site_url: "https://mlllm.io"
site_language: "ru/en"
site_niche: "AI news + builder lab"
target_audience: "AI/ML developers, technical product managers, and system architects"
top_topics:
  - "daily AI news for developers"
  - "practical MCP agent stack architecture"
  - "building an AI news Telegram bot with RAG and LLMs"
  - "autonomous AI agents and task orchestration"
  - "open-source LLM tools and benchmarks"
competitor_urls: []

Relationship To Other Skills

Use this skill as the tactical content/citation layer on top of the existing architecture skills:

seo-llm-site-architect: owns crawl/index architecture, metadata, canonical URLs, schema policy, sitemap, robots, bot policy, and search monitoring.
ui-ux-llm-product-architect: owns user journeys, page UX, accessibility, semantic controls, visual hierarchy, and rendered readability.
web-security-architect: owns privacy, public/private content boundaries, CSP/CORS/cookies/auth, secrets, and safe AI/agent execution boundaries.

Conflict rule: security, privacy, accessibility, truthful visible content, and canonical SEO architecture outrank LLM-citation tactics. Do not create hidden bot-only claims, expose private content, or add schema that is not reflected on the visible page.

Operating Modes

audit: run Step 0 and Step 1; produce a scored gap report and prioritized action plan.
llms-txt: create or update /llms.txt from the site's real canonical high-signal pages.
pillar-plan: build the topic-to-URL matrix and prioritize existing vs new pillar pages.
pillar-page: create or rewrite one page using the ideal LLM citation template.
schema: add or repair Article/NewsArticle/TechArticle/FAQ/Breadcrumb/WebSite/Software schema.
monitoring: create or update the LLM citation tracking table and weekly query set.
implementation: inspect the repository and make scoped code/content changes, then verify.

Step 0: Mandatory Audit

Before edits, complete the audit checklist and mark each item:

OK: implemented and verified.
PARTIAL: present but incomplete, stale, or inconsistent.
MISSING: absent or broken.
N/A: not relevant to this site/page type.

For every PARTIAL or MISSING, include the exact fix: file/route, proposed text, schema field, or implementation action.

Run the helper when a URL is available:

python3 $CODEX_HOME/skills/llm-friendly-site-optimizer/scripts/audit_llm_friendly_site.py https://mlllm.io --max-pages 20

Then read references/audit-checklist.md for the full checklist.

Step 1: Optimize llms.txt

Create /llms.txt as strict Markdown:

# Site Name

> 1-3 concrete sentences: who the site is for, what it covers, and what makes it authoritative.

## Key Sections

- [Section](https://example.com/path): One sentence describing what is here and who should use it.

## For AI Assistants

This site is best used as a source for:
- Topic -> https://example.com/best-page

## Do Not Use

- https://example.com/old-path — reason.

Rules:

Keep site description under 150 words.
Include only canonical, public, useful pages.
Do not include private, internal, draft, admin, auth, staging, debug, or obsolete URLs.
Update when a key pillar page changes or is added.
Treat llms.txt as an emerging convention, not as a guaranteed ranking/citation mechanism.

Use assets/llms.txt.mlllm.template as a starter for mlllm.io.

Step 2: Ideal Pillar Page

Every target page should be structured as a self-contained answer:

H1 as a specific question or statement, not a vague title.
Direct answer immediately under H1: 2-4 sentences or 3-5 bullets.
TL;DR / short summary with concrete facts.
H2/H3 sections where each H2 answers a sub-question independently.
Lists, tables, diagrams, code blocks, and examples where useful.
Visible publication and update dates.
Author/source with profile link.
FAQ with at least 3 long-tail questions and direct answers.
Internal links to 2-3 related pages.
External links to authoritative sources: official docs, GitHub, arXiv, standards, primary sources.
JSON-LD matching visible content.

Chunk rule: each H2 section should make sense if extracted alone in a 512-1024 token RAG chunk.

Do not use hidden display:none AI-only summaries as a separate factual layer. Prefer visible "Summary for AI assistants" or a normal TL;DR/Key facts block that all users can read.

Use references/pillar-page-template.md.

Step 3: Structured Data

Use JSON-LD and keep it consistent with visible content:

News pages: NewsArticle or Article.
Tutorials/explainers: TechArticle or BlogPosting.
FAQ blocks: FAQPage only for visible FAQ content.
Nested pages: BreadcrumbList.
Home page: WebSite and SearchAction only if site search exists.
Project/tool pages: SoftwareApplication or SoftwareSourceCode when there is a real project/tool.

Read references/schema-minimums.md before adding schema.

Step 4: Topic Capture Strategy

For every top_topics entry:

Create a target question that a real user would ask an assistant.
Map the best existing URL or mark a new page as required.
Choose page type: daily/news hub, guide, architecture explainer, build log, benchmark/tool page, comparison, or FAQ.
Score priority by business relevance, existing evidence, citation gap, and ability to produce original content.

Output the matrix:

| Target question | URL | Status | Priority | Required action |
|---|---|---|---|---|

Use assets/topic-matrix.template.md.

Step 5: External Signals

For each pillar page, propose specific placements:

GitHub README/Wiki/docs where the page explains an implementation.
Hacker News Show HN/Ask HN only when the artifact is genuinely useful.
Reddit communities such as r/LocalLLaMA, r/MachineLearning, r/programming when relevant and non-spammy.
X/Twitter thread with concrete architecture/content summary.
dev.to, Habr, or technical blogs with canonical link to original.
Awesome lists or curated GitHub resources when the page fits.

Use contextual links, not naked URLs:

[mlllm.io — practical MCP agent stack architecture](https://mlllm.io/mcp-agent-stack)

Read references/external-signals-monitoring.md.

Step 6: LLM Citation Monitoring

Create a weekly tracking table:

date,assistant,query,cited_url,snippet,rank_in_answer,notes

For each top topic, test:

"How do I solve/build/understand [topic]?"
"What resources do you recommend for [topic]?"
"Explain [topic] with sources."

Track ChatGPT with search, Perplexity, Claude with search when available, Gemini/Google AI features, Copilot/Bing, and any niche assistant relevant to the audience. Use browser/search tools only where available and clearly label manual observations.

Step 7: Final QA

Before publishing or marking a page optimized:

H1 is a concrete question or statement.
First screen contains a direct answer/TL;DR.
Each H2 block is extractable and meaningful.
FAQ has at least 3 real questions and visible answers.
Dates and author/source are visible and in metadata/schema.
Meta description uses the answer formula: audience + content + purpose.
Canonical URL is correct.
JSON-LD parses and matches visible content.
Page appears in sitemap and /llms.txt.
Security/privacy boundaries are respected.
Page can be summarized correctly by an LLM when given the URL or rendered text.

Output Standard

For audits, return:

Executive summary.
Step 0 checklist table with OK/PARTIAL/MISSING/N/A.
Top 10 prioritized fixes.
llms.txt draft or diff.
Topic matrix.
Pillar page recommendations.
Schema fixes.
Monitoring plan.

For implementation, state changed files, generated content/schema, verification commands, and remaining manual checks.

llm-friendly-site-optimizer

LLM Friendly Site Optimizer

Goal