The only tool purpose-built for programmatic SEO compliance. It shifts the unit of analysis from URL to template: point it at a 10,000-URL pSEO directory and pseolint identifies the template clusters (e.g. /listing/:slug, /category/:slug), samples K pages from each, and produces a per-template verdict + variance metric. Fix one template, fix N pages.
npx pseolint http://localhost:3000
Skills for Claude & coding agents (new)
Design pages that pass before you crawl them. The skills/ suite gives
Claude (and any agent that supports the skills / Claude Code plugin format)
programmatic-SEO and answer-engine optimization (AEO / GEO) guidance where
every recommendation is bound to a runnable pseolint rule:
npx skills add ouranos-labs/pseolint --skill pseolint aeo
pseolint: full-lifecycle programmatic SEO: design → build → audit → fix → gate.aeo: get cited in AI Overviews / ChatGPT / Perplexity, not just ranked.
Unlike prose checklists, these have teeth: the design-time advice ends in
npx pseolint pass/fail. See skills/README.md.
Why this exists
Programmatic SEO works, when it works. The gap between "1,000 indexed pages" and "1,000 pages that survive a SpamBrain pass" is where most pSEO sites die. The Helpful Content Update made that gap permanent.
Existing SEO tools (Screaming Frog, Sitebulb, Ahrefs Site Audit) were built for editorially-curated sites. They check pages one at a time. But the SpamBrain risks of pSEO are between pages: doorway clusters, near-duplicates, entity-swap templates, thin-content propagation. You can't catch them with per-page rules.
pseolint audits the graph: it groups results by template before surfacing them. Run it before you publish, gate it in CI, fix the broken template before SpamBrain does.
How it compares
| pseolint | Screaming Frog | Ahrefs Site Audit | Sitebulb |
|---|
| Unit of analysis | template cluster | URL | URL | URL |
| Near-duplicate / doorway / entity-swap detection | ✅ | partial | - | - |
| SpamBrain-policy risk verdict | ✅ | - | - | - |
| AEO / AI-Overview citability checks | ✅ | - | - | - |
| AI fix → pull request | ✅ | - | - | - |
| CLI · GitHub Action · MCP server | ✅ | desktop | SaaS | desktop |
| Open source | ✅ MIT | - | - | - |
The general-purpose crawlers do plenty pseolint doesn't (JS rendering at scale, backlink data, log-file analysis). pseolint is the specialist for the one thing they weren't built for: programmatic-SEO compliance at the template level.
How pseolint differs
- Graph-level, not page-level. Detects near-duplicate clusters, doorway patterns, and entity-swap doorways across thousands of pages. Per-page tools can't see these.
- SpamBrain + AI Overview. 65 rules across 8 categories: SpamBrain-policy mapping (penalty risk) plus
aeo/* (AI Overview citability: llms.txt, AI-crawler access, citable facts, answer-first, summary-bait).
- Developer workflow, not SaaS UI. CLI, GitHub Action, JSON/HTML reports, MCP server, browser extension (SERP competitive recon). Lives in your repo and your PRs.
- Actionable, not advisory. Every finding has a fix, an effort tag (
quick fix / moderate / structural), and a Google docs reference.
- Safe for hosted use. SSRF guard (DNS-validated), robots.txt honoured for our own crawler, analytics-blocking in render mode,
AbortSignal cancellation, safeMode: "saas" preset for embedding in services.
- Calibrated against reputable pSEO. Engine verdicts are calibrated against a curated corpus of in-production pSEO sites that demonstrably win in search. Doorway-pattern findings cluster (no more per-pair noise); verdicts are reproducible at a fixed
sampleSeed. Dated snapshot results, the open-source corpus, and the trade-offs we accepted live at pseolint.dev/methodology. Spec: docs/superpowers/specs/2026-05-03-calibration-against-reputable-pseo.md.
- Authority-blind by design, with a manual override. pseolint analyses static content + the link graph it can see. It does NOT measure backlinks, brand mentions, domain age, or any external trust signal: there is no Moz/Ahrefs/Semrush dependency. This means the engine itself is calibrated for the authority tier of the calibration corpus (established brands). It exposes
authorityScore (0-100, via the --authority-score CLI flag, the core API, or the MCP param) so callers can adjust the verdict ladder for their tier: >= 80 shifts one tier lenient (established brand can absorb shapes a newer site can't); <= 30 shifts one tier stricter. Raw risk number unchanged so CI gates stay stable. Without the flag, treat verdicts as a directional minimum.
- Honest about blind spots. Beyond domain authority, pseolint does not currently detect: image SEO dimensions, schema-content drift (e.g. JSON-LD price ≠ rendered price), outbound-link health, search-intent alignment, parameter-URL crawl-budget waste, and a handful of specialty gaps (mobile-friendliness, cookie-banner detection, AMP/News/Video schema). The complete blind-spot audit lives at : every gap categorized by impact tier with the roadmap fix.
Full version history (calibration rounds, per-rule changes, safety hardening) is in CHANGELOG.md.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
