seo

name

seo

description

SEO + AI-search (AEO/GEO/LLMO) optimizer for the Agora storefront (dancenitra.github.io/agora) — a bilingual EN/SK static research blog on GitHub Pages. Synthesized from 37 SEO YouTube transcripts (vault `04 Resources/raw/YouTube Transcripts - SEO Ranking/`) into an actionable pre-publish checklist + site-wide technical audit, tailored to OUR stack and rules (static HTML via tools/render_post.py, owner anonymity, verified-numbers/cite-prior-art discipline, owner-gated outreach). Use when: publishing/rendering a post, the owner says "seo"/"rankuj"/"optimize for ranking"/"SEO friendly"/"rank #1", auditing the site for SEO, or before any `render_post.py --piece`. Two modes: (A) per-post optimization, (B) site-wide technical audit. Goal: rank #1 on Google AND be the cited source in ChatGPT/Perplexity/AI-Overviews for our niche.

/seo — make every Agora publication rank (Google) and get cited (AI search)

Our unfair advantage, name it first: we publish original measured results, replications, and falsifiers nobody else has (the Crucible). That is exactly what both Google (helpful, original, E-E-A-T content) and LLMs (factual, citable, authoritative statements) reward. SEO for us is not keyword-stuffing — it is making our genuinely-unique research findable, skimmable, and quotable. The bottleneck has always been distribution; this skill is the on-page + technical half of fixing it. (#1 Google is a long game — authority + freshness + mentions compound over months; on-page SEO is necessary, not sufficient. The compounding lever is consistent publishing of citable results + getting mentioned.)

Hard guardrails (never break, even for ranking):

• Verified numbers only. Re-check every cited number vs its source Lab/.lab.json before it goes public (existing rule). A wrong stat that ranks is worse than no rank.
• Anonymity. Author = the Agora / …@users.noreply.github.com identity, never the owner's real name/email. Author schema uses "Agora" (Organization), not a person.
• Bilingual EN/SK stays (the toggle). Both languages must be in the served HTML (they are — <span class="en">/<span class="sk">), so crawlers see both.
• Outreach/mentions are owner-gated. This skill optimizes our OWN pages freely; getting cited on others' sites (Reddit/HN/roundups/Wikidata) is the gated outreach lane — draft, brief in Slovak, let the owner post.
• No paid-tool dependency. Use free surfaces: Google Search Console, Bing Webmaster Tools, Google Trends, autocomplete/"People also ask", our local LLM. Don't make the workflow require Ahrefs/Semrush/Surfer.

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MODE A — per-post optimization (run this for every post BEFORE render_post.py --piece)

Work on the piece spec / draft and adjust the EN + SK bodies and the spec fields. The render template (tools/render_post.py) already emits <title>, meta description, JSON-LD Article, OG/Twitter tags, tables, blockquote callouts, and SVG figures — use them well.

1. One keyword, picked BEFORE finalizing the angle

• Choose ONE primary long-tail query the post targets (3–8 words, high intent, low competition). For us these are specific technical questions, e.g. "does long context kill RAG", "agent memory poisoning defense", "LoCoMo multi-hop recall benchmark". We can win these because almost nobody has the measured answer.
• Mine real demand for free: Google autocomplete (type the seed), People Also Ask, Related searches, and once GSC is live, the Performance → queries already showing impressions. Harvest the sub-questions — each becomes an H2 or an FAQ entry.
• Classify intent = informational (almost always for us) → the page is an explainer/benchmark, answer-first, not a pitch.
• Reuse our signature terminology consistently so AI associates it with us and cites it by name: Adaptation–Corruption Separation Law, the Crucible, Diversity-Flip Law, mnemo, RAMR. Coin a memorable name for each new result.

2. The "Three Kings" — primary keyword in all three

• <title> (spec title / title_sk): primary keyword near the front, ≤ ~60 chars so it doesn't truncate, and lead with the measured result for CTR. Good: "Does long context kill RAG? We measured it", "Memory poisoning hits 70–95% — a corroboration gate stops it". Avoid vague clever titles.
• H1 = the post title (render uses the # line / spec title). Keyword present.
• First 40 words / first sentence: state the direct answer with the keyword in it. No flowery intro. This is what featured snippets and AI Overviews lift verbatim.

3. Slug, description, tags

• Slug (spec slug): the keyword phrase, hyphenated, concise (does-long-context-kill-rag, memory-poisoning-corroboration-gate). Controllable per-post; don't make Google guess.
• Meta description (spec desc / desc_sk): 1–2 sentences, includes the keyword, states the result + entices the click. CTR is a signal.
• Tags (spec tags): the topic entities (e.g. agent memory · memory poisoning · LLM security · replication).

4. Answer-first, skimmable, citation-shaped body

• Lead with the result (and a quotable number/table) high on the page — this is both the snippet bait AND the "frontloaded linkable point" others cite.
• Heading hierarchy: logical H2/H3, each answering one sub-question (map People-Also-Ask questions to headings). AI directs answers to the matching heading.
• At least one list and one table. Tables of measured results/comparisons get quoted verbatim by AI Overviews/Perplexity and are snippet-eligible. We already do this — keep it.
• An FAQ block (3–5 concise Q&A) near the end answering the obvious follow-ups → snippet + AI-citation eligible, and feeds FAQ schema.
• Semantic coverage, not repetition: include the related entities/methods/datasets/prior-art the top results cover (we do this naturally — name the real papers, datasets, methods). Never keyword-stuff.
• E-E-A-T = our moat: first-hand experiment (we ran the Lab), cite reputable sources + prior art, state the falsifier and the honest limits/boundary. Show the measured numbers. This is exactly what Google's helpful-content + AI trust both want.
• Internal links (topic cluster): link the post to ≥3 related Agora posts and back to a pillar (e.g. an "agent memory" hub). Internal links are the only backlink we fully control; they spread authority across the cluster.
• Outbound links to the authoritative sources we cite (the papers we replicate) — signals topical context and satisfies our cite-prior-art rule.
• Figures with descriptive alt text (our SVG <figure> should carry a meaningful aria-label/<figcaption> — already does). Charts get cited by AI more than prose.

5. AEO/GEO layer (rank in AI answers)

• Mirror the ideal AI answer: concise direct answer first, supporting detail after. When the engine drafts an answer and looks for corroboration, exact-match structure → "this confirms it" → we get cited.
• Make data explicit as numbered lists/tables (not buried in prose) — AI preferentially pulls original data + comparisons.
• LLM-summary self-test (pre-publish GATE): paste the finished post into our local strong model and ask it to summarize + list the key claims:

  # uses our local reasoning model (glm-5.2 via Ollama cloud-route); cloud-free-OK (data, not generation of claims)
  

  Run it through agora.execution.llm_client.call_llm(sys="Summarize this article and list its key factual claims with numbers.", usr=<post text>, tier="medium"). If the summary misses or mangles a key result, rewrite for clarity until the model gets it right. If our own strong model can't extract the finding cleanly, neither will ChatGPT.

6. Schema (JSON-LD) — verify/extend the rendered HTML

• Article/BlogPosting (render already emits this) — confirm headline, datePublished, dateModified (update on edits), author = Organization "Agora", inLanguage ["en","sk"].
• Add FAQPage JSON-LD when the post has an FAQ block (machine-parseable Q&A → snippet + AI).
• Add Organization site-wide (name "Agora", logo, sameAs the GitHub/HF/Zenodo URLs) so AI knows who we are.
• Consider Dataset schema for Crucible/benchmark posts (we publish crucible.json — machine-readable; mark it up).
• (If the render template lacks FAQ/Organization/Dataset/canonical/hreflang, that's a Mode-B implementation task — add it to tools/render_post.py once, then every post benefits.)

7. Publish + index

• Render (render_post.py --piece), verify deploy 200 (existing flow), then request indexing in Google Search Console (Inspect URL → Request indexing) and confirm the URL is in sitemap.xml. Submit to Bing Webmaster Tools too — ChatGPT search uses Bing's index, so Bing presence is disproportionately valuable for AI visibility.

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MODE B — site-wide technical audit (run occasionally; "the Claude-Code audit loop")

Run a thorough pass over public/ and tools/render_post.py; fan out sub-agents (one each for: missing/weak meta descriptions, missing alt text, internal-link opportunities, schema gaps, page-speed/image weight). Fix:

• sitemap.xml — generate one listing every post URL with <lastmod>; add to tools/render_post.py build (static site → must be scripted, not a plugin). Submit to GSC + Bing.
• robots.txt — exists, not Disallow: /; explicitly allow AI crawlers (GPTBot, Google-Extended, bingbot); add the Sitemap: line.
• Canonical tags — per-page <link rel="canonical"> to the preferred URL (avoid http/https, trailing-slash, query dupes diluting signals).
• hreflang — we serve EN+SK in one page via the toggle; add hreflang annotations (or document the decision) so the bilingual content isn't read as duplicate. (Our render keeps both languages in served HTML; decide one-URL-both-langs + inLanguage vs separate URLs — note in the post template.)
• No stray noindex; every real post resolves index,follow.
• Speed: convert figures to WebP where raster; keep assets lean; lazy-load below-the-fold; GitHub Pages CDN is already fast — just don't bloat. Check with Google PageSpeed Insights.
• Semantic HTML: content in <main>/<article>, clean H1→H2→H3 (render already does).
• Text in served HTML (not JS-injected) — we're static, keep it; never hide post text behind JS-only rendering (crawlers + AI bots largely don't run JS).
• Crawl depth ≤ 3 clicks from the homepage to any post; keep the index/nav flat.
• Measurement: set up Google Search Console + Bing Webmaster Tools + GA4 for the domain (owner action — provide the steps; he verifies ownership). Then the loop: GSC Performance → Pages, Compare last 28d vs previous, act on rising winners (double down) and decayers (refresh the post, bump dateModified, re-request indexing). Pages ranking positions 5–15 are the low-hanging fruit — inject the keyword into the Three Kings and re-index for outsized CTR jumps.

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Reusable SEO post structure (the writing template)

1. Keyword + intent chosen first; SERP + People-Also-Ask scanned.
2. Headline ≤60 chars, keyword near front, leads with the measured result.
3. Answer-first intro — direct answer + keyword in the first 40 words.
4. Body — H2/H3 per sub-question; ≥1 list + ≥1 results table; semantic/entity coverage; internal+outbound links; quotable stat/table frontloaded.
5. Figure(s) with alt text/figcaption (SVG charts of the measured result).
6. Falsifier + honest limits (our standard) — doubles as E-E-A-T.
7. FAQ block (3–5 Q&A) → FAQ schema.
8. Conclusion with a next-step internal link (no dead end) + the repo/mnemo CTA.
9. Schema (Article + FAQ + Organization), canonical, dates.
10. LLM-summary self-test passes → render → request indexing (Google + Bing).

Provenance

Synthesized 2026-06-27 from 37 transcripts in the vault 04 Resources/raw/YouTube Transcripts - SEO Ranking/ (general Google SEO, technical SEO, keyword research, SEO blog-writing, AI-search/AEO/GEO/LLMO; off-target YouTube/local/WordPress/Instagram/e-commerce tactics deliberately excluded). Keep this skill updated as search/AI-search rules change (the 2026 transcripts already show the shift from clicks → AI citations).