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Seo geo answerblocks

Skill Hainrixz/claude-seo-ai/skills/seo-geo-answerblocks

Audit and generate answer-extractable content — verify each question heading is followed by a ~40-60 word direct answer, passages are self-contained (~134-167 words, no unresolved anaphora), and lists/tables/definitions/TL;DR blocks exist; draft answer-block and summary rewrites for the user to accept. Module M11. Feeds the AI Visibility score.From its SKILL.md

Install
npx -y skills add Hainrixz/claude-seo-ai --skill seo-geo-answerblocks

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • runs commandsInstructs the agent to run 2 commands, including `node ${CLAUDE_SKILL_DIR}/../../scripts/check-answerblocks.mjs --url <u>` and 1 more.

SKILL.md

4.2 KB, 907 tokens by cl100k_base, as published. Nobody here has run it

seo-geo-answerblocks (M11)

AI engines cite passages they can lift whole, without surrounding context — so passage structure is the highest-leverage on-page AI signal. This module checks extractability; for who is allowed to fetch and cite the page, see references/ai-crawlers.md.

Audits

Working from the PageSnapshot (rendered_dom if present, else raw_html):

  1. Heading → direct answer: for each H2/H3 that reads as a question (or implies one), is the first paragraph a direct, ~40-60 word answer — not a windup ("In this section we'll explore…")?
  2. Self-contained passages: are top-level passages ~134-167 words and answerable on their own? Flag unresolved anaphora ("as mentioned above", "this", "the latter") that needs prior context to parse.
  3. Structured forms: presence of lists, comparison tables, and explicit term definitions where the content warrants them — these are disproportionately quoted.
  4. Question-shaped headings: do headings phrase real user questions (how/what/why/when), matching likely prompts?
  5. TL;DR / summary blocks: an up-front summary or key-takeaways block the engine can extract verbatim.
  6. Semantic completeness: can each passage answer its heading with zero external context — the strongest single correlate of AI citation. This is the check that gates the score.

Fixes

  • PROPOSED: answer-block rewrites (heading → tight 40-60 word lead) and TL;DR drafts, generated from existing page content, surfaced one item at a time for the user to accept/edit. Each becomes a fix_preview diff only after acceptance.
  • ADVISORY: structural suggestions ("split this 400-word passage", "add a definition list here") where intent is editorial.
  • Never silently rewrite published prose, and never invent facts to fill a passage — if a claim is missing leave a clearly-marked TODO placeholder for the user. No finding here is auto.

Verification

  • Method is manual_review. The deterministic part is reproducible offline: word-count windows (answer 40-60w; passage 134-167w), question-heading detection, anaphora openers, and TL;DR presence via node ${CLAUDE_SKILL_DIR}/../../scripts/check-answerblocks.mjs --url <u> (or --file <path>). Use manual_review as the finding's verification.method (the enum has no heuristic value); the script is the verification.reproduce command.
  • Semantic completeness and anaphora resolution still require a judgment pass; when that pass or the rendered DOM is unavailable, status is needs_api (or warn), never a false pass.

Findings

Findings conform to schema/finding.schema.json. Examples:

  • M11.heading.no_direct_answer — a question H2 followed by a windup, not an answer (status fail, severity 5, fixable: proposed, axis ai, confidence directional).
  • M11.passage.unresolved_anaphora — passage opens with "As mentioned above…" and is not self-contained (status warn, severity 5, fixable: proposed, axis ai, confidence directional).
  • M11.summary.missing_tldr — no up-front summary/key-takeaways block on a long-form page (status warn, severity 2, fixable: proposed, axis ai, confidence directional). Each finding's evidence.observed quotes the page (heading text + first sentence, or the offending clause) and verification.reproduce is the runnable command above; expected_impact is banded + confidence-tagged with no naked percentage.

Honesty

  • Word-count windows are heuristics from observed AI-citation patterns, not engine-published thresholds — they are directional, not established; flag a passage, don't hard-fail purely on length.
  • Adding a "TL;DR" label or stuffing question headings does nothing if the passage still can't answer standalone — semantic completeness is what carries weight, formatting alone is a myth this tool does not ship.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most marketing audience skills give in 907 tokens

Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07

  • Apply Poppins font to headingsin 41 of 690, across 6 files
  • Apply Lora font to body textin 41 of 690, across 6 files
  • Use Arial fallback for headingsin 39 of 690, across 4 files
  • Use Georgia fallback for body textin 39 of 690, across 4 files
  • Maintain text hierarchy and formattingin 39 of 690, across 4 files
  • Use accent colors for non-text shapesin 38 of 690, across 3 files
  • Use RGB values for precise color matchingin 38 of 690, across 3 files
  • Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
  • Read product marketing context file before asking questions, starting, or auditingin 35 of 690, across 23 files
  • Use active voice instead of passive voicein 26 of 690, across 10 files
  • Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
  • Prioritize clarity over clevernessin 22 of 690, across 8 files

Said here and by no other author read

  • run the verification script before reporting
  • check question headings have direct answers
  • flag top-level passages lacking self-contained context
  • verify presence of lists, tables, and definitions
  • check for up-front summary blocks
  • verify each passage answers without external context

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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