GEO Measure

SkillWeb & browsing

Measure GEO visibility from an approved, file-backed engine observation bundle. Use for AI answer mention rate, source inclusion, citation share, query-panel coverage, GEO monitoring, 监测 AI 可见度, 衡量 GEO 效果, and offline baseline comparison. Exclude live scraping, platform login, automated collection, and unsupported causal claims.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the GEO Measure skill

What this skill tells your AI

The instructions your AI receives, as published by yaojingang/geohub in skills/geo-measure/SKILL.md and read by ahel’s review.

Workflow

  1. Read references/measurement-method.md and verify collection permission.
  2. Prepare a protocol 1.0.0 engine observation bundle from manual export, approved API, or recorded fixture.
  3. Run python3 scripts/run_measure.py --input <bundle.json> --output <runs-root>.
  4. Inspect visibility-report.json, quality-report.json, and run-lineage.json; surface every gap and collection limitation.
  5. Deliver the Artifact Bus run directory as the output contract.

Output contract

Produce the input snapshot, visibility-report.json, quality-report.json, run-lineage.json, and run-manifest.json. Preserve query-level components, per-engine metrics, numerators, denominators, missing counts, panel version, and semantic digest.

Boundaries

Measurement is offline and file-backed. It never logs in, scrapes consumer AI pages, bypasses access controls, or turns recorded fixtures into live-effect evidence. Read references/output-contract.md before making a comparison claim.

Signals

GitHub stars
159
Forks
27
Last commit
Aug 2026

ahel review

  • K6low
    bundled executables the agent is told to run

Automated review, not a security audit. Ruleset v1+k2.

Advanced
Catalog kind
skill
Gateway key
geo-measure
Source
github.com/yaojingang/geohub