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Found 1,944 Skills
Buffett-style single-stock moat diagnostic — "Would Buffett buy this stock?" Five dimensions: business & moat / financial health / management & capital allocation / valuation & margin of safety / long-term visibility. Data from Longbridge CLI first, MCP fallback, WebSearch only for gaps. Runs cross-statement reconciliation (勾稽校验) BEFORE scoring; data-source appendix closes with a one-line reconciliation summary. Output: star-rated radar card, dimension detail, Buffett-voice narrative, mandatory holding-period education block. Triggers: "巴菲特", "护城河", "巴菲特会买吗", "价值投资", "好生意", "宽护城河", "定价权", "诊股", "巴菲特诊股", "巴菲特视角", "长期持有", "護城河", "巴菲特會買嗎", "價值投資", "寬護城河", "定價權", "診股", "巴菲特診股", "巴菲特視角", "長期持有", "Buffett", "Warren Buffett", "moat", "economic moat", "wide moat", "pricing power", "value investing", "owner earnings", "would Buffett buy", "Berkshire-style", "quality compounder".
On-chain data analysis framework — covers active addresses, whale behaviour, TVL (total value locked), DEX liquidity, and on-chain valuation metrics: MVRV (market cap / realised value), NVT (network value / transaction volume), SOPR. Longbridge provides spot crypto quotes (.HAS); raw on-chain data requires external sources (Glassnode / Dune Analytics). Triggers: "链上数据", "链上分析", "MVRV", "NVT", "活跃地址", "鲸鱼地址", "TVL", "SOPR", "链上指标", "链上估值", "鏈上數據", "鏈上分析", "活躍地址", "鯨魚地址", "鏈上指標", "鏈上估值", "on-chain data", "on-chain analysis", "MVRV ratio", "NVT ratio", "active addresses", "whale activity", "TVL", "SOPR", "on-chain valuation", "DeFi TVL", "crypto on-chain".
Deep formal test smell audit based on academic research taxonomy (testsmells.org). Detects 19 categorized smell types — conditional logic, mystery guests, sensitive equality, eager tests, and more — with calibrated severity and research-backed remediation. Use for comprehensive test suite health assessments. For a quick pragmatic review, use test-anti-patterns instead. DO NOT USE FOR: writing new tests (use writing-mstest-tests), evaluating assertion quality specifically (use assertion-quality), or finding test duplication and boilerplate (use exp-test-maintainability).
AI-powered stock and crypto analysis using the aipa CLI. Use this skill whenever the user asks to analyze a ticker, compare stocks, get technical analysis, or answer any financial market question about Vietnamese stocks (VIC, VCB, FPT...), cryptocurrencies (BTC, ETH...), or global assets. Also use for price action analysis, moving average analysis, support/resistance questions, sector comparison, Wyckoff analysis, or trading insights. Also handles fundamental analysis when the user explicitly asks for fundamentals, PE, ROE, NPL, CAR, valuation, or "phân tích cơ bản" — use `aipa fundamentals` commands to enrich technical analysis with financial ratios, company info, and fundamental screening/ranking. For raw price data without AI, use the aipa-data skill instead.
Create structured technology trade-off analysis documents with scored comparison matrices. Use this skill whenever the user wants to compare technologies, evaluate architectural options, analyze build-vs-buy decisions, assess migration strategies, or produce any decision document that compares multiple approaches across weighted dimensions. Triggers on: 'trade-off analysis', 'tradeoff', 'comparison matrix', 'evaluate options', 'which technology should we use', 'compare approaches', 'pros and cons of', 'build vs buy', 'migration analysis', 'consolidation analysis', 'technology selection'. Also use when the user has completed technical research and wants to structure findings into a decision document.
Activate when reviewing or modifying dependency resolution, lockfile schema, package downloaders, signature/integrity checks, file integration cleanup, or anything that could expose APM to dependency confusion, typosquatting, malicious packages, or token leakage.
Run the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models: baseline evaluate, RCA, ingestion of customer-supplied pre-generated AnomalyGen images, k-NN mining, retraining, and deployment gating until FAR / recall KPI targets are met. EA variant — does not run AnomalyGen inline; the customer pre-generates synthetic NG/OK pairs out-of-band and the loop ingests them. Use for prompts like "run the DEFT loop", "fine-tune until FAR below 0.1% at recall=100%", or "improve my AOI ChangeNet model with RCA and pre-generated synthetic defects"; do not use for standalone TAO training, one-off inference, generic anomaly generation, or RCA-only analysis.
PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via a pillar-based representation, then applies 2D detection — used in autonomous driving and robotics. Use when training, evaluating, exporting, pruning, retraining, or running inference for a TAO PointPillars model. Trigger phrases include "train PointPillars", "LiDAR 3D detection", "point-cloud object detection", "pillar-based 3D detector".
Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D reconstruction. Use when training, evaluating, exporting, or running inference for a TAO FoundationStereo model. Trigger phrases include "train stereo depth", "FoundationStereo", "stereo disparity estimation", "3D reconstruction from stereo".
Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize, evaluate, or compose these into a pipeline. Do NOT use for frontend/dashboard/visualization work, generic ML advice, billing/access, or non-Nemotron coding tasks.
PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.) with distillation and quantization for deployment. Use when training, evaluating, distilling, quantizing, exporting, or running inference for a TAO image-classification (PyT) model. Trigger phrases include "train image classifier", "TAO classification", "ResNet/EfficientNet/FAN backbone classifier", "classification-pyt".
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.