Data & Analytics

Segelflug- und Thermikvorhersage mit Thermik-Score (0-10). Nutze diesen Skill wenn der User nach Segelflugwetter, Thermik, Streckenflugbedingungen, Flugwetter fuer Segelflieger oder Gleitschirmflieger fragt - auch indirekt wie \"lohnt sich Samstag fliegen?\", \"wie wird die Thermik?\", \"Segelflugwetter Wochenende?\" oder \"kann ich am Sonntag einen Streckenflug machen?\" oder \"Wettercheck Werdenfels\". Der Skill ruft Open-Meteo (ICON-D2) ab und liefert eine Profi-Einschaetzung mit Tagesabla...

This skill should be used when the user asks to \"update old content\", \"refresh content\", \"content is outdated\", \"improve declining rankings\", \"revive old blog posts\", \"this post is outdated\", \"traffic is declining on this page\", or \"rankings dropped for this article\". Identifies and updates outdated content to restore and improve search rankings. Analyzes content freshness, adds new information, updates statistics, and optimizes for current SEO and GEO best practices. For writ...

时寒冰式多维投资分析模型 - 从宏观、稀缺性、公司质量、时机四个维度分析股票、商品、ETF等投资标的

Alpha Pulse

Kimi (Moonshot AI) agent tool-use policy ported to OpenClaw. Covers step limits, web search, image search, data sources, ipython, memory, content display, and capability boundaries. All Kimi-specific tools are mapped to available OpenClaw skills.

Data Analyst

A specialized tool for sending quantitative trading signals to the FMZ platform via HTTP API.

Personal AI agent visual diary. Auto-records all agent activity, generates daily narrative summaries, visual timeline replay, and AI first-person journal. Use /diary for today's summary, /diary:thoughts for AI personal journal, /diary:replay for visual timeline, /diary:stats for analytics, /diary:persona to view/edit AI personality.

Implements and audits FluentCart customer identity, WP_User linkage, addresses, ownership checks, account creation, customer-scoped queries, and custom portal endpoints. Use when working with Customer, CustomerResource, getCurrentCustomer(), fct_customers, customer-profile REST routes, fluent_cart_api() customer-dashboard endpoint registration, customer merges or email changes, portal menus, order/subscription ownership, or long-running tests that switch users.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Design, build, and manipulate xarray data structures for highly linked, multi-dimensional data where measurements, derived estimates, and model outputs share coordinates and must stay synchronized. Use when: structuring laboratory/experimental/multi-assay data in xarray (Dataset or DataTree); designing coordinate systems for linked data; writing custom xarray indexes (custom Index subclasses, CoordinateTransforms, interval-based or N-D derived coordinate indexes); selecting/aligning/computing...

Build an awesome marimo notebook that brings a research paper or dataset to life — for competitions, tutorials, demos, or blog companions. Use when asked to "make an awesome marimo notebook," "build a notebook for this paper," "create a competition submission," or pair-program an interactive research explainer on molab.

Log a LeetCode problem into the user's Obsidian interview-prep tracker (daily notes + Dataview dashboard). Trigger whenever the user pastes a leetcode.com problem URL — even with no other words — or says anything like "log this", "log this lc problem", "just did/solved a problem", "add two sum to my log", "did 239 today", or asks to backfill a solve for a past day ("log this for yesterday"). Also trigger for logging other prep categories into the same tracker (ML fundamentals reviewed, ML cod...

Log interview-prep work into the user's PrepDojo-enabled Obsidian vault (daily notes read by a Dataview dashboard). Trigger whenever the user pastes a leetcode.com problem URL — even with no other words — or says anything like "log this", "log this lc problem", "just did/solved a problem", "add two sum to my log", "did 239 today", or asks to backfill a solve for a past day ("log this for yesterday"). Also trigger for logging the other prep categories PrepDojo tracks — ML fundamentals reviewed...

Log and update job applications in the user's PrepDojo tracker (CSV files inside their Obsidian vault, read live by a Dataview dashboard). Trigger when the user says things like "log this application", "I applied to X", "add this to my tracker", or reports a stage change — "X moved me to phone screen", "got rejected by Y", "offer from Z", "recruiter reached out". Also trigger for follow-up bookkeeping ("remind me to follow up with X on Friday" → set the row's follow-up date). Do NOT trigger f...

Plan, audit, run, and explain reproducible statistical analyses for scientific research from a manuscript, study design, and real data or machine-readable results. Use for method selection, research-data QA, biomedical or quantitative models, publication figures/tables, result review, or figure/table reading guidance. Do not use for generic business charts, scientific illustrations, or manuscript writing without a statistical task.

Design system Soneta / enova365 do budowania aplikacji webowych spójnych wizualnie z enova365 ERP: palety kolorów (jasny/ciemny), typografia, layout, komponenty UI (przyciski, formularze, tabele, sidebar, header, kafelki, modale), odstępy, cienie, ikony SVG, animacje, responsywność. Używaj gdy użytkownik: (1) prosi o zaprojektowanie, zbudowanie lub ostylowanie strony/aplikacji w stylu Soneta/enova365; (2) wspomina 'styl enova', 'styl Soneta', 'design system enova365', 'UI enova'; (3) chce das...

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.

Track physical units and propagate measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibil...

Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.

Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pipelines, tokenizers, or TrainingArguments—not for general ML outside the Transformers library.

PyTorch Geometric (PyG) for graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torch_geometric, not for general NetworkX analytics or non-graph PyTorch models.

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.