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Decompose an unknown quantity into 3-5 estimable factors and produce a defensible order-of-magnitude answer without needing precise data. Load when the user needs to size something without data — market size, resource requirements, effort estimates, user numbers, costs — or when a decision is blocked by "we don't know the numbers". Also triggers on "ballpark this", "rough estimate", "how big is this market", "how long would this take", "how many users", or when deep-thinking diagnoses a sizing/estimation frame. The goal is not precision — it is a defensible answer that enables a decision to be made. Based on Enrico Fermi's estimation 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。

Create focused role prompts for agents in multi-agent topologies. Load when agent-builder needs role prompts for agents, or when a user asks to "create an agent prompt", "write a role prompt", "define agent identity", "write an agent role", "prompt for this agent", "write instructions for this agent", "agent persona". Scope: agent role prompts only (v1). System prompts, task prompts, and skill invocation prompts are future TODOs.

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 the smallest, highest-signal context package for an AI coding task — goal, constraints, repo facts, boundaries, and a verification plan. Load when prompts are underspecified, the agent is missing key files or decisions, the user says "use the right context", "here's the repo", or when work is drifting due to missing constraints. Also triggers on "context engineering", "gather context", "what do you need from me", "before you start". Not for cross-session continuity (use memory-startup/memory-recall).

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。

Orchestrator for the eval-output skill suite — evaluate LLM and agent outputs for quality, accuracy, helpfulness, and safety using structured rubrics and LLM-as-judge techniques. Load when the user says "evaluate this output", "score this response", "run an eval", "LLM as judge", "evaluate agent output", "how good is this response", "rate this answer", "eval this", or provides an LLM output that should be assessed for quality. Single entry point for all output evaluation 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。

Score LLM and agent outputs using LLM-as-judge techniques — direct scoring against rubrics or pairwise comparison between two outputs. Includes built-in bias mitigation for position bias, length bias, and self-enhancement bias. Load when the user asks to score an output, judge a response, evaluate against a rubric, compare two outputs, do direct scoring, run pairwise comparison, or says "rate this", "which response is better", "score this against the rubric", "judge this output", "LLM as judge this". Sub-skill of eval-output orchestrator.

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。

Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, and verification.

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 several variations of a design directly inside the running app, on the real page/route, with a live switcher to toggle between them in the browser, then recommend one and fold the winner into the real code. Variants are real implementations judged against real data, layout, and density (not an isolated mockup). Use this skill whenever the user wants to try design options live in the app, prototype changes directly on a real page, add a variant switcher, compare layouts in-context, asks to "build a few versions of this page", "try variants on the real screen", "switch between designs in the app", "implement this with a toggle", or says "real-prototype this". For a safe standalone .html copy that never touches the app, use the prototype skill instead.

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。

Automate College Football Data tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Automate AI ML API tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Automate Asin Data API tasks via Rube MCP (Composio). Always search tools first for current schemas.

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 this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts, and strategic insights.

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。

Help address review/issue comments on the open GitHub PR for the current branch using gh CLI; verify gh auth first and prompt the user to authenticate if not logged in.

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 when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.

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 this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.

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。

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

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, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

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。

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data 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。

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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。

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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。

Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.

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。

Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

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。

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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 when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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。

Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.

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。