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概要と使いどころ
Owns brand, demand generation, content, communications, and how the market understands what the business does. Use this to set marketing strategy, allocate budget across channels, decide positioning and messaging, judge whether a campaign is worth running, diagnose why demand has stalled, or arbitrate between brand-building and short-term acquisition. Also use to decide which marketing work to stop.
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Runs the ongoing relationship with accounts after the sale — segmenting coverage against account value, building a health score that predicts rather than describes, running reviews customers find worth attending, forecasting renewals honestly, and finding expansion that follows usage instead of quota. Use this to design a customer success motion, decide who gets a named contact, work out why renewals surprise you, or fix a health score everyone ignores.
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Builds large sets of search-targeted pages from a template and a dataset — the location, comparison, integration, and use-case pages that capture long-tail demand at scale. Use this when there is a repeating query pattern with real volume, when a dataset could answer many similar searches, or to judge whether a programmatic approach is viable before building it.
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Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.
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Evaluates where to spend limited capital — investment appraisal, hurdle rates, payback, and comparing proposals that are not alike. Use this to evaluate an investment or major purchase, compare competing funding requests, set a hurdle rate, decide between building and buying, or review whether past investments delivered what was claimed.
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。
Structures partnerships that change what the business can do — technology integrations, channel and reseller arrangements, joint ventures, and OEM relationships. Use this to evaluate or structure a strategic partnership, decide between partnering and building, negotiate commercial terms of an alliance, or diagnose a partnership that is signed but not producing. For audience-borrowing partnerships, use `marketing:partnership-marketing`.
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Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
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。
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP 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。
Idiomatic Go patterns, best practices, and conventions for building robust, efficient, and maintainable Go applications.
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Generate GitHub Actions CI/CD pipeline configurations for automated building and testing of library and package projects. Use when creating or updating CI workflows for npm packages, Python packages, Go modules, Rust crates, or other library projects that need automated build and test pipelines. Includes templates for common package ecosystems with best practices for dependency caching, matrix testing, and artifact publishing.
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Run the CI gate on your machine so it agrees with the runner — deriving the exact command, paths, markers, and env from the workflow file instead of the Makefile, unblocking gate steps that short-circuit and hide the next failure, pinning the linter version CI resolves, building the interpreter/toolchain environment the runner builds, and confirming the run is green instead of explaining a red job away. Use when a check passes locally but fails in CI (or the reverse), when a lint/format job goes red on an untouched file, when setting up a local dev loop for an unfamiliar repo, or before pushing a branch you expect to merge.
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Builds robust Python MCP (Model Context Protocol) servers with FastMCP — tool design, error contracts, event-loop-safe blocking work, subprocess/CLI wrapping, single-file vs packaged distribution, global-state-free testing, and prompt-injection awareness. Use when writing an MCP server, exposing a tool or CLI to an LLM client, debugging tool registration/packaging, or testing MCP tools.
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This skill is applicable when using LaminDB. LaminDB is an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR-compliant. It is suitable for managing biological datasets (scRNA-seq, spatial transcriptomics, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakes, or ensuring data lineage and reproducibility in biological research. It covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integration with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
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Designs discovery, modeling, and validation workflows for prognostic biomarkers in biomedical and clinical research. Always use this skill when the user needs a prognostic biomarker study blueprint rather than a diagnostic test protocol, predictive biomarker design, treatment recommendation, or a completed manuscript. Focus on endpoint family, follow-up horizon, time scale, candidate marker strategy, model-building logic, risk stratification framework, and internal/external validation requirements. Do not invent cohort size, event rate, assay readiness, literature support, or validation access.
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Use when building XGBoost models on tabular data and returning feature importance ranking outputs. Supports binary classification and regression with automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots.
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 building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.
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Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for: differential expression testing, gene clustering heatmaps, single-cell clustering workflows.
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 building a decision tree model in R and generating feature importance ranking outputs. Supports classification and regression, automatic task detection, parameter validation, model evaluation summaries, and exports of feature-importance tables and figures.
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 building a ceRNA regulatory network from a key gene list by combining bundled miRNA-mRNA and miRNA-lncRNA database files, with flat-file CSV exports and PDF visualization in a single output directory. NOT for: differential expression, single-cell analysis, enrichment analysis, or workflows without a key gene list.
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Use when building a binary classification model from an expression matrix or other omics feature matrix with LASSO logistic regression, cross-validation, and coefficient path visualization. NOT for: multiclass classification, survival/Cox models, or ordinary linear regression.
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Guides Metronome usage-based billing integration decisions — event ingestion (single and batch, idempotency, billable metrics), contract design (rate cards, overrides, dimensional pricing, products), invoicing lifecycle (grace periods, finalization, Stripe sync), credit and commit management (prepaid, postpaid, thresholds, auto-recharge), and Stripe integration (arrears invoicing, tax providers, line item limits). Use when building, modifying, or reviewing any Metronome integration — including ingesting usage events, creating customers, contracts, or rate cards, designing billable metric group keys, managing credits and commits, configuring invoicing, syncing invoices with Stripe Billing, billing AI applications for token usage, setting up self-serve (PLG) pricing, reviewing customer health, commit burn, portfolios, or renewals, or migrating from Stripe Billing Meters to Metronome.
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Use this skill when the user asks about Stripe Connect configuration, charge patterns, Dashboard access, or how to get started with Connect, is building a marketplace, platform, multi-vendor store, gig platform, or subscription platform, needs to pay out sellers, vendors, or providers, mentions split payments, revenue sharing, multi-party payments, or similar payment distribution concepts, provides a company URL or business description for a recommendation, builds SaaS that routes money between parties (for example, POS, booking, invoicing — not operational SaaS without payment routing), asks about onboarding or KYC for merchants, sellers, and vendors, mentions connected account Dashboard or responsibility configurations, or asks about payment flows, white-label payments, or embedded payments.
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。
Frontend development skill for React, Next.js, TypeScript, and Tailwind CSS applications. Use when building React components, optimizing Next.js performance, analyzing bundle sizes, scaffolding frontend projects, implementing accessibility, or reviewing frontend code quality.
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。