データ分析、機械学習、LLMアプリ、プロンプト設計（8,883 件）
概要と使いどころ
Update user personas from new feedback, pilot data, or research. Use this skill when: - New pilot data or feedback reveals changed user behaviors or pain points - Annual persona review is due (1-2x per year) - New user segments are discovered that need persona documentation - Existing personas need validation against recent data
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Synthesize pilot observations into structured debrief reports and spec updates. Use this skill when: - A pilot has completed and you have observation notes, tickets, or feedback transcripts - You need to turn messy pilot data into actionable spec changes - You want to generate edge case user stories from field observations - You need negative acceptance criteria from pilot bugs - You want a structured debrief report for stakeholder communication
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 Opportunity Solution Trees using Teresa Torres' framework for structured product discovery. Use this skill when: - You need to map a desired outcome to opportunities, solutions, and experiments - You want to decide WHAT to build next based on user research and discovery data - You have workshop findings, pilot feedback, or user research to structure into actionable options - You need a visual tree diagram showing the path from outcome to testable experiments
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。
Behavioral guardrails against the six most common LLM coding failure modes. Apply on every coding task: writing, editing, reviewing, or refactoring.
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。
Document a machine learning model in a structured, transparent format for stakeholders, reviewers, and future maintainers. Use when the user says "document this model", "write a model card", "model documentation", "how should I document my ML model", "bias and fairness report", "model transparency", "what does this model do", "model handoff", "production model documentation", or needs to communicate what a model does, how it was built, where it works, and where it fails.
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Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.
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 statistically rigorous experiments to test data science hypotheses. Use when the user says "design an A/B test", "run an experiment", "test this hypothesis", "is this difference significant", "how many samples do I need", "randomized controlled trial", "causal inference", "uplift test", "holdout group", "significance test", "power calculation", "avoid p-hacking", or needs to determine whether an observed effect is real before making a product or model decision.
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 exploratory data analysis on a dataset and produce a structured report. Use when the user says "explore this dataset", "EDA on X", "analyze this data", "what's in this dataset", "summarize this data", "first look at the data", "understand this dataset before modeling", "data quality check", "describe this dataframe", or wants to understand a new dataset before building models or dashboards.
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。
Write a data schema specification document. Use when the user says "schema spec", "document this schema", "schema design doc", "data model spec", "table spec", "field definitions", "schema contract", "data dictionary", "define these fields", or needs to formally document a database schema, event schema, or API payload schema - even if they don't explicitly say "schema 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。
Write a data pipeline design document. Use when the user says "pipeline design doc", "document this pipeline", "pipeline architecture doc", "data flow document", "how does this pipeline work", "design doc for ETL", "pipeline spec", or needs to capture the architecture, data flow, and operational details of a data pipeline - even if they don't explicitly say "design doc".
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。
Write an ETL operational runbook. Use when the user says "ETL runbook", "pipeline runbook", "data job runbook", "how to operate this ETL", "on-call guide for data pipelines", "what to do when the data job fails", "pipeline troubleshooting guide", "data ops runbook", or needs to document how to operate, monitor, and recover a data pipeline or ETL job - even if they don't explicitly say "runbook".
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。
Write a structured client status report for a consulting engagement. Use when a consultant says "write my weekly status update", "I need to send a status report", "draft the client update", "what should be in the weekly report", "client wants a progress update", "help me write the RAG status", "I need to report on blockers", "end of week update to the client", or "help me communicate what's stuck". Also trigger when someone needs to communicate engagement progress, surface blockers to the client, or flag decisions that need to be made by the sponsor.
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 data dictionary documenting entities, fields, and business rules. Use when the user says "data dictionary", "document the data model", "what fields are in this table", "data definitions", "field descriptions", "CRUD matrix", "entity relationship", "data catalogue", "what data does this system store" - even if they don't explicitly say "data dictionary".
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 interpret financial statements to surface performance signals, quality concerns, and red flags. Use when an accountant says "review these financials", "analyze the income statement", "look at the balance sheet", "what does the cash flow statement tell us", "check the financial health", "ratio analysis", "are the earnings real", "working capital review", "does this company have liquidity issues", or needs to interpret any of the three core financial statements - even if they don't explicitly say "financial statement review". Also trigger when someone shares P&L, balance sheet, or cash flow data and asks what it means.
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。
Parse natural-language quantities, units, dates, and ranges ("5'11\"", "1.5 cups", "72 in to cm", "three days ago", "between 5 and 10 kg", "it's hot") into canonical, validated values with issue codes and spans — and humanize them back. Use when a form input, LLM tool schema, MCP handler, or import pipeline takes free-text measurements/units/dates. Covers Standard Schema fields (quantityField/dateField/lingoObject) for the AI SDK and MCP, the headless <lingo-input>, unit conversion, and format/humanize round-trips. Zero-dependency TypeScript, deterministic.
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。
**CONVERSION SKILL** — Convert binary/Office documents to Markdown for LLM consumption. USE FOR: converting Word, Excel, PowerPoint, PDF, Visio, MS Project files to Markdown, batch document conversion, extracting text from non-readable formats. DO NOT USE FOR: Markdown formatting/linting, general file I/O, web scraping, image generation.
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。
**WORKFLOW SKILL — MANDATORY for ANY hook work.** Create, configure, edit, debug, or review lifecycle hooks for VS Code Copilot (default) and Claude Code. USE FOR: criar/configurar/editar hook, hook do Copilot/Claude/agente, lifecycle hook, PreToolUse, PostToolUse, SessionStart, SubagentStop, Stop hook, UserPromptSubmit, PreCompact, bloquear comando, validar tool call, injetar contexto, guard, .agent.md frontmatter hooks, hooks.json, .github/hooks, .claude/settings.json, hook Node.js, cross-platform, hook não dispara/silencioso/ignorado, permissionDecision, hookSpecificOutput, additionalContext. **DEFAULT = VS Code GitHub Copilot.** Switch to Claude Code only when user explicitly says "Claude" OR workspace has `.claude/`. NEVER assume Claude. DO NOT USE FOR: general coding, creating agents (use agent-creator), creating skills (use skill-creator).
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。
Judges whether a user's input suits the Vibe Creating style of video-prompt writing, and when it does, distills single-scene prompts, multi-shot descriptions, emotional imagery, or mixed input into prompts that are easier for a video model to generate from — while preserving any user-specified dialogue, voiceover, music, sound effects, and other hard constraints. Use when a user wants to turn an idea, story, feeling, or rough/over-specified prompt into a strong text-to-video prompt (Seedance, Sora, Kling, Veo, Runway, etc.), or asks to "rewrite", "improve", "clean up", or "vibe-ify" a video prompt. Do NOT use for long narrative films that need precise word-for-word dialogue sync, industrial shot lists meant to be executed verbatim, or functional/UI demos and step-by-step tutorials.
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。
Enhance and improve existing AI prompts using professional techniques from a 2,500+ prompt database. Adds detail, fixes structure, optimizes for specific models, and applies proven patterns from top-performing prompts. Use when user says "enhance my prompt", "improve this prompt", "make this prompt better", "optimize prompt", "fix my prompt", or "upgrade this prompt".
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 custom AI prompts from scratch using guided workflows and patterns from a 2,500+ prompt database. Supports image, video, and text prompts for Midjourney, Flux, Leonardo AI, DALL-E, Sora, and 12 more models. Use when user says "build a prompt", "create a prompt", "write a prompt", "make me a prompt", "new prompt", or "prompt from scratch".
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。
Adapt and convert AI prompts between different models and platforms. Translates between Midjourney, Flux, Leonardo AI, DALL-E, Sora, Imagen, Stable Diffusion, Adobe Firefly, Ideogram, and more. Handles syntax differences, parameter mapping, and model-specific optimizations. Use when user says "adapt prompt", "convert prompt", "translate prompt", "port to flux", "midjourney to dall-e", or "change 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。
Measure writing-style statistics (sentence length, vocabulary, transitions, phrasing) from 5 to 10 posts you or your client wrote, and produce a voice profile for VOICE.md, blog-persona, and blog-write. Use for "learn my style", "build a writing baseline". It describes style; it does not detect or attribute authorship, and it is not for scoring a post (blog-analyze) or writing brand rules (blog-brand).
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Browse, filter, and explore a curated library of 2,500+ AI prompts organized by 19 categories, 17 models, and 4 output types. View stats, discover trending styles, and export prompt collections. Use when user says "prompt library", "browse prompts", "show categories", "list prompts", "prompt collection", "explore prompts", or "what prompts do we have".
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。
Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Not for verifying statistics (blog-factcheck) or gathering authority sources. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".
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。