RISK
Invisible in answer engines
A page can rank and still miss AI answers. Weak structure and weak sources cause that.
Linting your website for AI answers
Lint your website before AI ignores it.
AnswerLint audits whether ChatGPT, Gemini, Perplexity, and AI Overviews can understand, trust, and cite your pages.
Instant validation
npx answerlint@latest audit --url https://website.comImmediate path
For executives: see AI visibility risk in one dashboard.
Start with commercial impact, competitor exposure, and the pages most likely to lose AI citation share.
✓Prioritize revenue and trust pages first.
✓Explain whether AI systems can quote the page.
✓Turn audit gaps into a short fix plan.
Evidence workspace
One audit surface for answers and citations
LIVE AUDIT
live CLI simulation
$ npx answerlint@latest audit --url https://website.com
fetch sitemap.xml discovered 42 urls
crawl selected /pricing, /docs, /compare for audit
pass robots and canonical signals detected
pass answer-first headings found
warn FAQPage schema missing on /compare
warn dateModified missing on /docs
score composite 82 aeo 88 geo 76
fail GEO threshold 90 not met
Process exited with code 1 (Threshold failed)
Composite
82
+12 vs floor
AEO
88
answer ready
GEO
76
trust gaps
best tools for AI visibility audits
/compare/ai-visibility
PASSING91AEO checklist for launch pages
/docs/aeo-checklist
FIX QUEUED64GEO audit in CI pipeline
/guides/ci
AT RISK44Why now
Why teams need a stricter visibility layer
AI discovery does not reward pages just because they exist. It rewards pages that answer cleanly, cite clearly, and expose enough trust for a model to use them.
AEO helps you become the answer. GEO helps AI systems treat your page as a reliable source. AnswerLint audits both so the team can see the whole risk profile.
Think ESLint for AI-ready content: one repeatable score, one evidence trail, and one release bar for content, SEO, and engineering.
Standard SEO result
10 blue links for "AI visibility audit"
Ranking still matters, but the click path is increasingly hidden behind generated answers.
AI citation layer
+31 cite fit"AnswerLint identifies missing FAQ schema, freshness metadata, and citation gaps before launch."
Answer blockTrusted sourceRelease gate
RISK
Teams read different signals
Leaders want risk and impact. Devs want clear markup fixes. Many audits speak to only one group.
RISK
Competitive gaps stay hidden
Compare side by side. Then you know if a low score is OK—or if a rival wins AI cites.
If answer visibility matters, it needs a release standard.
Who it is for
One story for revenue owners and build teams.
Say why this helps the business. Say how fast the team can act.
Evidence workspace
For business leaders
Lead with reach, trust, and rival risk. Say what the score means. Say how it hits demand.
01Explain whether the page is likely to be quoted or skipped.
02Call out the commercial risk of weak trust and freshness signals.
03Summarize the top 3 changes that most improve discoverability.
Executive Summary
Citation share is recoverable.
The page answers the core buyer question, but weak source attribution and missing schema make it easier for competitors to be cited in AI summaries.
Composite82
AEO88
GEO76
Risk
Schema gap
Risk
Freshness weak
Risk
Citations thin
Readable outputs
HTML · JSON · CSV
Inputs covered
URL · file · folder · sitemap
Release gates
Thresholds & exit codes
Built for
Business · SEO · Engineering
Product layer
An enterprise-grade audit loop inside an open-source CLI.
AnswerLint is designed for teams that want AI visibility measurement to live beside their normal shipping workflow.
Operating principle
Executive risk framing
Operating principle
Evidence per failed check
Operating principle
Open-source, CI-friendly, local-first
Reports that fit the audience
HTML for stakeholders, JSON for pipelines, CSV for sitemap or folder runs.
Project configuration
Optional .answerlint.json in the repo or home directory; override path per run.
CI you can trust
Exit codes for pass, score failure, network errors, and bad input—mirror how you run Lighthouse in CI.
Operational safety
Read-only fetches, configurable rate limits, and robots awareness for compliant crawling.
Overview command
AnswerLint overview surfaces capabilities, outputs, and quick-start hints in the terminal.
Competitor analysis
Compare one live URL against a competitor URL to see score deltas, parity gaps, and first fixes.
Report intelligence
A report that executives can read and builders can execute.
The strongest audit experience explains commercial risk first, then backs it with exact evidence and implementation details.
Audience 01
What business users should see first
Answer readiness is promising, but the page is under-signaling trust. Improve freshness, citation quality, and structured guidance to make it more quotable.
IMPACT 01
A short summary of whether this page is likely to earn trust and citations.
IMPACT 02
A simple statement of competitive position: behind, equal, or ahead.
IMPACT 03
Three highest-value actions tied to visibility impact, not technical jargon.
Sample output
Readable summary with technical depth underneath
Composite
74
AEO
81
GEO
68
Audience 02
What developers should see immediately after
DEV-1Audit evidence for each failed check, including the exact missing signal.
DEV-2Suggested schema, content structure, citation, and metadata improvements.
DEV-3Machine-readable outputs for CI gates, diffing, and automation.
CI command
answerlint audit --url "$DEPLOY_URL" --ci --threshold 9001 / commit
02 / audit
03 / block if GEO < 90
Highest-value fixes
#SEO-124schemaapp/content/page.tsx
Publish stronger authorship and freshness metadata.
#SEO-125citationscontent/sources.md
Expand support links to trusted external sources.
#SEO-126FAQPagecomponents/faq-jsonld.tsx
Turn core sections into answer-first blocks and FAQ markup.
Ship workflow
Run the same inspection locally, in preview, and in CI.
Start with the playground, then move the workflow into the repo so every important page ships with an answer-visibility bar.
answerlint overview
.github/workflows/answerlint.yml
name: AnswerLint audit
on:
pull_request:
push:
branches: [master]
jobs:
ai-visibility-gate:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npx answerlint@latest audit --url "$DEPLOY_URL" --ci --threshold 90CI/CD release gate
01
Commit
content change
02
GitHub Action
preview URL
03
AnswerLint runs
AEO + GEO gate
04
PR Blocked
GEO score < 90
Run the same checks locally, in CI, or across a sitemap.
Share a readable report with non-technical stakeholders without hiding the underlying evidence.
Use the playground for fast exploration, then move the workflow into your repo.
Audit path
How AnswerLint works
From a URL or file to a report you can share in CI or a browser.
- 01
Choose input
Point the CLI at a URL, a Markdown or HTML file, a folder, or a sitemap. It honors rate limits and robots.txt unless you override on your own site.
- 02
Run the audit
Checks run on your content. Failed checks explain why. Writers and devs see what to fix.
- 03
Get scores
You get combined, AEO, and GEO scores. Add .answerlint.json for project defaults if you want.
- 04
Export & gate
Print HTML for people, JSON for scripts, or CSV for batches. Use --ci and --threshold to fail builds when scores drop.
Developer workflow
How do teams use AnswerLint with GitHub, Vercel, and HTTPS pages?
These are the practical workflows teams care about when they move from one-off checks to repeatable release habits.
How do developers audit HTTPS pages before release?
Teams run AnswerLint against production-like HTTPS URLs so they can review answer readiness, trust signals, and citation gaps before a page ships.
How do teams use AnswerLint with GitHub and CI?
Teams export HTML and JSON reports into GitHub workflows, compare score changes across releases, and block merges when the agreed bar drops.
What type of report fits Vercel previews and production sites?
HTML works for human review, JSON fits automation, and CSV helps content ops benchmark many URLs or compare a launch page against a competitor.
Comparison & alternatives
AnswerLint vs manual review vs generic AI prompts
Should you audit by hand? Use a chat model? Or run an answer-readiness linter on each release? This lines up three paths.
Deterministic release tool
Opinionated pickAnswerLint
Repeatable scoring
AEO, GEO, and combined scores you can export.
Workflow fit
Runs local, in CI, and in batch reviews.
Evidence and outputs
Returns HTML, JSON, and CSV with clear findings.
Best use case
Teams that need one list of what to fix next.
Human review
Manual review
Repeatable scoring
Often subjective. Scores drift by reviewer.
Workflow fit
Hard to scale across many pages or ships.
Evidence and outputs
Proof often lives in notes or sheets.
Best use case
Small one-off reviews or editorial spot checks.
Prompted summary
Generic AI prompt
Repeatable scoring
It can sum things up. It does not give a steady audit line.
Workflow fit
Fine for ideas. Weak for rules and release gates.
Evidence and outputs
Quality depends on the prompt. It is hard to compare runs.
Best use case
Early research or narrative drafting.
Concepts
AEO & GEO in plain language
Both aim for visibility when answers are generated, not just ten blue links. AnswerLint checks your content with that lens.
AEO — Answer Engine Optimization
AEO is for engines that pull short answers. Think snippets, voice, and Q&A blocks. Use clear headings and facts. That helps you get cited.
AnswerLint shows gaps in clarity and structure. It shows what models need to quote you.
GEO — Generative Engine Optimization
GEO is for AI that reads many pages at once. Use one brand name. Add facts you can trace. Use clean HTML. That cuts wrong AI facts and keeps your brand right.
AnswerLint scores GEO with AEO. You see combined risk, not one vanity score.
AI visibility map
Show the market how visibility moves from ranking to being cited.
Compatibility
Runs anywhere you run Node—local, CI, or container.
- Node.js 18+
- npm / npx
- GitHub Actions, GitLab CI, etc.
- macOS · Linux · Windows
FAQ
What do teams usually ask before they adopt AnswerLint?
Content, SEO, and devs ask these when they pick AI visibility tools.
What does AnswerLint help content teams measure?
It checks if a page can be the answer and the source. One report scores clarity, structure, trust, dates, and cite fit.
How does AnswerLint compare with manual reviews?
Manual review can spark ideas. It is slow and uneven. AnswerLint gives a steady audit, a score you can track, and fixes for writers, SEO, and devs.
Can developers use AnswerLint in CI and automation?
Yes. Devs can run it on URLs, files, folders, and sitemaps. They can export HTML, JSON, or CSV. They can set CI floors to block ships when scores slip.
Why do AEO and GEO both matter for B2B teams?
AEO helps when buyers ask plain questions. GEO helps when AI picks which sites to trust. B2B teams need both to show up in AI-led discovery.
What people say
Real quotes from users will appear here as they come in.
Reviews coming soon
We are collecting feedback from teams using AnswerLint. Check back, or star the repo to follow releases.
Author
Rakesh Cheekatimala builds AnswerLint, an AI-visibility linting system for modern content teams.
npm package updated
Give every launch page a sharper AI answer-readiness review.
Use the playground for quick exploration, then move the same workflow into the CLI and CI. Reports stay local and under your control.
Fast first scanClear executive readoutDeveloper-grade evidence
Install
npx answerlint@latest audit --url https://website.com