Fetch available vehicle years, makes, models, trims, or variants for cascading dropdowns using the CarsXE YMM Options API. It presents the matching options and can continue to the next selection level; full vehicle specifications are handled separately.
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List year, make, model, trim, or variant options for cascading vehicle dropdowns using the CarsXE YMM Options API. Use this when the user wants available years, makes, models, trims, or which years a vehicle was sold — not full specs.
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When the user wants to browse available years, makes, models, trims, or variants (dropdowns, "what years was X sold", "which models does Toyota have in 2023"):
GET https://api.carsxe.com/v1/ymm-options?key={CARSXE_API_KEY}&source=codex_plugin[&dimension={DIMENSION}][&year={YEAR}][&make={MAKE}][&model={MODEL}][&trim={TRIM}]
dimension is optional: years | makes | models | trims | variants.
make is required for dimension=models. model is required for dimension=trims, and for dimension=variants unless both year and make are set. trim is a substring filter (ignored for years/makes/models).dimension is omitted, infer the next list: no filters → years; year → makes; make → models; make + model → variants.message if present. For bulk variants (dimension=variants + year + make, no model), mention modelCount — that is the billed unit count — and warn before making that call.year-make-model skill (/v1/ymm) for full specs of a chosen year/make/model.CARSXE_API_KEY environment variable (see AGENTS.md).还没有评价。欢迎分享你的使用感受。
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Plan worthwhile local tasks by checking dependencies, effects, resources, and platform boundaries, then execute the exact approved plan. It can use safe parallel work or suitable Apple-silicon backends when the project exposes them.
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Record deferred engineering decisions and temporary workarounds in a debt registry, with context such as effort, ongoing cost, affected area, and payoff trigger. Entries receive short labels so they can be reviewed or dropped later.
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Generate or compare AI agent code for LangChain, EINO, AutoGen, AgentScope, and CrewAI using framework-specific templates, API mappings, conventions, and project structures. Reference files include verification dates to help identify potentially outdated guidance.
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Investigate a reproducible bug, test failure, or unexpected page behavior, then make a narrowly scoped fix. The workflow calls for a regression test before changing code, verifies the fix, and reports remaining risks and evidence while avoiding unrelated refactoring or feature work.
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Build an AI agent project through six collaborative stages, from requirements and architecture to tools, prompts, code generation, and verification. Start framework-independent, then generate a runnable project for frameworks such as LangChain, EINO, AutoGen, AgentScope, or CrewAI.
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Audit a project or learning workflow for scope drift, conflicting plans, inconsistent names, and code quality concerns. The skill reads project files and code, then reports evidence, severity, and suggested next steps without changing files.
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