データ・AIのスキル

Plan and execute Convex schema migrations safely, including adding fields, creating tables, and data transformations. Use when schema changes affect existing data.

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Explain an existing Convex app — data model + relationships, public vs internal functions, auth/ownership model, components, a request→data flow — read from the schema and function surface. Read-only.

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Suggest the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.

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Set up Convex backups and run a restore DRILL that proves recovery — snapshot, restore into a throwaway preview, assert the data came back — plus a schedule matched to your RPO and a gated recovery runbook.

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Run an autonomous triage-resolve-review-merge loop to auto-pilot the GitHub issue backlog, resolving everything until done with zero prompts. Don't use for single-issue work (/issue-resolver), triage (/issue-triage), or PR review (/issue-pr-review).

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

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Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.

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。

Designs the data model, API contracts, and structural foundation of the system.

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

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

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

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Build natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea — non-custodial, fork-simulated. 40+ apps: Uniswap, Aave, Lido, Morpho, GMX, Hyperliquid, Polymarket.

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

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Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-c...

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

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You know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.

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Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.

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Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.

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Determine the best Anthropic architecture for your project by analyzing requirements and recommending the optimal combination of Skills, Agents, Prompts, and SDK primitives.

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This skill calculates key financial ratios and metrics from financial statement data for investment analysis

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do ...

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。

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do...

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.

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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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。