Loading...
Loading...
Found 2,100 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".
Evaluate verified findings from merge-ready, Greptile, pull-request, CI, security, billing, and other code reviews, then promote durable review gaps into the version-controlled .greptile configuration. Use when a review uncovers a recurring or high-risk repository invariant that Greptile does not capture, when Greptile repeatedly produces a false positive, or when asked to audit or update OpenSEO's Greptile rules and context.
Analyze supply chain operations using the SCOR model across Plan, Source, Make, Deliver, and Return processes. Use this skill when the user needs to optimize supply chain efficiency, evaluate supplier performance, improve logistics, or design an end-to-end supply chain strategy — even if they say 'our deliveries are slow', 'supply chain costs are too high', or 'we keep running out of stock'.
Build AI agents and agentic workflows. Use when designing/building/debugging agentic systems: choosing workflows vs agents, implementing prompt patterns (chaining/routing/parallelization/orchestrator-workers/evaluator-optimizer), building autonomous agents with tools, designing ACI/tool specs, or troubleshooting/optimizing implementations. **PROACTIVE ACTIVATION**: Auto-invoke when building agentic applications, designing workflows vs agents, or implementing agent patterns. **DETECTION**: Check for agent code (MCP servers, tool defs, .mcp.json configs), or user mentions of "agent", "workflow", "agentic", "autonomous". **USE CASES**: Designing agentic systems, choosing workflows vs agents, implementing prompt patterns, building agents with tools, designing ACI/tool specs, troubleshooting/optimizing agents.
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.
Apply signaling theory (Spence, 1973) to analyze how agents communicate private information through costly, credible signals under information asymmetry. Use this skill when the user needs to evaluate whether a corporate action serves as a credible signal, analyze dividend or IPO signaling, assess separating vs pooling equilibria, or when they ask 'why do firms pay dividends', 'is this signal credible', or 'how does underpricing signal quality'.
Analyze a cyclical business - locate where it sits in its cycle (with evidence), normalize its earnings vs peak/trough, run the P/E inversion that trips up naive valuation, read the structural top/bottom tells, and end in a disposition framed as thinking (never buy/sell). A decision-support thinking tool, not financial advice. A standalone skill, independent of /munger and /investment-checklist. Use when the user invokes /cyclicals, says "analyze this cyclical", "where is X in its cycle", "normalize earnings for X", "is it time to buy cyclicals", or pastes a cyclical business (commodity, metals, semis, autos, housing, shipping, airlines, chemicals, paper) to think through.
Create multi-criteria comparison charts using traffic lights or Harvey balls. Use for option evaluation, competitive comparison, and executive dashboards.
Evaluate whether a development ticket (user story, feature request, bug report, etc.) is ready for development, and provide specific, actionable feedback if it is not. Use this skill whenever the user asks to triage, evaluate, assess, review, or check the readiness of a ticket, story, issue, or work item. The ticket can come from anywhere: pasted inline, read from a file, fetched from Jira or another tracker via MCP, or any other source. Also use this when a user asks "is this ticket ready?" or "what's wrong with this ticket?" or wants to improve a ticket's specification.
Use when writing R code that manipulates expressions, builds code programmatically, or needs to understand rlang's defuse/inject mechanics. Covers: defusing with expr()/enquo()/enquos(), quosure environment tracking, injection with !!/!!!/{{, symbol construction with sym()/syms(). Does NOT cover: data-mask programming patterns (tidy-evaluation), error handling (rlang-conditions), function design (designing-tidy-r-functions).
Amazon profit margin calculator for sellers. Calculate cost breakdowns, profit margins, break-even points, and get pricing recommendations. Supports single product analysis and batch calculations. Input product cost, shipping, FBA fees, and get instant profitability analysis. No API key required. Use when: (1) evaluating new product profitability before sourcing, (2) diagnosing existing product margins, (3) adjusting pricing strategy, (4) analyzing multiple SKUs at once.
Analyze stocks using Mark Minervini's SEPA (Specific Entry Point Analysis) methodology. Use this skill whenever the user mentions SEPA, Minervini, superperformance, trend template, VCP (Volatility Contraction Pattern), Stage 2 uptrend, stage analysis, pivot point breakout, or asks about growth stock screening criteria. Also triggers when the user wants to evaluate whether a stock meets swing trading entry criteria, check moving average alignment (bullish stacking: price above 50MA above 150MA above 200MA), assess breakout quality with volume confirmation, calculate position sizing based on risk percentage, or identify consolidation patterns like cup-with-handle, flat base, bull flag, or high tight flag. Use this skill even when the user simply asks "should I buy this stock" or "is this a good setup" in the context of growth/momentum trading, or when they share a stock chart and want pattern analysis.