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Found 2,405 Skills
Fetch web page content via Chrome DevTools Protocol (CDP). Full JS rendering, handles redirects (including Google News). Use when you need to read the text content of a web page, scrape articles, or extract information from URLs. Zero dependencies — Python 3 stdlib only. Cross-platform (Mac, Windows, Linux).
Async Python bot for Polymarket that buys "No" on all standalone non-sports yes/no markets using the "nothing ever happens" strategy.
Comprehensive guide to the AgentMail Python and TypeScript SDKs. Use when building AI agents that need their own email inboxes, sending or receiving emails programmatically, managing threads and conversations, handling attachments, creating drafts for human-in-the-loop approval, setting up real-time notifications via webhooks or WebSockets, configuring custom domains, managing allow/block lists, using pods for multi-tenant isolation, or integrating email into any AI agent workflow. Covers the full AgentMail API with code examples, best practices, and production patterns.
CuTe Python DSL kernel workflow, CuteKernel runtime wrapper, suitability gate, tiling guidance, and CuTe-specific pitfalls. Use when: (1) planning or implementing a kernel in the CuTe Python DSL, (2) the optimization needs more explicit control than cuTile exposes but should remain in a Python-driven workflow, (3) defining package naming for cute-dsl kernels, (4) documenting CuTe Python DSL design choices, (5) recording language-specific knowledge for CuTe Python DSL.
Language-specific verification for Python, TypeScript/JavaScript, and Go. Checks type safety, language idioms, and best practices. Use when asked to "verify language", "check types", or for language-specific checks.
Write and execute Python scripts using the IDA Domain API for reverse engineering. Analyze binaries, extract functions, strings, cross-references, decompile code, work with IDA Pro databases (.i64/.idb). Use when user wants to analyze binaries, reverse engineer executables, or automate IDA Pro tasks.
Python backend testing patterns with pytest for FastAPI applications. Use when writing Python tests: unit tests for services and repositories, integration tests for API endpoints with httpx.AsyncClient, fixture creation, factory setup with factory_boy, async testing with pytest-asyncio, mocking strategies, and parametrized tests. Covers test organization (tests/unit, tests/integration), conftest hierarchy, and coverage requirements. Does NOT cover frontend tests (use react-testing-patterns) or E2E browser tests (use e2e-testing).
Academic backtesting framework for quantitative research. ~30 risk and performance ratios, 10 classes of indicators, event-driven engine with 6+ strategies, MPT optimizer, forward-looking simulation with Johnson SU + t-Copula, walk-forward CV, stress testing, fundamental analysis (Altman Z, Piotroski, DuPont). All flat Python + numpy.
Manage DNS records on DNSPod (Tencent Cloud's DNS service). Use when the user asks to add / update / delete A / AAAA / CNAME / MX / TXT / NS / SRV / CAA records, list records for a domain, search records, add ACME challenge / SPF / DKIM / domain-verification TXT records. Backed by the official tencentcloud-sdk-python DNSPod client.
Use when one Python service must send each agent's, tenant's, team's, or request's spans to its correct Arize space and project using application metadata. Covers dynamic OpenTelemetry routing for custom agent builders and multi-tenant applications, including register_with_routing, set_routing_context, multi-space tracing, and custom span routing.
Use when the user reaches for a Code node, mentions writing JavaScript or Python in n8n, or any custom logic comes up in workflow design. Triggers on "Code node", "Code", "JavaScript", "Python", "custom logic", "transform data", "$input", "$json transformation", "loop in code", "write a function", or any time the obvious answer seems to be "just put it in code."
Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.