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For AI agents

This site is published for people and for AI agents. Its source is an Open Knowledge Format (OKF) v0.2 knowledge bundle, and the deployed site exposes that structure directly. If you are an agent (or you are wiring one up), consume the content through these endpoints rather than scraping rendered HTML.

Entry points

Endpoint What you get
https://unm-carc.github.io/dust-2026/llms.txt Linked outline of every page with one-line descriptions (llms.txt convention); every entry lists the HTML page, its Markdown twin, and its raw GitHub source
https://unm-carc.github.io/dust-2026/llms-full.txt The entire corpus in one file: every page's Markdown with frontmatter, prefixed by its canonical URL, links made absolute (about 330 KB)
Any page URL + index.md That page's Markdown source with full OKF frontmatter, served as text/markdown (for example https://unm-carc.github.io/dust-2026/lessons/01-open-science/index.md); section listings too (https://unm-carc.github.io/dust-2026/lessons/index.md). Every rendered page links it from a "View this page as Markdown" button and from a "Machine-readable versions" line at the end of the article
Raw source on GitHub https://raw.githubusercontent.com/UNM-CARC/dust-2026/main/docs/<path>.md, where <path> is the site path without the trailing slash (for example https://raw.githubusercontent.com/UNM-CARC/dust-2026/main/docs/lessons/01-open-science.md). Same content as the Markdown twin; reachable from sandboxes that allow github.com but not *.github.io
sitemap.xml, robots.txt Standard crawl surface; robots.txt repeats all of these pointers
Source repository The bundle itself (docs/ mirrors the site paths one to one), plus AGENTS.md with contribution rules for coding agents
Lesson page <head> A schema.org LearningResource JSON-LD record (objectives, duration, delivery format, accessibility profile) and okf:lesson-* meta tags, generated from the page's lesson: frontmatter block

Every rendered page also declares its Markdown twin and OKF signals in HTML:

<link rel="alternate" type="text/markdown" href="index.md">
<meta name="okf:type" content="Lesson">
<meta name="okf:status" content="stable">
<meta name="okf:trust-tier" content="unverified">
<meta name="okf:generated-at" content="2026-09-11T00:00:00Z">
<meta name="okf:lesson-format" content="in-person">
<meta name="okf:lesson-duration-minutes" content="50">
<script type="application/ld+json">{"@context": "https://schema.org", "@type": "LearningResource", ...}</script>

The head tags are invisible to most fetch tools

The <link rel="alternate">, okf:* meta tags, and JSON-LD live in <head>, which text-extracting fetchers discard, and a link-derived URL allowlist never sees them. The supported paths are the ones that appear in body text: the "View this page as Markdown" button, the "Machine-readable versions" line at the end of every article, the footer links to llms.txt, and the addresses listed in llms.txt itself. All of them are absolute.

If you cannot fetch this site

Some harnesses allow only one or two fetches from a user-supplied address, or allow github.com and raw.githubusercontent.com but not *.github.io. In that case:

  1. Use the raw source. docs/ in the repository mirrors the site paths one to one on branch main:

    Site page        https://unm-carc.github.io/dust-2026/<path>/
Markdown twin    https://unm-carc.github.io/dust-2026/<path>/index.md
Raw source       https://raw.githubusercontent.com/UNM-CARC/dust-2026/main/docs/<path>.md

Content page     /lessons/01-open-science-self-paced/  ->  https://raw.githubusercontent.com/UNM-CARC/dust-2026/main/docs/lessons/01-open-science-self-paced.md
Section listing  /lessons/                             ->  https://raw.githubusercontent.com/UNM-CARC/dust-2026/main/docs/lessons/index.md
Whole corpus     https://raw.githubusercontent.com/UNM-CARC/dust-2026/main/docs/llms-full.txt

    main moves; to cite a fixed version use https://github.com/UNM-CARC/dust-2026/blob/<commit>/docs/<path>.md, taking the commit from the repository's history.

  2. Prefer one fetch over six. llms-full.txt holds every page (about 330 KB); if you can make a single request, make that one.

  3. Avoid the GitHub tree API unless authenticated: api.github.com rate-limits anonymous calls per shared IP. Raw file paths do not.

  4. If you reached only the landing page, its footer links llms.txt, llms-full.txt, and this guide, and its "Browse the site" list links the section listings; all are absolute addresses that appear in extracted text.

Reading the OKF frontmatter

Each content page's YAML frontmatter answers the questions an agent should ask before relying on it:

  • What is this? type (Lesson, Guide, or Reference), title, description, tags.
  • Where did it come from? generated: { by, at } and sources. The three lessons were rewritten in September 2026 from the DUST 2025 lessons (CC BY 4.0) and draw on the UNM CARC edition of Foundational Open Science Skills and the GPT 101 workshop; sources[].resource points at the exact upstream file and commit.
  • How much should I trust it? The verified key (OKF §5.3): absent means unverified; by: "human:<id>" means human-reviewed by the author. Prefer human-reviewed pages when answers conflict.
  • Is it still true? status (stable by default; draft needs review; deprecated is kept for history) and stale_after. The lessons carry a stale_after date because the policy, pricing, and AI-landscape facts they cite change quickly; a page past that date describes the state of affairs as of its generated.at, not today.

Dated facts

Funder policies, article-processing charges, AI regulations, and energy figures in these lessons are stated as of September 2026 with sources. When answering a question about current rules, say so and point the user to the linked primary source rather than asserting the figure is current.

These sites share the same agent conventions:

  • CARC Documentation: UNM HPC clusters, Slurm, storage, software.
  • Foundational Open Science Skills: open science, data management, version control, containers, HPC.
  • GPT 101: generative-AI platforms, prompt engineering, agents, ethics, and law. Its raw-source convention differs: replace a page URL's trailing / with .md.

Teaching a lesson

The lessons are written to be delivered by an agent as well as read. Each lesson is a pair: a 50-minute in-person lecture (for example lessons/01-open-science/) and its self-paced homework (for example lessons/01-open-science-self-paced/), split into twelve numbered modules that each end in a checkpoint question. The lecture pages are text-only; the self-paced pages carry the figures, each with a text description. Every lesson page carries a lesson: frontmatter block:

lesson:
  number: 1
  format: in-person            # or self-paced
  duration_minutes: 50
  companion: 01-open-science-self-paced.md
  delivery_modes: [lecture, tutor, interactive]
  objectives: [...]            # what the learner should be able to do afterwards
  key_terms: [...]             # glossary and flashcard source
  accessibility:
    language: en
    access_mode: [textual, visual]
    access_mode_sufficient: [textual]
    features: [alternativeText, longDescription, readingOrder, structuralNavigation, tableOfContents]
    hazards: [none]
    media: "No audio or video. Every figure has alt text and a text description."

When a user asks you to teach, tutor, or quiz them on a lesson:

  1. Fetch the Markdown twin (page URL + index.md), not the rendered HTML, and read the lesson: block first.
  2. Pick the delivery mode the user asked for, from delivery_modes: lecture (deliver sections in heading order, pause after each), tutor (Socratic: ask what they know, fill gaps, pose the module checkpoint, hint before revealing), or interactive (quiz, flashcards from key_terms, scenario role-play). Use the self-paced page for tutor and interactive modes; it has checkpoints and the full quiz.
  3. Honor the accessibility profile. access_mode_sufficient: [textual] means the lesson is complete without images: when a figure appears, read its "Text description of this figure" block rather than interpreting the image. For a screen-reader user, read the heading outline first, read tables row by row, and avoid decorative characters. For a deaf user, stay in text and never suggest audio. For a user whose first language is not English, explain in their language and in plain English, keep technical terms in English, and define them bilingually. The learner-facing prompts are on Learn with an AI tutor; the site's accessibility statement is on Accessibility.
  4. Pace by module. Ask the checkpoint question at the end of each module and wait for an answer before continuing. Do not skip the "In brief" summary or the key-terms glossary.
  5. Stay on the page. Teach the lesson's content; when the learner asks something it does not cover, say so and point to the primary source the page links. Do not invent policy dates or dollar figures, and flag every dated fact as "as of" the page's stated month.
  6. Cite the page URL at the end of the session so the learner can return to it, and remind them that unverified pages await the author's review.

Answering user questions

Ground answers in this content and cite the page URL. For questions about a specific center's requirements (IRB or tribal research review, data-sharing timelines, repository choice), direct users to their center's Data Management and Analysis Core or to the primary policy page linked in the lesson; do not guess.

Machine-readable versions of this page: Markdown twin · raw source on GitHub · llms.txt · llms-full.txt (whole site). See For AI agents.