LLMScout

Install•Quickstart•CLI reference•Comparison•FAQ•Contributing

npm install -g llmscout-cli llmscout init ./my-site --site-url https://example.com llmscout check ./my-site

[!NOTE] The npm install pulls inundicias a transitive dependency ofcheerio.npm audithas flagged a high-severity advisory against it in the past;package.json'soverridesblock pinsundici(andjs-yaml/nanoid) to patched ranges to close it. Runnpm auditafter install to confirm your resolved tree is clean.

pip install llmscout-cli llmscout init ./my-site --site-url https://example.com llmscout check ./my-site

Zero runtime dependencies -- HTML parsing and HTTP fetching both use only the Python standard library. Seepython/README.mdfor the full Python-specific guide.

llmscout init ./my-site --site-url https://example.com llmscout check ./my-site

LLMScout check -- https://example.com [PASS] (technical) Title tag Title "Example Domain" is 14 characters, within the recommended 10-60 range. [WARN] (technical) Meta description No meta description found. Fix: Add <meta name="description" content="..."> with 50-160 characters summarizing the page. [WARN] (technical) Canonical tag No <link rel="canonical"> tag found. Fix: Add a canonical link tag pointing at the preferred URL for this page. [FAIL] (technical) robots.txt robots.txt was not reachable at https://example.com/robots.txt (HTTP 404). Fix: Add a robots.txt file at your site root, even a permissive one, so crawlers and agents have explicit directives. [WARN] (technical) sitemap.xml No sitemap was reachable (tried: https://example.com/sitemap.xml). Fix: Add a sitemap.xml at your site root, or point to one with a Sitemap: directive in robots.txt, to help search engines discover pages. [PASS] (technical) Heading structure Exactly one <h1> and no skipped heading levels detected. [PASS] (technical) Image alt coverage No  tags found on the page. [WARN] (technical) Open Graph tags No Open Graph tags found. Fix: Add Open Graph meta tags (og:title, og:description, og:image, og:url) so shared links render rich previews on social platforms. [WARN] (technical) Twitter/X Card tags No twitter:card meta tag found. Fix: Add <meta name="twitter:card" content="summary_large_image"> (or another valid card type) so links render rich previews on X/Twitter. [WARN] (technical) Meta robots directives No meta robots directives found; default Google Search snippet/preview limits will apply. Fix: Add <meta name="robots" content="max-snippet:-1, max-image-preview:large, max-video-preview:-1"> to control search snippet appearance. [PASS] (technical) Image weight No  tags with an http(s) src to measure. [PASS] (technical) Redirect chain The homepage resolved with no redirects. [WARN] (geo) Structured data (JSON-LD) No JSON-LD structured data found. Fix: Add schema.org JSON-LD markup (e.g. Organization, WebSite, or Article) so generative engines can understand the page's entities. [WARN] (geo) llms.txt No llms.txt found at https://example.com/llms.txt. Fix: Optional: add an llms.txt at your site root summarizing the site for LLM-based agents (see llmstxt.org). [WARN] (geo) AI crawler directives robots.txt is unreachable, so AI-crawler directives could not be determined. Fix: Add a reachable robots.txt if you want to state an explicit policy for AI crawlers (GPTBot, OAI-SearchBot, ClaudeBot, Claude-SearchBot, PerplexityBot, Google-Extended, Applebot-Extended). [WARN] (geo) FAQ schema No FAQPage structured data found. Fix: If this page has an FAQ section, mark it up with FAQPage JSON-LD so generative engines can surface individual answers. [PASS] (geo) Content extraction friendliness Found 1 heading(s) and 1 structured text block(s); content appears reasonably extractable. (Heuristic: cannot assess semantic quality or JS-rendered content.) [WARN] (geo) Speakable schema No Speakable structured data found. Fix: If this page has content suited for voice assistants, add a "speakable" SpeakableSpecification to its JSON-LD so voice search can surface it. [WARN] (geo) Organization schema No Organization/Corporation/LocalBusiness/Person structured data found. Fix: Add Organization (or Person) JSON-LD with a sameAs array of your official social/profile URLs to strengthen Knowledge Panel signals. [WARN] (geo) Markdown content negotiation Requesting https://example.com/ with "Accept: text/markdown" returned Content-Type "text/html" instead of text/markdown. Fix: Optional: serve a text/markdown representation of pages when the client sends "Accept: text/markdown" so LLM-based agents can fetch clean Markdown directly instead of parsing HTML. [WARN] (geo) Link header (RFC 8288) The homepage does not send a Link response header. Fix: Optional: add an RFC 8288 Link response header (e.g. <https://example.com/feed>; rel="alternate") to advertise machine-readable service-discovery endpoints to crawlers and AI agents. Summary: 6 PASS, 14 WARN, 1 FAIL (21 checks)

{ "siteUrl": "https://example.com", "summary": { "pass": 6, "warn": 14, "fail": 1, "total": 21 }, "results": [ { "id": "title", "name": "Title tag", "category": "technical", "status": "PASS", "message": "Title \"Example Domain\" is 14 characters, within the recommended 10-60 range." } ] }

checkexits0when no check FAILs,1when at least one check FAILs (WARN alone does not fail the run), and2on a usage error such as a missing or misconfiguredllmscout.json.

- 21 checks across two categories.12 technical-SEO checks and 9 GEO checks, listed by name inThe 21 checks.
- Zero external toolchain, either language.child_processis never imported anywhere in the TypeScript source; the Python port has zero runtime dependencies. Checks run inside the process instead of shelling out to Python scripts or a headless browser.
- Cross-platform by construction.Nopython3-versus-py -3shelling and no relative-path script resolution, so the same install runs identically on Windows, macOS, and Linux.
- Hardened fetch.The single fetch wrapper (src/fetch-utils.ts) rejects any non-http(s)scheme, blocks loopback/private/link-local hosts, follows redirects manually one hop at a time, and bounds the chain at 5 hops and the response body at 10 MiB.
- Fleet mode with per-site reports.llmscout fleet manifest.jsonruns the full suite across many local client-repo paths in one invocation, and--out-dirwrites one auto-named report file per site -- built for agencies checking many client sites at once.
- Structured output.Every command accepts a global--jsonflag for machine-readable output, so an agent invoking the CLI can parse results programmatically.
- A real, configurable User-Agent.Sends a genuine browser User-Agent by default (some SSR frameworks and CDNs reject bot-style strings outright) and a--user-agentflag to override it.
- Well tested.248 TypeScript tests and 233 Python tests, both reproducible locally withnpm test/npm run test:coverageandpytest. The Python distribution has zero runtime dependencies, so there is nothing for a dependency audit to flag. On the npm side,npm auditcurrently reports one high-severity advisory inundici, a transitive dependency pulled in bycheerio, not in this project's own code.

- Google's own AI Overviews are already cutting click-through.Ahrefs measured position-1 CTR on AI-Overview-triggering keywords fall from 7.3% (December 2023) to 1.6% (December 2025) -- a 58% average CTR reduction across the study. (Ahrefs, December 2025) Semrush's independent 10-million-keyword analysis found organic CTR drops 61% and paid CTR drops 68% when an AI Overview appears on the results page. (Semrush)
- ChatGPT itself is now a real, measurable traffic source.Search Engine Land's analysis of 6.77 million sessions found ChatGPT accounts for 92% of all AI-assistant referral traffic, converting at 7.1% -- close to paid search's 7.8%. (Search Engine Land)
- AI crawlers are not one crawler anymore.Between May 2024 and May 2025, GPTBot's share of AI-crawler traffic rose from 5% to 30%. OpenAI and Anthropic have since split their bots into training crawlers (GPTBot, ClaudeBot) and separate, independently blockable search/retrieval crawlers (OAI-SearchBot, Claude-SearchBot). (Cloudflare Radar, "From Googlebot to GPTBot") LLMScout'sai-crawler-directivescheck reports on all seven of the current major training and search bots (GPTBot, OAI-SearchBot, ClaudeBot, Claude-SearchBot, PerplexityBot, Google-Extended, Applebot-Extended) separately, since blocking a company's training bot has no effect on whether its assistant can still retrieve and cite your page live through its own search bot.
- Markdown-native delivery is a real, emerging practice, not a fad.Cloudflare documents HTTP content negotiation (Accept: text/markdown) as a standards-based way to serve agents a lighter, cleaner representation of a page -- their own benchmark saw an 80% token reduction on one blog post. (Cloudflare, "Markdown for Agents") Worth being honest about the current state: independent analysis across 300,000 domains found that in practice, no major AI crawler currently sends theAccept: text/markdownheader to actually negotiate it yet -- they discover Markdown only via direct links. (Dries Buytaert) LLMScout'smarkdown-negotiationcheck reports on this without pretending the ecosystem is further along than it is.
- Not every signal in this space is settled, and LLMScout does not pretend otherwise.llms.txtis a real, community-driven convention (created September 2024, adopted by roughly 8-10% of top sites as of mid-2026, including Anthropic, Stripe, Cloudflare, and Vercel) -- but Google's own Gary Illyes has stated publicly that Google does not support it and has no plans to, comparing it to the deprecatedkeywordsmeta tag. (Search Engine Journal) LLMScout'sllms-txtcheck reports its presence as informational, never as a required pass -- the tool's job is to report what is actually configured, not to prescribe a policy the evidence doesn't yet support.
- Google removed FAQ rich results from Search entirely on May 7, 2026.(Search Engine Journal)FAQPageschema itself is not deprecated -- it remains valid markup other engines and AI assistants can still parse for direct-answer extraction -- so LLMScout'sfaq-schemacheck still reports on it, just without implying it earns a Google SERP rich result anymore.

$ llmscout --help Usage: llmscout [options] [command] Zero-config, cross-platform SEO and GEO checks for local projects, with no Python or headless-browser dependency. Options: -V, --version output the version number --json output structured JSON instead of human-readable text (default: false) --user-agent <string> override the default User-Agent header sent on outbound fetches -h, --help display help for command Commands: init [options] <path> Scaffold a LLMScout setup (llmscout.json + a Claude Code skill file) into a target directory check [options] <path> Run SEO/GEO checks against a local project's configured site fleet [options] <config.json> Run the full check suite against every site listed in a fleet manifest help [command] display help for command

--json,--user-agent,-V/--version, and-h/--helpare the only global options.

LLMScout ships aModel Context Protocolserver so an AI agent (Claude, Cursor, or any MCP-compatible client) can run SEO/GEO checks against a live site directly, without a human invoking the CLI by hand.

pip install "llmscout-cli[mcp]"

Add it to your MCP client's config (for Claude Desktop,claude_desktop_config.json):

{ "mcpServers": { "llmscout": { "command": "uvx", "args": ["--from", "llmscout-cli", "llmscout-mcp"] } } }

The server exposes one tool,run, that shells out to the publishedllmscoutnpm binary with the given subcommand and arguments plus--json, and returns the parsed JSON result:

run(["check", "./my-site", "--site-url", "https://example.com"])

Transport is stdio, so there is nothing to host: the MCP client spawns the server as a local subprocess. Source:python/src/llmscout/mcp_server.py.

{ "sites": [ { "name": "client-a", "path": "./clients/client-a" }, { "name": "client-b", "path": "./clients/client-b" } ] }

LLMScout fleet report [FAIL] client-a (/abs/path/clients/client-a) -- 6 PASS, 14 WARN, 1 FAIL [PASS] client-b (/abs/path/clients/client-b) -- 8 PASS, 13 WARN, 0 FAIL Fleet summary: 1 site(s) passed, 1 site(s) failed, 0 site(s) errored (2 total).

Add--out-dir ./reportsand each site's result is also written to its own auto-named file (client-a.txt,client-b.txt, or.jsonwith--json) -- instead of one combined stdout dump, an agency running this across many client sites gets one distinguishable report per client. Each manifest entry'spathresolves against the manifest file's own directory, not the process working directory, so the same manifest works no matter where you invoke it from. Everything is local filesystem access, with no SSH and no remote-execution surface.

Both distributions are also genuinely importable libraries, not just CLIs. The exports below are real (grepped fromsrc/index.tsandpython/src/llmscout/__init__.py), and the examples are tested against the published packages, not written from memory.

import { loadSite, runChecks, ALL_CHECKS } from "llmscout-cli"; const ctx = await loadSite("https://example.com"); const results = await runChecks(ALL_CHECKS, ctx); console.log(results[0].status, results[0].name); // "PASS" "Title tag"

Types:Check,CheckContext,CheckResult,CheckStatus("PASS" \| "FAIL" \| "WARN"),CheckCategory("technical" \| "geo"),SiteResources,LLMScoutConfig,FleetManifest. No generated API docs site exists yet; this table and the source's own TSDoc comments are the reference.

from llmscout import load_site, run_checks, ALL_CHECKS ctx = load_site("https://example.com") results = run_checks(ALL_CHECKS, ctx) print(results[0].status, results[0].name) # PASS Title tag

The Python package mirrors the TypeScript one function-for-function:load_site,run_checks,has_failure,ALL_CHECKS/TECHNICAL_CHECKS/GEO_CHECKS,init_project,load_fleet_manifest/run_fleet,safe_fetch/assert_http_url,load_config/default_config/select_checks, plus theCheck,CheckContext,CheckResult, andSiteResourcestypes. Full list inpython/src/llmscout/__init__.py's__all__. No generated Sphinx docs exist yet; the module docstring (import llmscout; help(llmscout)) and this table are the reference.

Every cell below is drawn from a verifiable source (a repo file, a package manifest, or an open issue), cited under the table. "Checker" means the tool audits an existing live site; "generator" means it emits SEO/GEO asset files for you to publish.

Sources: LLMScout, from this repo'spackage.json,src/fetch-utils.ts,src/checks/index.ts, and the absence ofchild_processinsrc/. claude-seo, from its README install section (Python 3.10+,install.sh/install.ps1,/plugin), its MIT license, and open issues #137/#138/#139. geo-seo-claude, from its README and its own issue tracker (Windows install pain issues #69, #21, #3, all cited above). geo-optimizer-skill, from its README (Python 3.9+,pip install geo-optimizer-skill/uvx, MIT, CLI with audit/fix commands).

Hosted analytics products such as Profound, Peec, and Evertune are a different category (paid, hosted GEO-tracking dashboards, not installable OSS checkers) and are intentionally not in this table.

LLMScout is an independent, open-source command-line tool that checks a website for 21 technical-SEO and generative-engine-optimization issues. It ships as two genuinely independent, feature-equivalent distributions: a TypeScript/Node CLI with two runtime dependencies (cheerio,commander) and zero external interpreter or browser, and a Python CLI with zero runtime dependencies at all. Neitherchild_process(TypeScript) nor a subprocess call (Python) appears anywhere in either implementation's checks.

It exists to replace the install flow of an existing project,AgriciDaniel/claude-seo, for one specific class of problem. That project is a Claude Code SEO/GEO skill that carries out its checks by shelling out to Python scripts (Playwright-based rendering,pip-installed dependencies) from skill instructions. That external-toolchain-plus-path-resolution chain is the root cause behind a recurring pattern of install/Windows/path bugs filed against it, including three issues (since closed, but each documenting the architectural failure mode this project exists to avoid):#137(a fresh/plugininstall provisions neither the Python dependencies nor Playwright Chromium),#138(skill instructions hardcodepython3, which does not exist on a stock Windows install), and#139(skills callscripts/*.pythrough a relative path that does not resolve when the scripts live under the plugin root). The same class of pain shows up independently inzubair-trabzada/geo-seo-claude, another actively used Claude Code GEO skill (issues#69,#21,#3) -- the failure mode is architectural, not specific to one project.

Beyond the install-fix wedge, LLMScout's checks track the concrete, evidence-backed direction the GEO space has actually moved since mid-2025 -- seeWhy GEO checks matter right nowfor the cited sources behind that claim, including the training-versus-search AI crawler split, Markdown content negotiation, and Google's own documented FAQ-rich-result deprecation.

LLMScout was freshly renamed from its original name, SeoFleet. Both distributions are being republished under the new package name (llmscout-clion both npm and PyPI) -- seeInstallfor both paths, and the badges above for live version status.

Does LLMScout tell me to add an llms.txt?No, it only reports whether one exists.llms.txtis a real, growing convention, but Google has stated publicly it does not support it -- seeWhy GEO checks matter right nowfor the source. LLMScout reports facts a site owner can act on; it does not take a position on an unsettled question.

Is there a Python version?Yes --pip install llmscout-cliinstalls a genuine, independent Python port (not a wrapper around the Node binary), with zero runtime dependencies. It runs the same 21 checks with the same PASS/WARN/FAIL verdicts as this npm package. Seepython/README.md.

Can I use LLMScout commercially, or in a closed-source project?Yes. Both distributions are MIT licensed (seeLICENSE): you can use, modify, and redistribute LLMScout in commercial and closed-source work, with no royalty and no obligation to open-source anything it checks. The only requirement is keeping the copyright notice and license text if you redistribute the source itself.

SeeCONTRIBUTING.mdfor the full guide, covering both the TypeScript and Python codebases. Useful commands for this package:

npm install npm run build # tsc build to dist/ npm run typecheck # tsc --noEmit npm test # vitest run (248 tests) npm run test:coverage npm run lint # eslint src test

CI (.github/workflows/ci.yml) runs lint, typecheck, build, coverage, andnpm audit --audit-level=highon every push and pull request tomain. Issues and pull requests are welcome athttps://github.com/RudrenduPaul/LLMScout/issues.

Adding a 22nd check is intentionally small: implement theCheckinterface (src/types.ts) in a new file undersrc/checks/, then register it insrc/checks/index.ts(and the Python equivalent underpython/src/llmscout/checks/, perCONTRIBUTING.md).

Scan how ChatGPT and Claude read your app, see who they name instead of you, and get the paste-ready fixes (llms.txt, JSON-LD, FAQ, meta) to get found.