Aeo Geo Improvement Bot

AEO / GEO Improvement Bot

The visibility audit tells you where a brand is invisible in AI answers. This skill tells you what to do about it -- it is the second half of the loop: measure, diagnose, fix, re-measure.

When to use

• Right after running AEO / LLM Visibility Audit and getting back gap_questions.
• When the ask is to build an actual improvement plan, not another audit -- content briefs, schema fixes, citation targets, in priority order.
• To decide what a content/SEO team should work on first when there are more gaps than there is time.

When NOT to use

• To measure share of voice in the first place -- that's the visibility audit skill.
• As a substitute for writing the content or shipping the schema -- this produces the plan and the specific instruction, not the final asset. Feed the content fixes to a content-writing skill and the schema fixes to a schema-audit skill.

Method

1. For every gap question, read four signals: does an owned page answer it, does that page carry schema markup, how many independent (third-party) sources corroborate it, and how much demand it represents.
2. Classify the gap into exactly one root cause, cheapest-explanation-first: no content > no schema > no citations > authority deficit. Fixing the wrong layer wastes effort -- adding schema to a page that doesn't exist does nothing.
3. Prioritise: higher demand first; at equal demand, cheaper fixes (schema) surface before expensive ones (earning citations or authority) so the plan front-loads fast wins.
4. Attach a re-check window per fix type -- schema changes show up in re-crawls faster than earned citations do.

Inputs

• brand -- the brand name
• gap_questions -- from the visibility audit's gap_questions[]
• signals -- per question: has_owned_content, has_schema_markup, third_party_mentions, search_volume_proxy (1-10). In production these come from a site crawl + a third-party source search; the demo ships a deterministic sample.

Output (JSON)

fixes[] (each with question, fix_type, action, recheck_after_days, priority), fix_type_mix, and a one-line summary naming the top fix to start on.

Run it

python scripts/geo_improve.py         # built-in sample
python scripts/geo_improve.py in.json # your own brand + gap_questions + signals

Zero dependencies, no API keys. To go live: swap the sampled signals for a real site crawl (does the page exist, does it carry JSON-LD) and a real third-party mention count; the classification and prioritisation logic stays identical. Chain it after aeo_score.py's output and, on a schedule, after re-running the audit to confirm each fix moved the needle -- a scheduled n8n version of the full measure-diagnose-fix-recheck loop ships in pipeline-automation/aeo-geo-improvement-loop.json.