Using this documentation with AI assistants
This site follows the llms.txt convention: everything that renders here is also published as plain markdown, so a language model can read the specification, the decision log and the proof notes without scraping HTML. Point your assistant at the URLs below — nothing else is needed.
Endpoints
| URL | What it is | Use when |
|---|---|---|
/llms.txt | Index: one line per page with its title and a short description | The model should decide what to fetch (small, a few KB) |
/llms-full.txt | Every page concatenated into one markdown file | You want the whole corpus in context at once (roughly 800 KB, about 200k tokens) |
/assets/md/<route>.md | The markdown of a single page | You want one page verbatim, e.g. /assets/md/spec/erc-draft.md |
Every route on this site has a markdown twin: take the page path, prefix it
with /assets/md, and add .md. The landing page is /assets/md/index.md.
Recipes
Chat assistants (Claude, ChatGPT, Gemini …) — paste the llms-full.txt
URL, or the URL of a single page's markdown, into the conversation and ask
your question. For a focused task the ERC draft plus the technical spec is
usually enough:
https://skanislav.github.io/pq-sap/assets/md/spec/erc-draft.md
https://skanislav.github.io/pq-sap/assets/md/spec/technical-spec.mdCoding agents (Claude Code, Cursor, Copilot, Codex …) — give the agent the index and let it fetch what it needs:
Read https://skanislav.github.io/pq-sap/llms.txt and then the pages you need
to implement scheme 2 (ML-KEM-768 + blinded ML-DSA-65) in <language>.
Check your output against the v0 test vectors.Cursor users can register https://skanislav.github.io/pq-sap/llms-full.txt
as a custom docs source (Settings → Features → Docs).
Shell — fetch the corpus directly:
curl -s https://skanislav.github.io/pq-sap/llms.txt
curl -s https://skanislav.github.io/pq-sap/llms-full.txt > pq-stealth-docs.md
curl -s https://skanislav.github.io/pq-sap/assets/md/spec/decisions.mdWhat to read for which task
| Task | Pages |
|---|---|
| Implement the scheme | ERC draft, Technical spec |
| Understand a design choice | Decisions & findings |
| Verify an implementation | Python executable spec and the v0 test vectors (JSON, with negative cases) |
| Reason about security | Lean 4 / VCVio proofs and the per-module proof browser |
| See what is still open | ERC submission gap analysis |
Ground truth
Most pages here are mirrored from markdown that lives next to the code in the
GitHub repository (docs/, lean/docs/,
the per-implementation README.md files), and each mirrored page names its
source in a banner at the top. If an assistant needs the latest state, or the
test vectors and code themselves, the repository is the source of truth; the
site is rebuilt from main on every push.
Things the markdown cannot convey: the Lean proofs are only as good as their axioms (listed on the proofs overview), and the specification is a working draft that has not been through ERC review yet. Ask the assistant to say which page a claim comes from so you can check it.