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Answer engine optimizer

Skill siddiqss/semantic-seo-suite/skills/answer-engine-optimizer

Grounded semantic SEO, GEO & off-page as Claude Code skills — with a fabrication guard that refuses to invent numbers. Free & MIT.

Install
npx -y skills add siddiqss/semantic-seo-suite --skill answer-engine-optimizer

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

  • 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Optimize a brand's mapped content to get cited by AI answer engines — Google AI Overviews, ChatGPT, Perplexity, Gemini. Scores drafts for citation-readiness with aeo_score.py, produces a per-node hardening checklist, and spot-checks live AI answers for whether the brand (vs competitors) is cited. Use whenever the user mentions AEO, GEO, LLM SEO, "getting cited by ChatGPT/Perplexity", AI Overviews, answer engines, AI search visibility, or asks why an AI assistant recommends competitors and not them. The GEO half of seo-performance-tracker. Never invents a visibility score. Triggers on AI-visibility / answer-engine intent broadly.

SKILL.md

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answer-engine-optimizer

The citation feedback loop. Where seo-performance-tracker measures Google rankings, this optimizes for being the source an LLM quotes — which, for a tool category whose buyers research inside ChatGPT and Perplexity, is where a lot of the demand now decides.

It reuses the suite's spine: read the brand workspace, respect the grounding tier, tag every value, and feed results back into the map and calendar. It layers onto the on-page map — same nodes, hardened — it does not replace it.

Read first: ../../framework/answer-engine-optimization.md (the method + the honesty rules), then ../../framework/macro-micro-semantics.md (the writing tactics it scores).

Preconditions

  • entity-profile.json + topical-map.json exist (run seo-brand-foundation / topical-map-builder first).
  • Drafts to score live in brands/<slug>/drafts/. With no drafts yet, the skill still produces the hardening spec and the live-answer probe.
  • Live-answer probing needs grounding.sources.web_search: true (T1). Without it, do the offline scoring only and say the probe was skipped — do not guess citations.

Workflow

  1. Score citation-readiness (T0, offline). For each draft:

    python ../../scripts/aeo_score.py --draft brands/<slug>/drafts/<slug>.md \
  --schema-dir brands/<slug>/data/schema --json

    Run it after validate_draft.py is clean — AEO is advisory, fabrication is a gate. Collect score, grade, and the specific fixes (DEF / QA / TLDR / LIFT / BREV / SELF / SCHEMA). Scores are measured (mechanical), the recommended rewrites are asserted.

  2. Probe live answer engines (T1, web_search). For the highest-value target queries (core-section, especially comparison/alternative nodes), query them answer-style and record, per query + engine + date: is the brand named? cited with a link? which competitor sources are quoted instead? This is a dated spot check (n=1 per probe), labelled measured — not a rank tracker. Never aggregate it into a visibility %.

  3. Write the AEO report → brands/<slug>/audits/<date>-aeo.md:

    • Readiness table — per node: AEO score, grade, top fixes (measured + asserted).
    • Live citations — per probed query: brand cited? competitors cited? (measured, dated, with the query text; honest about the tiny sample).
    • Hardening queue — nodes <70, ranked, with the concrete edits.
    • If web_search is off: state the probe was skipped; emit only the readiness table.

  4. Feed the loop.

    • Nodes scoring <70 → mark needs-update in the map; push up calendar.md.
    • Apply hardening to drafts via semantic-draft-writer; ensure JSON-LD via linking-and-schema. Re-score to confirm the lift.
    • Queries where competitors are cited and the brand isn't → a hardening task on the owning node and a signal for off-page authority (link-opportunities).
    • New questions found while probing → query-network additions via topical-map-builder.

Definition of done

  • Every existing draft scored; a dated AEO report with the three sections written.
  • Hardening queue fed back into map statuses + calendar.
  • No invented visibility number anywhere — citations are dated, per-query observations or they are absent. If web_search was off, the report says so.

Grounding ladder

  • T0: offline aeo_score.py readiness scoring + hardening spec. Fully useful alone.
  • T1 (web_search): + live answer-engine spot checks (measured, dated, per query).
  • T2: no paid dependency; SERP-feature data from DataForSEO (if on) can corroborate which queries trigger AI Overviews, labelled measured.

Keep looking

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