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Found 1,944 Skills
Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: "AI Projects", "AIProjectClient", "Foundry project", "versioned agents", "evaluations", "datasets", "connections", "deployments .NET".
Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.
Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use when making architecture decisions, refactoring code structure, or evaluating when abstractions are appropriate.
Resolves unresolved GitHub PR review threads end-to-end: evaluates whether each review comment is correct, applies a targeted fix when valid, replies with rationale when not, commits, and resolves the thread. USE FOR: unresolved review threads, PR review feedback, changes requested PRs, PR review URLs (#pullrequestreview-...), fix the review comments, close the open threads, address PR feedback. DO NOT USE FOR: summarizing feedback without code changes, creating new PRs, or read-only branches.
Set up and maintain basic bookkeeping for a solopreneur business. Use when tracking income and expenses, preparing for taxes, managing invoices and receipts, understanding cash flow, or generating financial reports. Covers accounting software selection, chart of accounts, expense categorization, reconciliation, and financial statements. Not professional accounting advice — consult a CPA for complex situations. Trigger on "bookkeeping", "accounting", "track expenses", "financial records", "QuickBooks", "invoicing", "receipts", "profit and loss".
Convert a completed paper into presentation slides (Beamer LaTeX) or poster. Extract key figures, tables, equations, and create a narrative flow for oral presentation. Identified gap in existing tools — designed from best practices.
查询亚马逊商品的历史时序数据，包括价格走势、BSR（畅销排名）趋势、评分变化、卖家数量和月销量，支持多个亚马逊站点的任意ASIN。当用户提到价格历史、价格追踪、BSR历史、BSR趋势、历史定价、价格波动、Keepa数据、排名历史、降价提醒、秒杀历史价格、Buy Box价格趋势、优惠券价格、FBA/FBM价格对比、卖家数量变化、评分趋势、销量历史、price history, BSR trends, Keepa historical data, price tracking, sales history, rating changes, seller count changes, price fluctuation时触发此技能。即使用户未明确提及"Keepa"或"时序数据"，只要其需求涉及亚马逊历史商品级数据（如价格、排名或销量随时间的变化趋势），也应触发此技能。
Split text into contextual chunks for RAG/embedding pipelines. Document segmentation and section extraction using window, tfidf, punctuation, or hybrid strategies chosen by intent.
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
Review contracts for concerning clauses, extract key terms, compare to standard terms, and flag unusual provisions. Use when user needs contract review, legal document analysis, or agreement evaluation.
Query historical search volume of Jungle Scout keywords, returning the exact search volume trend of Amazon keywords on a 7-day cycle, covering 10 marketplaces including the US, UK, Germany, Japan, etc. This skill is triggered when users mention keyword search volume trends, historical search volume, changes in search popularity, keyword seasonality, search volume fluctuations, Jungle Scout search volume, keyword search volume history, keyword trend, search volume over time, seasonal search volume, keyword popularity trend. Even if users do not explicitly mention "Jungle Scout", this skill should be triggered as long as their needs involve viewing the search volume change trend of a certain Amazon keyword over a period of time.
Deep research skill — broad parallel web searches, multi-source validation, confidence tracking, cited Markdown report. Supports 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory landscape), technical (architecture, tools, benchmarks), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, roadmap signals), academic (literature survey, citation networks, key authors), person/org (due diligence on a company or public figure), financial (funding rounds, valuation multiples, revenue signals), legal (IP, patents, litigation, compliance), trend (emerging signals, foresight, scenario mapping), community (ecosystem health, key voices, governance, fragmentation). Use when asked to: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'technology evaluation', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Apply whenever the deliverable is a thorough, sourced report rather than a quick answer. Trigger even when phrased casually: 'look into X', 'what's the deal with Y', 'dig into Z', 'I need to understand the space', 'catch me up on X'.