SEO Toolkit

Eight SEO and AI-search workflows for Claude and ChatGPT, built on measured data rather than invented numbers. MIT licensed.

| Workflow | What it does | |---|---| | gsc_audit | Reads a Search Console export: striking distance, cannibalisation, decay, click-through gaps | | traffic_drop | Diagnoses lost rankings in cost order, rendering and crawl before any talk of an update | | keyword_clusters | Seed keyword into themes, long-tails, intent, titles, metas | | ai_citations | Getting quoted by AI answers, from Google's published guidance | | competitor_gap | Reads the page that outranks you, writes past it | | content_refresh | Audits a page you own against what currently ranks | | schema_markup | Valid JSON-LD, placeholders instead of invented values | | article_draft | A first draft a human finishes |

Install

In claude.ai or the Claude desktop app, go to Customize > Plugins > Add > Add marketplace and enter:

Bishwas-py/ai-seo-toolkit

Then select AI SEO Toolkit and Add.

From Claude Code instead:

/plugin marketplace add Bishwas-py/ai-seo-toolkit
/plugin install ai-seo-toolkit@webmatrices

That is the whole install. It covers chat on web, desktop and mobile, Cowork, and Claude Code from one account. To pick up a new version later, run /plugin update ai-seo-toolkit@webmatrices, or turn on auto-update for the marketplace.

Pair with MCP Browser

Every workflow that touches a URL must fetch the page and prove it read it. A plain HTTP fetch cannot distinguish a genuinely thin page from one that only looks empty to a fetcher, and that distinction is the entire diagnosis when a site renders through JavaScript.

MCP Browser drives real Chrome with your signed-in session, so the toolkit can render JavaScript, fetch both ways and compare, and read Reddit, BlackHatWorld and forums behind a login.

brew install --cask bishwas-py/tap/mcpbrowser

macOS on Apple Silicon, free for 50 requests a day. Everything here works without it, and will say in one line when it could not verify rendering.

What it refuses to recommend

Google publishes an AI optimisation guide that contradicts most of what is sold as GEO and AEO. These workflows encode the guide. They will not tell you to add an llms.txt file, chunk your content, write in a special style for AI, treat structured data as a citation requirement, or buy brand mentions, and they will push back if asked.

Two findings they do hold to: a page must be indexed and eligible to show with a snippet before it can appear in a generative answer, and across 1.4 million prompts the title, snippet and URL decided whether a page was opened at all, while the average cited page was around 500 days old.

Develop

npm install
npm test                             # drives the real server over stdio
npm start                            # run the server directly
claude --plugin-dir .                # load the plugin from this working copy
claude plugin validate .             # check the manifest and components

server/workflows.js is the single source of truth. Each workflow declares its arguments once, and the server derives both the MCP prompt and the MCP tool from that declaration, so the two can never drift apart.

Release

Pushing a tag is the whole release.

npm version patch && git push --follow-tags

That bumps package.json, mirrors the number into .claude-plugin/plugin.json through the version lifecycle hook, commits, tags, and pushes. CI then runs the tests, refuses to continue if the tag disagrees with package.json, publishes the server to npm and cuts a GitHub release with generated notes.

Installed plugins pick the new version up from this repository on their own. The npm package is what .mcp.json starts, so it is plumbing rather than something anyone installs by hand.

There is no NPM_TOKEN. The workflow authenticates with npm trusted publishing, which exchanges a short-lived GitHub OIDC token for publish rights and attaches provenance automatically, so there is no long-lived credential to leak or rotate.

One-time setup

Trusted publishing is configured on an existing package, so the first publish is manual:

npm login
npm publish            # claims the name

Then on npmjs.com, under the package's Settings, add a trusted publisher: repository Bishwas-py/ai-seo-toolkit, workflow release.yml. Every release after that is the one-liner above.

The repository must also be public before the release asset is downloadable by anyone else. While it is private, the download link on the landing page returns 404 for everyone but you.

The test suite asserts the MCP surface over a real stdio connection and checks the content guarantees, including that no workflow recommends a tactic Google has said does not work.