Feather

# Agent Score Fix Report — Feather
URL: https://docs.featherhq.com/
Score: 98/100 (Grade A)

I need help improving the AI-readiness of the documentation at https://docs.featherhq.com/.
Agent Score found 1 failing checks and 2 warnings.

## Failing Checks (1)
- [page-size] Page Size Html: 1 of 10 sampled pages convert to over 100K chars (max 1513K, 0% boilerplate)

## Warnings (2)
- [content-discoverability] Llms Txt Valid: llms.txt contains parseable links but doesn't fully follow the proposed structure: https://docs.featherhq.com/llms.txt: No blockquote summary found
- [content-discoverability] Llms Txt Directive: llms.txt directive found in 9 of 10 sampled pages (1 missing)

## Fix Instructions

For each issue above, please:
1. Analyze the documentation site at https://docs.featherhq.com/
2. Implement the specific fix
3. Verify the fix would cause the check to pass

### Common fixes:
- **No llms.txt**: Create /llms.txt following https://llmstxt.org — list all doc pages in markdown format
- **No .md URL support**: Configure your docs platform to serve pages at equivalent .md URLs (e.g. /docs/quickstart.md)
- **No content negotiation**: Return markdown when request includes Accept: text/markdown header
- **Large page size**: Reduce nav boilerplate, inline scripts, and repetitive markup
- **No sitemap**: Generate /sitemap.xml listing all documentation URLs
- **Auth walls**: Ensure docs pages return 200 without requiring login cookies or tokens
- **No Last-Modified header**: Configure your server/CDN to include Last-Modified response headers
- **Tab content hidden**: Ensure tabbed content is rendered in the HTML (not JS-only) so agents can read all variants

## Run afdocs Locally for More Detail

To get deeper visibility into what's failing, run afdocs against your docs:

  npx afdocs check https://docs.featherhq.com/ --fixes --verbose

- **--fixes**: Adds "Fix:" lines to the output for each warn/fail check with actionable remediation steps
- **-v, --verbose**: Shows per-page details (specific URLs, character counts, error codes) for checks with issues — useful for per-URL visibility into what's failing