Data analysis, machine learning, LLM apps, and prompt design (9.2K skills)
Automatically translate and sync App Store metadata (description, keywords, what's new, subtitle) to multiple languages using LLM translation and asc CLI. Use when asked to localize an app's App Store listing, translate app descriptions, or add new languages to App Store Connect.
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Route loader option, loaderDeps for cache keys, staleTime/gcTime/ defaultPreloadStaleTime SWR caching, pendingComponent/pendingMs/ pendingMinMs, errorComponent/onError/onCatch, beforeLoad, router context and createRootRouteWithContext DI pattern, router.invalidate, Await component, deferred data loading with unawaited promises.
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Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.
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Iterative Python via live Jupyter kernel (hamelnb).
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Use at the end of every important response. Save your decision-making thoughts as a signed A2A insight so they become training data for future fine-tuning.
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Use for local LLM fallback, failover, and Ollama health.
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W&B: log ML experiments, sweeps, model registry, dashboards.
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vLLM: high-throughput LLM serving, OpenAI API, quantization.
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HuggingFace hf CLI: search/download/upload models, datasets.
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Songwriting craft and Suno AI music prompts.
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Karpathy's LLM Wiki: build/query interlinked markdown KB.
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Plan and prepare an OpenAmer-specific fine-tune from the collected brain data: data curation, ChatML JSONL, training, evaluation. 'The organism becomes its own brain'.
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Neuromorphic Computing - Fraunhofer Institute for … — Neuromorphic computing is a field of technology aimed at designing computer architectures that m
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OpenAI(api_key='your-api-key') model = OpenAIEmbeddingsModel(model='text-embedding-3-small', openai_
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The addition was a prompt injection, a form of AI attack that exploits an LLM s inability to distinguish between legitimate user prompts and those fro
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Microsoft AI, Cloud, Productivity, Computing, Gaming & Apps Explore Microsoft products and servi
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As a heavy Claude Code user, Simon estimates his own usage at the equivalent of 4,400 "typical queries" to an LLM, for an equivalent of around $15-$20
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AI agent system prompts cannot serve as the sole enforcement layer — guardrails must be implemented at the API gateway and token-permission level, not
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Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"
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Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"
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End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.
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Entfernt KI-Spuren, Chat-Reste, Platzhalter und LLM-Stilmuster aus fertigen Texten und prüft die AI-Disclosure auf Vollständigkeit und korrekten Sitz.
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Split documents into overlapping token chunks for RAG pipelines and LLM context windows. Zero dependencies.
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Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.
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