Answer Engine Optimizer

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//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//drafts/.md \ --schema-dir brands//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`.

1. 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 %.

1. Write the AEO report → brands//audits/-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.

1. 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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: siddiqss
• Source: siddiqss/semantic-seo-suite
• License: MIT

Install and usage instructions live in the source repository linked above.