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AI / LLM Integration
Machine-readable docs for AI coding assistants.
llms.txt
Compact link index. Every doc page in one file. Updated at build.
llms-full.txt
Full corpus with per-page summaries. Single-fetch context.
context7
Live-indexed manifest. Use with the @context7 MCP server.
Both `.txt` endpoints regenerate on every docs build. The corpus tracks the published site.
Endpoints
| Endpoint | Purpose |
|---|---|
| /llms.txt | Indexed link list of every doc page. Drop into any LLM context. |
| /llms-full.txt | Same index plus embedded per-page summaries. Full corpus in a single fetch. |
| context7.com/websites/reelkit_dev | Live-indexed Context7 manifest. Plug in via the @context7 MCP server. |
Quick start
Agent prompt
Paste an endpoint URL into the agent to ground answers in current docs.
text
Context7 MCP server
Install the @context7 MCP server. The agent picks up the ReelKit manifest automatically and fetches docs on demand. Mention reelkit in your prompt.
Direct ingestion
Fetch programmatically:
bash
What gets indexed
- Getting started + Installation
- Core engine guide + API
- React, Vue, Angular bindings (guide + API + reel-player + lightbox + stories-player)
- Stories core engine
- SSR notes
- Troubleshooting
- Changelog
Why?
AI assistants lag behind library changes. Generated code references stale APIs. These endpoints update with every doc release, so suggestions match current behavior instead of last quarter's.