データ分析、機械学習、LLMアプリ、プロンプト設計（8,865 件）
概要と使いどころ
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
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 effective data dashboards with proper KPI hierarchy, chart type selection, and interactive features. Use this skill when the user needs to create a dashboard, choose the right visualizations, organize metrics for different audiences, or evaluate dashboard tools — even if they say 'build a dashboard', 'our reports are confusing', 'which chart should I use', or 'executives can't find the metrics they need'.".
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 or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common 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。
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
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 a compliance check on a proposed action, product feature, or business initiative, surfacing applicable regulations, required approvals, and risk areas. Use when launching a feature that touches personal data, when marketing or product proposes something with regulatory implications, or when you need to know which approvals and jurisdictional requirements apply before proceeding.
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。
"Interpret the three core financial statements (income statement, balance sheet, cash flow statement) to assess business health and performance. Use this skill when the user needs to read financial statements, understand profitability vs cash flow, evaluate a company's financial position, or prepare for investor/board meetings — even if they say 'explain these financials', 'are we making money', 'read this annual report', or 'what do these numbers mean'.".
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。
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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 and maintain Architecture Decision Records (ADRs) for significant technical choices—frameworks, data stores, API shapes, ML platform decisions. Use when documenting a decision, onboarding, or superseding a prior approach.
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。
Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.
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。
"Implement collaborative filtering for recommendations based on user behavior patterns. Use this skill when the user needs to build a recommendation engine from user-item interaction data, find similar users or items, or predict ratings — even if they say 'users who bought this also bought', 'similar users', or 'recommend based on behavior'.".
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。
"Implement content-based recommendation by matching item features to user preference profiles. Use this skill when the user needs to recommend items based on attributes, solve the cold start problem for new items, or build recommendations without collaborative data — even if they say 'recommend similar products', 'items like this', or 'feature-based matching'.".
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。
"Implement Elo rating system to rank items or players from pairwise comparison outcomes. Use this skill when the user needs to rank items from head-to-head matchups, build a competitive rating system, or evaluate relative quality from comparison data — even if they say 'player rating', 'ranking from comparisons', or 'competitive scoring system'.".
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。
"Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, handling of missing data and holidays, or automatic changepoint detection — even if they say 'forecast with Prophet', 'business forecast', or 'easy time series model'.".
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 exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.".
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。
Evaluate LLM agents and tool-using workflows—task success, tool accuracy, latency/cost, safety, and regression suites. Use when shipping agent features, comparing prompts/models, or debugging agent failures.
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。
Auto-activates when generating Product Requirements Prompt (PRP) documents from synthesized design outputs, providing structured templates and comprehensive implementation 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。
Generate project assessment markdown documents from JSON data with WHY/WHO/WHAT sections and GO/NO-GO decision matrix.
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 data models with Pydantic schemas, comprehensive validation rules, type annotations, and relationship mappings.
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。
Multi-language code quality validation (Python, JS/TS, Shell, SQL) — formatting, linting, type-checking, complexity analysis with auto language detection. Use when checking code quality or running linters. Trigger on "lint", "type-check", "quality check", "run the linters", "check 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。
Run SQL queries against psql, BigQuery, or MySQL from the terminal, including natural-language-to-SQL and schema exploration. Use when analyzing data, inspecting DB state, or debugging tables. Trigger on "query the database", "SQL", "show me data from", "explore table".
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。
Systematically add a new memory-system reference to Auto_Memory's reference hub. Use when analyzing a new agent memory system, adding reference/<system>/details.md, saving prompts, or updating reference/hub/technique_map.json.
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 concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.
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 preparing a desktop or web app update for commit, packaging, installer validation, GitHub publication, release asset upload, or old-release pruning. Protects user data and verifies that published artifacts match the tested build.
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。
Frappe-native SQL and ORM guidance for `frappe.db`, Query Builder, `tab{DocType}` tables, permission-sensitive queries, and raw SQL review. Use when writing or reviewing database access in Frappe.
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。