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Found 1,909 Skills
CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF object pose estimation. Use when training, evaluating, exporting, or running inference for a TAO CenterPose model. Trigger phrases include "train CenterPose", "6-DoF object pose", "keypoint estimation", "object pose regression".
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.
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.
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.
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.
Apply governance theory to analyze multi-level, network, and collaborative governance arrangements beyond traditional government. Use this skill when the user needs to evaluate public-private partnerships, analyze multi-stakeholder governance structures, compare governance models across sectors, or assess institutional arrangements for collective decision-making — even if they say 'who governs this', 'public-private collaboration', or 'how are decisions made across organizations'.
An automated data exploration and visualization tool that provides a complete EDA solution from data loading to professional report generation. It supports multiple chart types, intelligent data diagnosis, modeling evaluation, and HTML report generation. Suitable for data analysis projects in fields such as healthcare, finance, e-commerce, etc.
Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs them to learn visual representations; supports pretrain and finetune stages. Use when training, evaluating, exporting, or running inference for a TAO MAE backbone. Trigger phrases include "pretrain MAE", "self-supervised vision pretraining", "Masked Autoencoder", "Mask Auto-Encoder", "MAE fine-tune".