Generate llms.txt indexes from markdown and MDX documentation files.
npx skills add https://github.com/hidonguyen/trade-ops --skill ck-llms-hidonguyen
Please help me install this Agent Skill. Skill: ck:llms Source: https://github.com/hidonguyen/trade-ops/tree/main/.opencode/skills/llms Command: npx skills add https://github.com/hidonguyen/trade-ops --skill ck-llms-hidonguyen
Large documentation sites and codebase docs are hard for LLMs to consume because context windows are limited and raw sitemaps contain too much noise. This Skill produces concise, curated, LLM-friendly indexes that surface the most relevant pages and short summaries so AI assistants and search agents get the right context quickly.
Run the llms generator against your docs folder to produce llms.txt and an optional llms-full.txt in the target output directory.
High-intent search queries and answers about installing and using this skill.
To generate an llms.txt file, scan your local documentation directory or remote doc site to extract H1 titles and descriptions from markdown and MDX files, categorize the content, and assemble a curated link list validated against llmstxt.org rules.
The llms.txt format provides a concise, curated index of documentation links with short summaries, helping LLMs consume large documentation sites quickly by surfacing relevant pages without the noise of raw sitemaps.
Yes, you can generate an optional llms-full.txt file that includes expanded inline document content alongside the standard llms.txt, providing context-heavy summaries for offline or automated AI assistants.
Yes, the generator scans both local documentation directories and remote documentation sites, extracting titles and first paragraphs from markdown and MDX files to build web-friendly URLs for the index.
When building an llms.txt index, the generator scans markdown and MDX files, extracts H1 titles and first paragraphs, and categorizes the content into structured sections to create a curated, LLM-friendly site summary.
Publish an llms.txt file at your site root to improve AI discoverability, providing succinct context and curated link lists that help chatbots and automated helpers navigate your documentation efficiently.
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