에이전트 강화
게시자의 자체 명령입니다. PlugMCP 엔드포인트가 아닙니다.
Host a copy with our keys and logs.
npx -y @iliasabk/[email protected]Claude Code에는 로컬 명령용 설치 링크가 없습니다. 아래 명령을 복사해 터미널에서 실행합니다.
claude mcp add io.github.iliasabk-geolint -- npx -y @iliasabk/geolint@0.4.0터미널에서 실행합니다.
{ "mcpServers": { "io.github.iliasabk-geolint": { "command": "npx", "args": [ "-y", "@iliasabk/[email protected]" ] } }}claude_desktop_config.json
원격 URL인가요, 내 컴퓨터에서 실행하는 명령인가요? 두 설치 방식의 차이
PlugMCP에서 실행
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
실행게시자 제공
No install, no config:
npx @iliasabk/geolint check yoursite.com
geolint fetches the page, its robots.txt and llms.txt, evaluates 51 known AI crawler tokens against your robots.txt, runs 52 audit rules, and prints a scored report with a concrete fix for every finding.
ESLint for AI search: audits AI-crawler access, llms.txt, schema and citability.
Disallow: /, a noindex left over from staging, a client-rendered page that
looks empty to a bot that doesn't run JavaScript.52 rules across 5 categories — geolint rules lists them all, and
docs/rules.md documents what each rule checks, why it matters
and how to fix violations.
Real output, auditing the bundled demo site (examples/demo-site, which
deliberately blocks two bots) — trimmed for width:
$ geolint check localhost:4173 --ignore technical/https
geolint v0.2.1 — AI-search readiness
http://localhost:4173/
200 OK · text/html · TTFB 113ms · robots 200 · llms.txt 404
██████████████████████████░░░░ 86/100 Grade B
CATEGORIES
AI Crawler Access ███████░░░ 70 ✗ 2 errors
llms.txt █████████░ 92 ⚠ 1 warning · 1 hint
Structured Data █████████░ 88 ⚠ 1 warning · 3 hints
Citability ████████░░ 82 ⚠ 2 warnings · 3 hints
Technical Foundation ██████████ 100 ✓ clean
AI CRAWLER ACCESS — 49/51 allowed · 2 blocked
OpenAI
GPTBot ✓ training
OAI-SearchBot ✓ search
ChatGPT-User ✓ user-fetch
Perplexity
PerplexityBot ✗ search
Perplexity-User ✓ user-fetch
Google
Googlebot ✓ search
Google-Extended ✓ training
… 51 tokens total, grouped by vendor …
FINDINGS
AI Crawler Access
✗ ai-crawler/search-bots-blocked PerplexityBot is blocked by robots.txt — Perplexity cannot use your pages as AI answer sources
fix: Remove the Disallow covering PerplexityBot in robots.txt, or add an explicit "Allow: /" for it.
evidence: Disallow: / (matched by PerplexityBot)
llms.txt
⚠ llms-txt/missing No llms.txt found
fix: Create /llms.txt at the site root: an H1 title, a short blockquote summary, and ## sections linking to your key content.
evidence: http://localhost:4173/llms.txt → HTTP 404
────────────────────────────────────────────────────────────────────
2 errors · 4 warnings · 7 hints · 32/44 checks passed
Every finding carries a rule id, a severity, the evidence geolint matched, and a fix. Compare two pages or two competitors head-to-head:
geolint check a.com --compare b.com
Full flag reference: docs/configuration.md.
- uses: iliasabk/geolint@v1
id: geolint
with:
url: https://example.com
fail-under: 80
- uses: github/codeql-action/upload-sarif@v3
if: always()
with:
sarif_file: ${{ steps.geolint.outputs.sarif-file }}
The action produces score/grade step outputs, a SARIF report for GitHub code scanning, and a markdown report for job summaries and PR comments. Full recipes — SARIF upload, updating a single PR comment, baseline drift detection — in docs/github-action.md.
npx @iliasabk/geolint check https://example.com --fail-under 80
Exit code is 1 when the score drops below the gate (or findings regress
against --baseline), 0 otherwise — works in GitLab CI, CircleCI, npm
scripts, pre-deploy hooks.
npx @iliasabk/geolint check https://example.com --badge
# → writes geolint-badge.svg + prints the markdown snippet to paste
Commit the SVG, or regenerate a shields endpoint JSON in CI
(--badge-endpoint) for a badge that never goes stale.
-f pretty (default) renders the terminal report above. The machine formats:
-f json — the full ScanReport: findings, per-category scores, bot access matrix-f sarif — SARIF 2.1.0, upload straight to GitHub code scanning-f markdown — PR-comment/job-summary-ready tables-f html — a self-contained interactive report (score ring, findings filter,
bot matrix) you can share or host anywhereAdd -o report.json to write to a file; stdout stays clean for piping.
The repo dogfoods itself: a nightly workflow re-audits eight
well-known sites and commits the scores back, and the showcase
site publishes the full interactive
reports — github.com, anthropic.com, stripe.com and more, regenerated on every
push to main.
import { scan } from '@iliasabk/geolint';
const report = await scan('https://example.com', {
ignore: ['technical/https'],
timeout: 10_000,
});
console.log(report.score, report.grade); // e.g. 86 'B'
for (const f of report.findings) {
console.log(f.severity, f.ruleId, f.message, f.fix);
}
scan(url, options) returns a typed ScanReport. Also exported: the bot
registry (AI_BOTS, botsByPurpose), the rule registry (allRules,
ruleById), robots.txt/llms.txt parsers, badge generators, scorers and all
four reporters.
geolint mcp speaks the Model Context Protocol
over stdio — Claude Desktop, Cursor, VS Code and Windsurf can audit sites,
generate llms.txt and compare URLs as native tools:
// claude_desktop_config.json / ~/.cursor/mcp.json
{
"mcpServers": {
"geolint": {
"command": "npx",
"args": ["-y", "@iliasabk/geolint", "mcp"]
}
}
}
Five tools: audit_url, generate_llms_txt, compare_urls, list_rules,
list_ai_bots — all read-only, with structured output and per-call timeouts.
Setup for every client: docs/mcp.md.
geolint bots lists 51 AI crawler tokens with a purpose-aware impact
assessment — because "should I block this bot?" has a different answer for each:
And two nuances other tools miss:
ai-crawler/user-fetch-bypass
tells you when a Disallow won't work — enforce at the WAF/auth layer instead.anthropic-ai, Claude-Web, FacebookBot are retired.
ai-crawler/stale-tokens flags them and names the replacement token — a
User-agent: anthropic-ai rule does nothing today.Control-only tokens like Google-Extended and Applebot-Extended never fetch
at all — they only set a preference — and geolint treats them accordingly.
llms-txt/* findings are weighted as warnings and hints,
not errors. geolint still checks it (and geolint init generates it) because
adoption is growing and the cost is one file.info
severity and geolint says so.geolint bots and the list_ai_bots MCP tool expose.Details and the reasoning behind each column: docs/comparison.md. geolint also ships an MCP server, a score badge and regression baselines.
Planned for v0.4+:
geolint watch — re-audit on deploys/file changes@type validators)Issues and PRs welcome — see CONTRIBUTING.md. New rules are
the best contribution: each needs a check(ctx), findings with fix, a test
and a docs entry.
ESLint for AI search: audits AI-crawler access, llms.txt, schema and citability.
{ "mcpServers": { "io.github.iliasabk-geolint": { "command": "npx", "args": [ "-y", "@iliasabk/[email protected]" ] } }}도구
아직 도구 목록이 없습니다. 원격 서버는 첫 상태 확인 후에 도구 목록이 생깁니다.
FAQ
이 페이지에서 Cursor, Claude Desktop, Claude Code 또는 VS Code용 stdio 설정을 복사합니다. PlugMCP 프록시가 아니라 게시자의 설치 방식을 사용합니다.
이 레코드에는 필수 환경 변수가 등록되어 있지 않습니다.
Geolint은(는) stdio를 통해 로컬에서 실행됩니다.
마지막 확인: 2026-11-09. ESLint for AI search: audits AI-crawler access, llms.txt, schema and citability.