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Found 3,494 Skills
Sports news via RSS/Atom feeds and Google News. Fetch headlines, search by query, filter by date. Covers football news, transfer rumors, match reports, and any sport via Google News. Use when: user asks for recent news, headlines, transfer rumors, or articles about any sport. Good for "what's the latest on [team/player]" questions. Supports any Google News query and curated RSS feeds (BBC Sport, ESPN, The Athletic, Sky Sports). Don't use when: user asks for structured data like standings, scores, statistics, or xG — use football-data instead. Don't use for prediction market odds — use polymarket or kalshi. Don't use for F1 timing data — use fastf1. News results are text articles, not structured data.
USE FOR web search. Returns ranked results with snippets, URLs, thumbnails. Supports freshness filters, SafeSearch, Goggles for custom ranking, pagination. Primary search endpoint.
Track build processing, status, and retention for Google Play using gpd publish commands. Use when waiting on processing or managing releases.
Resolve Google Play identifiers (package, tracks, version codes, products, subscriptions) using gpd. Use when commands require IDs or exact values.
Orchestrate Google Play beta testing groups and distribution using gpd. Use when managing testers, internal testing, or beta rollouts.
Set region-specific pricing for Google Play subscriptions and products using gpd monetization commands. Use when adjusting prices by territory or PPP strategy.
Sync and validate Google Play metadata, listings, and assets with gpd, including Fastlane-style workflows. Use when updating store listings or translations.
Preflight Google Play releases, validate edits, and verify listing completeness with gpd. Use when shipping to production or troubleshooting a failed release.
Guidance for using the Google Play Developer CLI (flags, output formats, auth, pagination). Use when asked to run or design gpd commands for Play Console workflows.
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.