP and account lists, prospecting, cold email and LinkedIn outbound sequences, lead scoring, MQL to SQL handoff, sales decks, objection handling, demo scripts, CRM pipeline hygiene, and RevOps reporting. Trigger on 'find leads', 'prospect list', 'cold email sequence', 'outbound campaign', 'lead scoring model', 'our pipeline is empty', 'sales deck for enterprise buyers'. Also use when the user has a strong product and no route to the companies that need it. Not for — consumer lifecycle email, see `14-email-marketing-global`; the offer being sold, see `31-offer-design-global`; positioning against rivals, see `58-positioning-global`; inbound content, see `36-content-brief-global`.

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。

Scaffolds a structured investigation in scratch/ for empirical research and documentation. Use when the user says "start an investigation" or wants to: trace code paths or data flow ("trace from X to Y", "what touches X", "follow the wiring"), document system architecture comprehensively ("document how the system works", "archeology"), investigate bugs ("figure out why X happens"), explore technical feasibility ("can we do X?"), or explore design options ("explore the API", "gather context", "design alternatives"). Creates dated folder with README. NOT for simple code questions or single-file searches.

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。