geo-score

geo-score is an open-source AI visibility (GEO) scorer for websites, by Jianrun Tech (见润科技). Formerly geo-watch: geo-watch merged into geo-score in 1.2.0 and lives on as its levels 2 and 3. Website, docs and a free online check: geo-score.dev.

geo-score in 60 seconds

One command, a 0–100 score, the three gates, the gaps worth fixing first, and why every point can be re-checked.

https://github.com/user-attachments/assets/2270314f-446a-4c2d-b27f-11e1f6d66ff8

English · 60 seconds · Chinese version

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Can AI engines cite your site? A free 0–100 score from one command. Do they? Check through their APIs with your own keys.

geo-score scores a website from 0 to 100 on whether AI answer engines can reach it, parse it, trust it and cite it, against a published, versioned rubric that anyone can recompute. Then, with your own API keys, it asks the engines the questions your buyers ask and records whom the answers cite. The score is free and needs nothing installed; the citation results are reported beside it and never added to it.

Quick start · Screenshots · What it checks · How accurate is it? · Docs · Website · Changelog · Public leaderboard

Version: the current release is geo-score==1.8.3 on PyPI, with its tag, wheel and checksums on GitHub Releases; what changed is in the changelog. Every install line on this page is pinned to that release (scripts/release.py --bump moves them together), so the tool you run is the one these pages describe. The rubric is versioned separately, and v1.1 is current. main can run ahead of the release: for CI and client work, use the pinned release.

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❤️ Sponsors

Want to appear here? · Sponsorship never affects the rubric, any score, the public leaderboard or the technical recommendations in these docs.

Sponsors have not been tested for geo-score's citation measurements (levels 2 and 3). Those need each engine's own search-enabled API, and a relay may not pass web search and citations through unchanged.

What problem it solves

GEO means Generative Engine Optimization — getting cited by ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot. Nothing to do with geography or maps.

An answer engine quotes passages from sources it can reach, parse and trust. The reasons a site is left out are often small and invisible from a browser: a robots.txt line that turns a retrieval crawler away, a page whose text only exists after JavaScript runs, figures with no source. geo-score finds which of these a site has and what each one is worth, then checks whether the engines cite it, in three levels:

Score (free, no key) → Ask one question (your API keys) → Watch a question set every week (your API keys).

Three levels, one tool

Level	What it answers	Needs	Command
1 · Score	Can AI engines reach, parse, trust and cite the site? 0–100 against the open rubric	Nothing: no key, no install	geo_score.py stripe.com
2 · Ask	Right now, for a question you care about, do they cite it?	An API key for any of OpenAI, Perplexity, Gemini, Anthropic, OpenRouter	geo_score.py stripe.com --ask "payments API for marketplaces"
3 · Watch	How often are you cited, against which competitors and sources, week over week?	Your API keys and a fixed question list	geo_score.py watch run

flowchart LR
    S[Your site] --> L1["Level 1 · Score<br/>free, no key"]
    L1 --> R["0–100 readiness<br/>5 pillars · 3 gates · biggest gaps"]
    Q[Questions your buyers ask] --> L2["Level 2 · Ask<br/>one question, your API keys"]
    Q --> L3["Level 3 · Watch<br/>a fixed question set, every week"]
    L2 --> C["Who the answers cite<br/>you · competitors · third-party pages"]
    L3 --> C
    R -. reported side by side, never summed .- C

Level 1 is the score. Levels 2 and 3 measure the outcome the rubric keeps out of the score on purpose (two scores, never one): they are reported next to the 100 and never summed into it. A site can score 90 and still lose every answer to a competitor with more third-party coverage, and the reverse also happens, which is why you want both.

Why this is a different question from SEO

Classic SEO asks where do I rank. Answer engines don't rank — they retrieve passages, decide whether a source is worth quoting, and cite it. Different question, different failure modes: a site can sit at position 3 on Google and never be quoted. Because engines retrieve passages, this rubric gives Content Citability the most weight. That is a design choice, not a measured effect: the evidence for how much passage shape matters is thin, and the evidence table says where.

Most of what the rubric checks is mechanical and cheap to fix — a robots.txt line, a JSON-LD block, a date in a template, a paragraph rewritten so it stands on its own. The hard part is knowing which of them you are missing, and what each one is worth.

Two guides start here: can ChatGPT read your website?, a check you can do by hand and then with geo-score, and how geo-score differs from a general AI SEO skill.

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Screenshots

Every image shows real output, captured for this page.

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Core capabilities

Capability	What it does	More
Score (level 1)	21 tiered checks in five pillars, three gates, the biggest gaps first, and what each next tier needs. One file, standard library only, no key	What it checks
Ask (level 2)	Puts one question to ChatGPT, Perplexity, Gemini and Claude through their search-enabled APIs, with your keys, and reports whom each answer cites	Levels 2 and 3
Watch (level 3)	A fixed question set, week over week: cited rate with a 95% interval, share of voice, the sources the engines lean on, and a diff that calls a change a change only when a paired test says so	Watch methodology
Reports and diffs	Text, JSON against a published schema, SARIF and JUnit; diff compares two reports check by check, and --baseline re-scores the same pages	CLI reference
MCP server	All three levels as tools for agents over stdio; --read-only gives a server that cannot spend	MCP server · guide/mcp.md
GitHub Action	Scores on every push, fails the build on a regression or a gate at zero, and writes a job summary	GitHub Action · action.yml

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What it checks

21 tiered checks totalling 100 points, plus 4 bonus checks worth up to +6 outside the denominator. Full specification: rubric/v1.1.md · 简体中文

Pillar	Pts	Asks
Reachable — gates	15	Can a retrieval crawler get the page at all? robots.txt, live reachability across 10 AI user-agents, server-rendered content
Understandable	22	Can it tell what the page and the company are? Organization + WebSite, llms.txt, sitemap, breadcrumbs, page-type schema
Content Citability	35	Is there anything here worth quoting? Self-contained answer passages, headings that match how people ask, sourced figures, real bylines, freshness
Brand Credibility	18	Why should an engine trust it? Knowledge-graph entity, third-party listings, sameAs that resolves, video presence
Answer Fit	10	Is the content shaped to be lifted into an answer?

Content Citability carries the most weight by design: answer engines retrieve passages, not domains. Whether passage shape outweighs domain authority is not established: the heaviest single check, p2.answer-passages, rests on thin support (where the evidence is thinnest).

Every scored check is tiered — 2 to 4 tiers, each naming a count out of the 8 sampled pages, so two people scoring the same site agree on the arithmetic. Three checks are gates: score zero on crawler access, live reachability or server-rendered content and the result caps at 40, because until a crawler can reach the content nothing else you change has any effect.

Bands

0–30	31–50	51–65	66–82	83–100
Not started	Early	Growing	Solid	Leading

Band names describe a stage, not a verdict. External benchmarks put most business sites in the 30–55 range, so a score in the forties is ordinary, not alarming. In this repository's own benchmark, 85% of 324 sites reach Early and 6% reach Leading (where the cuts fall).

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324 sites, scored in public

A quarter of them are unreadable to AI crawlers. 73 sites have a gate check at zero — an AI retrieval crawler cannot get the content, so it has nothing of theirs to quote. 20 block AI crawlers by name in robots.txt, which is an editorial choice and reported as such — amazon.com lands at 17 for exactly this reason. 41 serve a page whose body only exists after JavaScript runs. Their content is there, a browser sees it, and a crawler gets an empty shell. That group almost certainly did not choose it. At a further 12 the server refuses crawler user-agents (unverified probes from the auditor's address).

Median 55 (95% CI 51–58, stats.py). Range 11 to 96. 66 sites were scored on fewer than four pages; without them the median is 59.

Site	Score	Band
resend.com	96	Leading
pulumi.com	94	Leading
supabase.com	93	Leading
elevenlabs.io	91	Leading
lumalabs.ai	90	Leading
…
qcloud.com	12	Not started
mercadolibre.com	12	Not started
keepa.com	11	Not started

The full table, by sector → · markdown · raw data · CSV · every site's full report · re-run it

Two more findings worth the click. Sites in the five China sectors score 22 points lower than everyone else (95% CI 16–30; median 36 against 58; earlier samples measured with 1.1.0 put the gap between 16 and 23 points). Grouped by the language a site serves instead, the 54 Chinese-language sites score 26 points lower than the 270 others (95% CI 22–32), and within matched sector families the gap runs from -8 to 27 points. Counted check by check in raw points, 69% of the gap sits in checks the CLI reads with a heuristic, so at most that share could be the tool misreading Chinese pages (the breakdown). The two largest per-check differences, question-shaped headings and self-contained answer passages, are heuristics the CLI also read lower than a human on the one Chinese site in the hand-audit comparison, so part of the gap may be the tool reading Chinese pages conservatively. From 1.5.0 the CLI reads each page in its own language and counts Chinese task, explanation and comparison headings; the leaderboard above was measured with 1.5.0, so its figures include that change. And statistics that carry a source and a named, verifiable byline are among the three largest gaps on more than half the sites.

Every number here is reproducible with the command in Quick start (the benchmark was run with 1.5.0 on 2026-10-02) — and we measured how reproducible. On 2026-10-12, 1.8.0 scored the whole list three times from one place the same day (the third run re-reading the first run's pages): of the 317 sites scored every time, 99% landed within ±5 between the first two runs and 81% scored identically (ICC 0.99, SEM 1.9; REPRODUCIBILITY.md, derived by benchmark/retest.py). An older 1.1.x build, measured on 2026-09-09, put 96% within ±5 and 45% identical. Read one site's score as ±5 rather than as exact; medians are stable. Most of what still moves is the knowledge-graph lookup, which went unanswered on most sites from that vantage, and the gate checks of sites whose bot protection answers a crawler differently from one run to the next. The band, the control experiment and the per-site pairs are in benchmark/REPRODUCIBILITY.md.

For five reference sites we also publish hand-scored audits covering all 21 checks, with the evidence behind each one: examples/audits/v1.1/.

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How accurate is it?

Five questions people ask, and where this repository answers each one:

Question	Answer	Record
Score the same site twice: how far apart?	A same-day retest with 1.8.0 on 2026-10-12: 99% of 317 sites within ±5 and 81% identical (ICC 0.99)	REPRODUCIBILITY.md
Can anyone recompute it?	Yes: every check, tier and weight is published, so the same pages give the same score whoever runs it	rubric/v1.1.md
Does it match a person reading the same rubric?	80% of 84 check pairs land in the same tier; only 5 sites, all of them calibration sites	VALIDITY.md
Do the weights decide the ranking?	With equal weights, or 300 random perturbations of them, the rank correlation stays at 0.99 or above	SENSITIVITY.md
Are higher-scoring sites cited more often by AI?	The preregistered test's first wave (2026-10-12 to 14; 16 categories, 83 sites, 1,280 answers from one engine): no association was detected (T = 0.07, p = 0.51); the design could detect a T at least as large as 0.29. Not detected is not the same as none	RESULTS-wave0.md, PREREGISTRATION.md

Against hand audits. The CLI leaves the three checks that need off-site search or human judgement out of the denominator rather than guessing (and a fourth, p4.cn-engines, unless the site serves Chinese). Re-scoring the five published hand audits on the same pages, it matched the auditor's tier on 80% of 84 check pairs (92% where it reads a rule, 69% where it approximates a judgement). On the checks both scored it read 2.4 points lower on average (per site from 12 lower to 10 higher; mean absolute difference 8.8). Its full normalised score read 5.2 points lower on average, from 16 lower to 7 higher, because the hand audits also score p3.listings, p3.mentions and p4.question-coverage, which the CLI leaves out. n=5, all of them calibration sites: VALIDITY.md lists every disagreement.

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Quick start

One command

Python 3.8+, standard library only, nothing to install. It reads public URLs and prints a score against a published, versioned rubric — not a black box.

curl -sL https://raw.githubusercontent.com/jianruntech/geo-score/v1.8.3/cli/geo_score.py \
  | python3 - stripe.com --brief

  AIV READINESS  https://stripe.com
──────────────────────────────────────────────────────────────────────────
  72 / 100   Solid
  11 points to Leading

  Reachable 13/15
   ◐ Crawlers allowed in robots.txt   ███████████░░░░░░░  3/5
   ✓ Reachable to retrieval agents    ██████████████████  5/5
   ✓ Main content server-rendered     ██████████████████  5/5
  Understandable 17/22
   ◐ Sitemap discoverable and fresh   █████████░░░░░░░░░  2/4
   ✓ llms.txt present and structured  ██████████████████  5/5
   ✓ Organization + WebSite schema    ██████████████████  6/6
   ✗ BreadcrumbList on nested pages   ░░░░░░░░░░░░░░░░░░  0/3
   ✓ Page-type schema (Product, FAQ…) ██████████████████  4/4
  Content Citability 21/35
   ✓ Self-contained answer passages   ██████████████████  9/9
   ◐ Headings match how people ask    ████████░░░░░░░░░░  3/7
   ◐ Freshness signal present         █████████░░░░░░░░░  3/6
   ◐ Statistics carry a source        ████████░░░░░░░░░░  3/7
   ◐ Named, verifiable authorship     █████████░░░░░░░░░  3/6
  Brand Credibility 9/10
   ⊘ Third-party listings             ··················   —
   ⊘ Independent mentions             ··················   —
   ✓ Knowledge-graph entity           ██████████████████  4/4
   ✓ sameAs links resolve             ██████████████████  3/3
   ◐ Video and multimodal presence    ████████████░░░░░░  2/3
  Answer Fit 2/4
   ◐ Content shaped for extraction    █████████░░░░░░░░░  2/4
   ⊘ Covers the questions people ask  ··················   —
   ⊘ Chinese engine readiness         ··················   —
  ✓ full · ◐ partial · ✗ zero · ⊘ not measured (left the denominator)

  Biggest gaps
   +3 next tier (+3 to full)  Freshness signal present          needs: most pages do, and dateModified agrees with the visible date
   +3 next tier (+3 to full)  Named, verifiable authorship      needs: and the name links to a verifiable identity page
   +2 next tier (+2 to full)  Crawlers allowed in robots.txt    needs: mainstream retrieval user-agents explicitly allowed

  Scored 62 / 86 observable · 4 checks left the denominator · rubric v1.1 · took 91.5 s
  Needs off-site search or judgement: p3.listings, p3.mentions, p4.question-coverage
  Not observable this run: p4.cn-engines (not a Chinese-language site)

  Full rubric and what each tier means:
  https://github.com/jianruntech/geo-score/blob/v1.5.0/rubric/v1.1.md
  Score moved, or a check shows —? https://github.com/jianruntech/geo-score/blob/v1.5.0/guide/troubleshooting.md

geo_score.py stripe.com, recorded with geo-score 1.5.0 on 2026-10-03 through an HTTPS proxy (the footer's time includes it). For a run with 1.8.3, see Screenshots. From 1.7.0 the report also prints three observations under Outside the score after the checks (robots.txt rules page by page, links in the server HTML, hreflang), which never move the score; this recording predates them. See cli/README.md.

From PyPI: uvx or pipx

All three levels. Levels 2 and 3 need cli/geo_watch.py next to cli/geo_score.py, and the package on PyPI has both; a clone, or both files downloaded, works too. Neither line below needs git or a build step.

uvx [email protected] example.com            # run it without installing (needs uv)
pipx install geo-score==1.8.3              # or install the geo-score command
geo-score example.com --explain            # every check, with the evidence behind it

Without access to PyPI, install the same wheel from the release: pipx install https://github.com/jianruntech/geo-score/releases/download/v1.8.3/geo_score-1.8.3-py3-none-any.whl. From source (needs git): pipx install git+https://github.com/jianruntech/[email protected]. Standard library only, whichever you pick. Release downloads carry a SHA256SUMS file, and the wheel and sdist on PyPI match it byte for byte (how to check).

CLI — level 1 is one file with no dependencies, and every report records how long its run took (elapsed_s). Levels 2 and 3 add cli/geo_watch.py from the same release. With the package from PyPI, uvx [email protected] (or geo-score, after pipx install geo-score==1.8.3) takes the place of python3 cli/geo_score.py below, all three levels included.

python3 cli/geo_score.py example.com            # human-readable
python3 cli/geo_score.py example.com --explain  # with the evidence behind every check
python3 cli/geo_score.py example.com --json     # conforms to schema/report.v2.json
python3 cli/geo_score.py example.com --compare competitor.com   # side by side
python3 cli/geo_score.py example.com --badge aiv-badge.svg      # embeddable SVG
python3 cli/geo_score.py example.com --badge-json aiv-badge.json  # the same badge as shields.io endpoint JSON
python3 cli/geo_score.py example.com --share                    # one line to paste somewhere
python3 cli/geo_score.py diff before.json after.json             # what changed between two reports, check by check
python3 cli/geo_score.py example.com --baseline before.json --fail-on-drop   # CI: re-score the same pages, fail only on a drop

"Scored with geo-score": an optional badge for your own README. --badge aiv-badge.svg writes an SVG with your score and band. Commit it next to your README and link it to what the number means, this repository or the public leaderboard:

[![AIV readiness, scored with geo-score](aiv-badge.svg)](https://github.com/jianruntech/geo-score)

It is optional, and it changes nothing: a badge or a link back never moves a score, and never decides whether or where a site appears on the leaderboard. A site joins the leaderboard through the Add a site route, never in exchange for a link. A committed SVG shows the run that wrote it; --badge-json writes the same badge as shields.io endpoint JSON, so it can stay current without a new image (details).

Claude Code skill

The CLI measures what a static fetch can see. Three checks need off-site search or human judgement, and the skill does those too.

npx skills add jianruntech/geo-score     # add -g for a global install
# then: /geo-score audit https://example.com

npx skills add runs the open skills CLI. It finds one skill in this repository, geo-score, and copies the whole repository (about 9 MB) as it is on the default branch, main, into .claude/skills/geo-score for Claude Code. Without Node.js, or for the release rather than main, clone the release tag into your own skills folder instead: git clone --branch v1.8.3 --depth 1 https://github.com/jianruntech/geo-score ~/.claude/skills/geo-score.

How close the CLI comes without the skill: How accurate is it?

MCP server

geo-score-mcp is a local MCP server (stdio, Python standard library only). It gives an agent the same three levels as the CLI: score a site for free, ask the AI engines one question, and track a question set week over week. uvx [email protected] mcp runs it from PyPI (geo-score-mcp after a pipx install), and python3 cli/geo_score.py mcp runs it from a clone.

The buttons and the lines below run geo-score 1.8.3 from PyPI with uvx, which needs only uv: no git and no build step. They add no keys of their own, but a server started from a shell that exports a provider key (Claude Code passes its environment on) can use it. For a server that cannot spend, add --read-only after mcp.

Claude Code

claude mcp add --scope user geo-score -- uvx [email protected] mcp

Standard config (no keys in the file) for any client that reads an mcpServers file:

{
  "mcpServers": {
    "geo-score": {
      "command": "uvx",
      "args": ["[email protected]", "mcp"]
    }
  }
}

Claude Desktop does not read your shell's PATH: put the full path that which uvx prints in command.

Without access to PyPI, the release wheel: uvx --from https://github.com/jianruntech/geo-score/releases/download/v1.8.3/geo_score-1.8.3-py3-none-any.whl geo-score-mcp

From source (needs git): uvx --from git+https://github.com/jianruntech/[email protected] geo-score-mcp

Tool	Level	Cost	Writes	Hints	Args
score_site	1	Free	No	read-only, open-world	url, urls, sample
ask	2	Paid (your keys)	No	non-destructive, open-world	question, engines, brand, domains
run	3	Paid (your keys); dry_run is free	A run file under .geo-score/watch/runs/, next to the config	non-destructive, open-world	dry_run, engines, limit, max_calls, budget_usd
list_runs	3	Free	No	read-only, idempotent	—
report	3	Free	No	read-only, idempotent	run, since, format
diff	3	Free	No	read-only, idempotent	from, to, format
status	—	Free	No	read-only, idempotent	—

Resources: geo-score://rubric/v1.1.

Prompts: audit_site (url), check_citations (question, brand), weekly_watch, compare_runs (from, to), explain_check (check_id).

Bold arguments are required. Hints are the tools' MCP annotations: clients may use them to decide what to confirm, and they are hints, not guarantees. Only ask and run can spend money, and only with the provider keys you give the server.

Things to ask your agent:

Prompt	What it calls
Score https://acme.com for AI-search readiness and list the 3 checks with the most points to gain.	score_site with url
Dry-run my tracked questions and tell me the planned calls and caps.	run with dry_run: true (level 3, needs a config)
Compare my last two watch runs: which engines changed, and is any change outside the noise?	diff (defaults to the latest two runs)

Keys and config. score_site needs no key. ask and run read the provider keys (OPENAI_API_KEY, PERPLEXITY_API_KEY, GEMINI_API_KEY, ANTHROPIC_API_KEY, OPENROUTER_API_KEY) from the server's environment, which is not your terminal's: a desktop app does not see what you exported in a shell. The level 3 tools also need a geo-score-watch.json. Pass it as -c /absolute/path/geo-score-watch.json, because a GUI client does not start the server in your project. From 1.4.0, --read-only gives a server that cannot spend anything.

Setup for each client (Claude Code, Claude Desktop, Codex, Cursor, VS Code, Gemini CLI, Devin Desktop, Zed), the Claude Code plugin, timeouts, security and troubleshooting: guide/mcp.md.

GitHub Action

Level 1 on every push: score the site, and fail the build when it regresses. For level 3 on a schedule, see examples/ci/watch-weekly.yml.

- uses: jianruntech/geo-score@v1
  with:
    url: https://example.com
    fail-under: 40
    fail-on-gate: true

A gate at zero caps the score at 40: a site that would otherwise score 40 or more reads exactly 40 and passes fail-under: 40. fail-on-gate: true fails any capped site.

The job summary it writes is in Screenshots.

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Levels 2 and 3 · does AI actually cite you?

--ask and watch put the questions your buyers ask to ChatGPT, Perplexity, Gemini and Claude, through each provider's search-enabled API with your own keys, and record who the answers cite: you, your competitors, or the third-party pages (forums, review sites) the engines lean on instead. Keys are read from the environment; geo-score never writes them anywhere.

git clone --branch v1.8.3 --depth 1 https://github.com/jianruntech/geo-score && cd geo-score/cli
export OPENAI_API_KEY=… PERPLEXITY_API_KEY=…         # any subset of engines works
python3 geo_score.py acme.com --ask "invoicing app for freelancers"        # level 2
python3 geo_score.py watch init --brand Acme --domain acme.com --competitor "Rival=rival.com"
# put the questions your buyers ask an AI assistant into queries.csv, then:
python3 geo_score.py watch run --dry-run            # the plan, the caps, what a diff could detect and how precise one question's rate is; no calls, no cost
python3 geo_score.py watch run                      # level 3: every question on every engine, saved
python3 geo_score.py watch diff                     # this run against the last, with a significance test
python3 geo_score.py watch report --since 30        # the last 30 days pooled, one interval over questions

Level 2 asks each engine each question once and prints the result under the readiness report (and into the report's citation object with --json). One ask is an anecdote: use it to see what the engines say today, not to measure a rate.

geo-score watch · Lumo · run 20260926T083000Z · api channel

6 questions × 2 engines × 2 = 24 planned · 23 answered · 1 failed · 0 skipped

Cited in 38% of answers to questions that do not name Lumo (6 of 16, 4 questions, 95% CI 12–62%) · mentioned in 38%
Questions that name Lumo (2, kept out of the headline): cited in 100% (7 of 7, 95% CI 65–100%) · mentioned in 100%
Your site was cited somewhere for 5 of 6 questions.

By engine
  engine          model             cited         95% CI   mentioned  named first (tracked)  avg rank
  chatgpt-api     gpt-6-luna        9/12 75%      42–100%  75%        9/12                   1.0
  perplexity-api  sonar             4/11 36%      8–75%    36%        0/11                   2.0       1 failed
  gemini-api      gemini-3.8-flash  not measured                                                       no_key: set GEMINI_API_KEY or GOOGLE_API_KEY

These rows, and the tables below, count every question, the 2 that name Lumo included.

Answer share your pages support: not measured; this run was saved before citation positions were recorded.

Sources listed and used in the answer: not measured; this run was saved before listed sources were recorded.

Repeat consistency of cited domains (per engine; descriptive, not read by diff)
  engine          questions measured  pairs  median Jaccard
  chatgpt-api     6/6                 6      1.00
  perplexity-api  5/6                 5      1.00
  gemini-api      not measured                               no_key

Least consistent questions (up to 3 per engine):
  id   question                         engine          answers that cite  Jaccard
  q02  ai video tool with no watermark  chatgpt-api     2                  0.50
  q01  best free ai video generator     perplexity-api  2                  0.50

- Repeat consistency: for each question an engine answered more than once in this run, the overlap of the domains two of its answers cite, shared domains over all domains of the two (Jaccard), for every two answers, averaged per question; the median over questions per engine. 1: every ask cited the same domains; 0: no domain in common. Domains are read as everywhere in this report, www dropped; the order of citations is not compared.
- Answers that cite nothing are left out: two empty lists are not agreement, and one empty list is not a 0. On an engine, a question with fewer than two answers that cite is not measured. Lowest: up to 3 questions per engine whose answers did not all cite the same domains, lowest first.
- Descriptive only: no interval and no test, never part of the cited rate, and watch diff does not read it. It describes asks minutes apart in one run, which share that day's index; it is not computed over pooled runs.

Share of voice
              cited  95% CI  mentioned  named first (tracked)  sentence share
  Lumo (you)  57%    26–86%  57%        9/23                   100%
  Pixa        52%    33–71%  100%       14/23                  100%

- Named first (tracked): answers that name that brand before any other brand in your config, out of the answers that name at least one of them. Brands your config does not list are not seen, so first among the brands you track may not be first in the answer. Two brands first named at the same place, one name inside the other, are neither first.
- Sentence share: in the answers that name a brand, the mean share of their sentences that name it, every sentence counted the same. A sentence ends at a line break, at 。！？, or at . ! ? before a space.
- Both read the answer as saved, which keeps its first 20,000 characters; a name that first appears after that is not seen.

Sources the engines cite most (not yours)
  domain        answers  share  owner
  pixa.example  12       52%    Pixa
  reddit.com    11       48%

Questions where a competitor is cited and you are not (1)
  id   question                    cited instead
  q03  cheapest text to video app  Pixa

Who is named for each question
  id   question                         answers  Lumo (you)             Pixa               no tracked brand named  searched yes/no/unknown  most cited
  q01  best free ai video generator     4        cited 3/4 · named 3/4  cited 2 · named 4  0/4                     2/0/2                    Lumo (you)
  q02  ai video tool with no watermark  4        cited 1/4 · named 1/4  cited 2 · named 4  0/4                     2/0/2                    Pixa
  q03  cheapest text to video app       4        cited 0/4 · named 0/4  cited 2 · named 4  0/4                     2/0/2                    Pixa
  q04  lumo vs pixa                     4        cited 4/4 · named 4/4  cited 2 · named 4  0/4                     2/0/2                    Lumo (you)
  q05  pixa alternatives                4        cited 2/4 · named 2/4  cited 2 · named 4  0/4                     2/0/2                    —
  q06  鹿末视频免费吗                   3        cited 3/3 · named 3/3  cited 2 · named 3  0/3                     2/0/1                    —

- An answer given without a web search cannot cite anything.
- "No tracked brand named" counts answers that name none of the brands in your config; they may name others.
- Perplexity and OpenRouter search on every call or do not say, so their answers count as unknown under searched.

By question type
  type         cited     95% CI   mentioned
  alternative  2/4 50%   15–85%   50%
  list         4/8 50%   22–78%   50%
  pricing      3/7 43%   0–100%   43%
  vs           4/4 100%  51–100%  100%

What the engines searched for (from 12 answers that show it)
  times  search
  2      best free ai video generator
  2      ai video tool with no watermark
  2      cheapest text to video app
  2      lumo vs pixa
  2      pixa alternatives
  2      鹿末视频免费吗

- Of 12 searches: 0 contain a year, 6 contain a ranking or comparison word.

Ledger
24 questions asked · 23 API requests · 14,620 in / 4,860 out tokens · 23 searches · $0.07 + 12 answers with no price (add prices to geo-score-watch.json)
1 failed after 24 HTTP requests in all; a request that timed out may still be billed.
Caps: at most 100 questions.

Read this before quoting the numbers
- API channel. Answers come from each provider's search-enabled API, which is not the consumer app. Compare runs with runs; never pool them with answers sampled by hand in the apps.
- The same question gets different answers from one ask to the next, and answers to one question move together. Rates carry a 95% interval: Wilson when each question was answered once, a bootstrap over questions when a question has several answers. diff pairs the questions both runs answered and calls a change a change only when an exact paired test (McNemar, or a sign-flip test when a question has several answers), Holm-corrected across engines, says so (p < 0.05).
- A question that names the brand invites an answer that cites it. Those questions are kept out of the headline rate and reported on a line of their own.
- Engines without a key, calls that failed and calls skipped by a cap count as not measured, never as zero.
- This measures citations. It does not predict traffic, rankings or revenue.
- This run was saved before questions were flagged at run time. Questions that name the brand were found when this report was made, from the saved question text and the run's brand name, aliases and domains.

Level 3 keeps a fixed question list, saves every answer under .geo-score/watch/runs/ (schema), and reports:

	Meaning
cited	The answer links to a URL you own: one of your domains (subdomains included), or a url_prefixes entry such as your Amazon store or GitHub org
rank	Your position among the distinct domains the answer cites. Rank 1 means you were the first source
mentioned	The answer names you, one of your aliases or your domain. Chinese, Japanese and Korean names match anywhere; all other names match whole words only
share of voice	The same two rates for each competitor, over the same answers
sources	The third-party domains cited most often: the pages the engines trust in your category
answer share	How much of each answer your pages support (supported_share): by characters, by sentences and by position-weighted characters, read from where the provider says each citation sits. Printed beside the cited rate, never merged into it; an answer whose citations carry no position is not measured
sources listed and used	Perplexity and Gemini (generate_content) return sources beside the answer and mark the ones the text uses: sources listed and used per answer, used over listed, and the answers that list and use a URL of yours. APIs that return one list are not measured
repeat consistency	When a run asks a question more than once on an engine: how far the cited domains agree between asks (Jaccard, the median over questions per engine), and the least consistent questions. Descriptive only; watch diff does not read it
gaps	Questions where a competitor is cited and you are not, in any answer
searches	The searches the engine actually ran before answering, where the API exposes them (OpenAI, Gemini, Claude). This is the query fan-out, observed rather than guessed. The report counts how many contain a year or a ranking or comparison word, and says so when answers searched but the endpoint returned no query text
ledger	Questions asked, API requests, tokens, searches and cost. Every run keeps its own ledger

What it will not tell you.

• It is not the ChatGPT app. Answers come from each provider's API with web search switched on. The consumer apps use other models, prompts and personalisation. Compare API runs with API runs; never pool them with answers sampled by hand in the apps.
• Some surfaces are out of reach. Google AI Overviews and AI Mode, the AI summaries inside apps such as TikTok, Reddit or Xiaohongshu, an assistant's memory and account settings, location finer than the country hint, and which library a coding agent picks cannot be reached through these APIs.
• A citation is presence, not merit. A citation rate says an engine named and linked you, not that what it said is true or that the page deserved it; engines also cite misleading and self-promotional pages.
• One answer is an anecdote. Every rate carries a 95% interval (a bootstrap over questions when a question has several answers, since those answers move together), and diff calls something a change only when an exact paired test on the questions both runs answered, Holm-corrected across engines, says so (p < 0.05). Too few shared questions (under 6, or too few for the number of engines compared) is reported as too few to tell. The method: guide/watch-methodology.md.
• Questions that name you are kept apart. "acme vs rival" or "is acme worth it" put your name in the engine's search and are cited almost every time. watch flags them when it runs, keeps them out of the headline rate, and reports them on their own line with their own count. An optional branded column in queries.csv corrects the flag by hand.
• Not measured is not zero. An engine without a key, a failed call and a capped call are all reported as not measured, and none of them lowers your rate.
• Citations are not traffic. Nothing here predicts visits, rankings or revenue.

Engines. Pin the model you mean in the config and keep it fixed between runs; diff flags a run where the model changed. OpenRouter covers hundreds of models with one key.

Engine id (default)	Provider	Key	Default model	What counts as cited	Searches shown	Cost reported
chatgpt-api	OpenAI Responses API + web_search	OPENAI_API_KEY	gpt-6-luna	url_citation annotations	yes	no, set prices
perplexity-api	Perplexity Sonar	PERPLEXITY_API_KEY	sonar	numbered sources the answer uses	no	yes
gemini-api	Gemini Interactions API + google_search	GEMINI_API_KEY or GOOGLE_API_KEY	gemini-3.8-flash	url_citation annotations	yes	no, set prices
claude-api	Anthropic Messages + web_search tool	ANTHROPIC_API_KEY	claude-sonnet-5	citations on the answer text	yes	no, set prices
any id you choose	OpenRouter + web plugin (the model answering over OpenRouter's search, not the vendor's own)	OPENROUTER_API_KEY	openai/gpt-6-luna	url_citation annotations	no	yes

Caps and cost. max_calls is an exact cap on questions asked in a run, and a plan that exceeds it refuses to start. budget_usd is checked before every call against the spend so far plus the most expensive call seen on that engine, so it can be exceeded by at most one call per engine; with a budget set, engines whose cost cannot be known are left out unless you pass --allow-unpriced. Every run keeps a ledger of requests, tokens, searches and cost. Configuration, prices and all commands: cli/README.md.

From an agent. Levels 2 and 3 are also MCP tools, and an agent can only tighten your caps, never loosen them: see MCP server.

Every week. Citation rates move slowly and noisily: run on the same weekday with the same questions and models, and read diff, not single runs. examples/ci/watch-weekly.yml does it on a schedule with keys from repository secrets and commits each run, so the history lives in git. Run files contain your questions and the full answers: use a private repository if they are confidential.

Run it yourself, or have it run for you. Everything here is MIT; the tool has no paid edition. You pay your model providers directly and the ledger shows what each run used. What needs people rather than an API, Jianrun does as a service:

	Run it yourself (free)	Run by Jianrun
Channel	Provider APIs with search	APIs and the consumer apps, sampled by hand each week
Engines	OpenAI, Perplexity, Gemini, Anthropic, anything on OpenRouter	ChatGPT with search, Perplexity, Gemini, Google AI Overviews, Copilot; Chinese engines when you sell into China
Report	Text, Markdown, CSV, JSON	A weekly report with the AIV dashboard: citation trend, per-engine rates, facts AI gets wrong about you, readiness history
When citations drop	Out of scope	We do the fixing
Price	Your API bill	AEO delivery system, from US$7,790 per quarter (US$30,590 paid yearly), AIV dashboard included. See pricing

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Who it is for

Who	What they use it for	Where to start
Site owners and marketing teams	Whether AI engines can reach and quote the site, and which gaps are worth the most points	--brief, then --explain; level 2 for the questions that matter to you
Agencies and consultants	A diagnosis the client can re-check against a published, versioned rubric; the same pages re-scored after each change	--json, --baseline last.json, diff; name the rubric version in the report
Developers and CI	A failed build when a deploy blocks crawlers, drops server rendering or loses points	The GitHub Action, --fail-on-gate, --assert, SARIF and JUnit
Researchers	Every published number recomputed, the weights challenged, readiness tested against citations	rubric/v1.1.json, benchmark/, the preregistration

geo-score measures whether engines can cite a site and whether they do. It does not fix anything, and it never predicts traffic, rankings or revenue.

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Boundaries and governance

Area	Where it stands
Measurement, not remediation	No fix templates, rewriting or per-engine tactics, beyond two public-knowledge examples: Scope
Rubric changes	Public proposals, a comment period and an independent review; no certification sold: rubric governance
Weights	Each check declares what its weight rests on, with sources and the date they were checked: evidence table
Privacy	No telemetry. Level 1 fetches the site, what its markup and robots.txt point to, and Wikidata and Wikipedia search; levels 2 and 3 call only the providers whose keys you set
Stability	The report schema, check ids, CLI flags, exit codes, Action inputs and MCP tools are public contracts: stability policy

Scope — what this does not do

This is the part most tools leave out, so it's stated plainly.

AIV Score measures. It does not fix.

Not included	Why
Fix templates — robots.txt, JSON-LD blocks, llms.txt boilerplate, beyond two public-knowledge examples (a robots.txt that lets retrieval crawlers in, an Organization JSON-LD object)	Remediation is where the actual work and judgement live. It is a separate, non-open project
Content rewriting — how to shape a passage so it gets quoted	Same
Per-engine tactics — what to do differently for Perplexity vs Gemini	Same
A remediation roadmap	Same

Other honest limits:

• It measures input-side readiness, not outcomes. A high readiness score means engines can cite you. Whether they do depends on competition, query intent and factors no external audit can observe. Citation performance is reported as a separate, unscored block and never folded into the 100 — see Two scores. Measure it with levels 2 and 3 above. One reason it stays out: asked again, the same question gets a largely different set of sources. Repeated runs of a prompt within 24 hours, counting runs that cited something, had a mean source overlap (Jaccard) of 0.32 to 0.43 in each of four industries in one market with four engines pooled, and 0.23 to 0.51 engine by engine (Schulte et al. 2026, §5, Tables 4 and 6), and two runs of Google AI Overviews on the same device and location 0.66 (Grossman et al., SIGIR 2026, §4.4, Table 4).
• Brand Credibility and the named-author check need human judgement. "Is this a real identifiable person" and "is this mention independent" are not fully automatable. Treat those ~24 points as assisted, not automatic.
• Tiers reduce disagreement, they do not remove it. Every tier names a count out of the 8 sampled pages, so two auditors agree on the arithmetic. They can still disagree on whether a given paragraph is a self-contained answer. The settled ambiguities are the ones we found; there will be more.
• Heavily client-rendered sites score low, sometimes unfairly. If your content only appears after hydration, most checks will read the pre-hydration HTML — which is also roughly what a crawler sees, so the low score is usually right, but verify by hand.
• Engine behaviour moves. The rubric is versioned for exactly this reason. A score from an older rubric version is not comparable to a current one.

Why a rubric, not just a tool

A score you cannot audit is a number someone made up. So the specification is the product, and the tools are implementations of it:

• Versioned. Every score reports the rubric version. 71 (v1.1) is a claim; 71 is not.
• Tiered, with counts. Each tier names a page count out of 8, not "most".
• Evidence-bound. Every check requires an observation someone else can reproduce.
• Calibrated against public benchmarks, with the record published — including the four external sources the thresholds were checked against, and the eight specification ambiguities that real audits surfaced and v1.1 settled.
• Machine-readable. rubric/v1.1.json with stable check ids, and schema/report.v2.json so results from different implementations are comparable.

Implement it in your own stack, disagree with a weight, open a rubric proposal. That is the main thing we want contributions on. How a proposal becomes a new version, who decides, and why we sell no certification and pay for no seat on a standard: rubric governance.

Research behind the weights

The weights are opinionated but not invented. The two findings that most shaped them:

• Aggarwal et al., GEO: Generative Engine Optimization, KDD 2024 — in a simulated engine, rewriting one source at a time to cite sources, add statistics or quote experts raised that source's share of the answer by up to about 40% (Position-Adjusted Word Count, not citation count). The paper also found an authoritative tone produced no significant improvement, which is why this rubric scores structure and attribution, not voice. The gain does not carry over cleanly: when every source was rewritten at once, the source that had ranked first lost 20 to 30% of its visibility (§5.2, Table 2), and a later benchmark that measures citation rank found most published methods gave no significant gain (C-SEO Bench, arXiv 2506.11097 §6.2). The details are in the evidence table, row p2.sourced-stats (rubric/evidence-v1.1.md).
• llms.txt proposal, Answer.AI — the convention this rubric checks for in the Understandable pillar (p1.llms-txt).

Every check declares what its weight rests on (published research, a vendor's own documentation, our field observation, or a convention no engine has confirmed using), with sources and the date they were last verified: rubric/evidence-v1.1.md. Where the evidence is thin (p2.answer-passages, p2.question-intent, p1.llms-txt), the table says so. If you have evidence that a weight is wrong, open a rubric proposal — that is the main thing we want contributions on.

Related tools

Deliberately naming what this is not, so you can pick correctly:

Project	What it does	Relationship
llms-txt	The llms.txt specification itself	AIV checks for compliance with it
yao-geo-skills	21 categorized GEO skills, execution-oriented	Complementary — they do production, this does measurement
GEOFlow	Full GEO operations system for company sites	Much larger scope; AGPL

If you need remediation and not just a score, those projects overlap with the part this repo deliberately excludes.

Stability, privacy and security

Install options are in Quick start.

• Stability: the rubric and the tool are versioned separately, and the report schema, check ids, CLI flags, exit codes, Action inputs and MCP tools are public contracts. What may change in which release: the stability policy.
• No telemetry. geo-score sends nothing to us or anyone else. Level 1 fetches the site you name (following its redirects), the URLs its markup and robots.txt point to (sameAs profiles, logo, sitemap), and Wikidata and Wikipedia search; levels 2 and 3 call only the AI providers whose keys you set.
• Security: text quoted from the audited site is fenced as data in every report, the MCP server's score_site connects only to public addresses, and keys never reach a file. Threat model and reporting: security policy.

Why did my score move 4 points? Each run samples up to 8 pages, and the sample changes; in a same-day retest with 1.8.0, 99% of sites stayed within ±5 (REPRODUCIBILITY.md). To compare before and after a change, re-score the same pages with --baseline last.json, which also prints what changed, check by check (--urls-from last.json re-scores them without the comparison). Why does a check show —? It was not measured, so it left the denominator instead of scoring 0. More answers: troubleshooting. Every term the README, the reports and the guides use is defined in the glossary; every flag, exit code and watch command is in the CLI reference (简体中文).

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For developers

Repository layout

Path	What is there
cli/	geo_score.py (level 1: one file, no dependencies), geo_watch.py (levels 2 and 3), their tests and the CLI reference
rubric/	The rubric (v1.1 and its JSON), the evidence behind each weight, calibration, band analysis and open questions
benchmark/	The 324-site leaderboard: results, one raw report per site, the preregistration, reproducibility and validity records
docs/	The GitHub Pages copy of the leaderboard, llms.txt and the badge at the top of this page
guide/	MCP setup, troubleshooting, the glossary, watch methodology, the stability policy, two public examples, and how to check whether ChatGPT can read a site
reference/	Background the checks rely on: known AI crawlers, and which sources each platform draws from

Tests and the gate

bash scripts/check.sh           # the gate, offline: structure, lint, every test suite, the Action, packaging
bash scripts/check.sh --live    # before a release: also example.com and the MCP Inspector

scripts/check.sh is the CI: nothing merges unless it passes. The rules it enforces, and the check behind each one, are in AGENTS.md. Several figures on this page are among them: when one no longer matches the data it quotes, the gate fails.

Contributing

The most valuable contribution is evidence about the weights. See the contributing guide. Which issue form takes a wrong score, a disputed leaderboard row or a tool that does not work: getting help.

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License, citation and maintainers

License

MIT

Citation

If you reference the rubric in research or a report, see CITATION.cff.

Who maintains this

Built and maintained by Jianrun Tech (见润科技), Shenzhen. We run GEO and AI-adoption programs for companies in China and abroad: B2B exporters, software companies, professional services, local businesses, education providers, and companies going global, cross-border e-commerce included. The rubric came out of client work and out of optimizing our own products; publishing it is how we'd like AI visibility to be measured consistently, including by people who never become our clients.

Commercial use of this repository is unrestricted under MIT — including inside paid consulting work. You do not need our permission, and there is no separate commercial licence.

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Documentation

Document	English	Chinese
This README	README.md	简体中文
The rubric, v1.1	rubric/v1.1.md	简体中文
Evidence behind each weight	rubric/evidence-v1.1.md	简体中文
Rubric governance	rubric/governance.md	简体中文
CLI reference	cli/README.md	简体中文
MCP setup for every client	guide/mcp.md	简体中文
Troubleshooting	guide/troubleshooting.md