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Composite walkthrough, SaaS, 8 weeks

An open-source dev tool gets cited in 6 of 10 LLM answers in 60 days

GitHub repo with weak landing page. Fixed docs site, llms.txt, BLUF answers across docs. Strong GitHub-stars → strong brand-entity.

Composite
Before51After87

Readiness, out of 100: up 36 points in 8 weeks.

Composite walkthrough: patterns aggregated from real implementations. No specific named brand. Numbers are typical, not promised.

The site

Open-source dev tool, ~4K GitHub stars, used by a few thousand devs, but invisible to ChatGPT + Claude for the canonical 'how do I X with [tool]' queries. Maintained by a 2-person team plus contributors.

What moved, signal by signal

In priority order. Signals in the 47-signal rubric link to their documentation.

  1. llms-txt-presence

    Fail to Pass

    llms.txt presence

    Published /llms.txt + /llms-full.txt per the llmstxt.org spec. Linked to docs sitemap. Validated via VectorCite tool.

  2. bluf-answer

    Weak to Pass

    BLUF (Bottom Line Up Front)

    Rewrote the top 30 docs pages to lead with a 2-sentence direct answer before the conceptual context. Moved 'why we built this' below the fold.

  3. structural-depth

    Weak to Pass

    Structural depth (GEO-SFE)

    Restructured docs from 'Getting started → API reference' to H2-segmented sub-question landing pages: 'How to install', 'How to configure auth', 'How to handle errors', etc.

  4. faq-schema

    Fail to Pass

    FAQ schema

    Added FAQPage schema to every docs page with the top 5 most-asked questions on Discord / GitHub Issues.

  5. brand-entity

    Weak to Pass

    Brand entity in Wikidata

    4K GitHub stars + Wikipedia + Crunchbase already qualified the brand for Wikidata. Submitted Wikidata entry referencing the GitHub repo as primary external ID. Approved in 6 days.

  6. outbound-authority-links

    Weak to Pass

    Outbound authority links

    Added citations to relevant RFCs, official docs (Node.js, Python, MDN), and academic papers in the conceptual sections.

  7. case-study-evidence

    Fail to Pass

    Case study evidence

    Published 6 'How [company] uses [tool]' showcase pages, each with concrete code snippets + scale numbers from real users.

How it went

Week 1–2 was the diagnostic: scored 51. The shocking finding was that the existing docs site at /docs was a clean SSG built with VitePress but had zero LLM-discoverability features. Week 3 deployed /llms.txt + restructured top 30 pages with BLUF. Week 4–5 was the FAQPage + structural-depth pass. Week 6 was Wikidata submission (approved in 6 days). Week 7–8 was the case-study showcases. The key insight: an open-source tool with 4K stars has more brand-entity authority than most SaaS at 100K MRR. The OSS metric (stars + contributors + downloads) is actually a stronger Wikidata-citation signal than press mentions. By day 60, the cite rate on 'how do I X with [tool]' queries was 6 of 10 (vs <1 of 10 baseline) across ChatGPT, Claude, Perplexity, and AI Overviews.

Takeaways

  1. Open-source tools have an unfair advantage on brand-entity signals: GitHub stars + npm/PyPI downloads + contributor lists are all Wikidata-citation-grade external IDs. Use them.
  2. Documentation-first sites need to invert their structure for AEO: top pages should answer 'how do I X' not 'what is X'. The conceptual content goes below the fold.
  3. llms.txt + llms-full.txt deserves to be treated as a publishing standard for open-source projects: it's how you tell AI engines what your canonical docs are vs the noisy GitHub README.

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