Generate deployable skills or tools from natural-language or JSON specs.
npx skills add https://github.com/isLinXu/under-one --skill shenji-bailian
Please help me install this Agent Skill. Skill: shenji-bailian Source: https://github.com/isLinXu/under-one/tree/main/underone/skills/shenji-bailian Command: npx skills add https://github.com/isLinXu/under-one --skill shenji-bailian
This Skill turns ambiguous natural-language requirements into a production-ready, testable skill or tool package, reducing the effort of manually drafting code, contracts, and verification assets.
Ask the AI to forge a JSON-validation CLI by providing your requirement in one sentence, then run the generated script with the provided sample input.
High-intent search queries and answers about installing and using this skill.
To generate a skill scaffold from natural-language requirements, you can input a one-sentence request or structured JSON spec. The system normalizes the input and produces deployable files including scripts, SKILL.md, and metadata for immediate use.
The best way to create runtime contracts and acceptance assets is using a spec-to-code generation process. This approach produces standalone smoke checks, failure-mode assets, and benchmark cases to validate and maintain generated CLI tools over time.
Yes, you can automatically generate multi-file asset packages. By inferring the correct artifact type from your input, the system delivers complete packages containing SKILL.md entrypoints, _skillhub_meta.json metadata, and specialized scripts for testing and acceptance.
Yes, this natural-language spec-to-code tool supports specialization-specific skeleton generation. It detects whether you need a tool or a skill and generates scaffolds and assets tailored for specific engineering scenarios like analysis, browser, workflow, or CLI tasks.
You produce testing-ready outputs by providing your requirements to the skill-scaffolding generator. It outputs standalone smoke validation scripts alongside failure-mode and benchmark-oriented assets, ensuring regression confidence for your generated tools.
You should use a tool factory approach when you need to turn ambiguous natural-language requirements into a production-ready package. It reduces the effort of manually drafting code, defining contracts, and creating verification assets by automating the entire delivery process.
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