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Found 2,166 Skills
Use the built-in web_search function to perform web searches and return summary results. Prepare a clear and specific `query`. Run the script `python scripts/web_search.py "query"`. Organize the answer based on the returned summary list without adding or fabricating content.
Fetch financial and market data using the yfinance Python library. Use this skill whenever the user asks for stock prices, historical data, financial statements, options chains, dividends, earnings, analyst recommendations, or any market data. Triggers include: any mention of stock price, ticker symbol (AAPL, MSFT, TSLA, etc.), "get me the financials", "show earnings", "what's the price of", "download stock data", "options chain", "dividend history", "balance sheet", "income statement", "cash flow", "analyst targets", "institutional holders", "compare stocks", "screen for stocks", or any request involving Yahoo Finance data. Always use this skill even if the user only provides a ticker — infer intent from context.
Parse and analyze Linux auditd logs to detect intrusion indicators including unauthorized file access, privilege escalation, syscall anomalies, and suspicious process execution using ausearch and Python.
This skill should be used when the user asks to "create a new FastAPI project", "setup a fastapi api", "new fastapi project", "scaffold a fastapi app", "initialize a fastapi backend", or "start a new python api". Scaffolds a complete production-ready FastAPI project with SQLAlchemy, PostgreSQL, JWT auth, Pydantic v2 settings, and uv package management.
[Hyper] Run deploy-readiness validation and fix reproduced lint/typecheck/build blockers for Node.js, Rust, and Python repos. Use for pre-deploy checks, deploy-ready requests, or final quality/build gates before deployment.
Fix markdown table alignment and spacing issues. Use when formatting tables in markdown files, aligning columns, normalizing cell padding, or ensuring proper GFM table structure. Runs a Python script to normalize column widths while preserving alignment markers.
Solve quantitative problems in biophysics, pharmacokinetics, epidemiology, toxicology, population genetics, and statistical mechanics. Provides reasoning strategies and Python templates for calculations alongside ToolUniverse data lookups. Use when users ask about drug dosing, half-life decay, radioactive tracers, R0, herd immunity, diffusion, Hardy-Weinberg, binding equilibria, or any computation-heavy biology/chemistry question.
Use when adding, retiring, or auditing feature flags. Triggers on "add a flag", "ship behind a flag", "rollout plan", "kill switch", "stale flags", "flag debt", "LaunchDarkly", "GrowthBook", "Statsig", "Unleash", "Flipt", or any progressive-delivery question. Ships flag debt scanner, rollout planner, and kill-switch auditor (all stdlib Python), 4 references on flag taxonomy + provider trade-offs + rollout strategies + lifecycle, plus a /flag-cleanup slash command.
FastAPI OpenTelemetry style: native FastAPIInstrumentor, centralized observability init, Python decorators, OTLP logs, and LLM cost metrics.
Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. Use when the user says "generate Python experiment", "write an SDK experiment", "create a ddtrace experiment", "Python notebook experiment", "use the LLM Obs SDK", or has `ddtrace` installed and wants idiomatic SDK code.
Iterative Python via live Jupyter kernel (hamelnb).
Guide for using ruff, the extremely fast Python linter and formatter. Use this when linting, formatting, or fixing Python code to maintain code quality and consistency.