Google Search / Ads / Analytics docs

Google Search / Ads / Analytics - Knowledge Base + Agent Skills

A curated, validated local corpus of official Google documentation for Search/SEO, Search Console, Google Ads, and Google Analytics 4 - plus two Claude Code skills, two slash commands, and two multi-agent workflows that turn it into an AI-agent capability: cited retrieval and a multi-agent SEO audit.

Install (one line, in Claude Code)

/plugin marketplace add bsisduck/google-search-ads-analytics-docs

then /plugin install google-search-ads-analytics@bsisduck - the skills, /seo-audit and /google-docs commands, and the workflows are now available.

The corpus mirrors Google's own URL structure, carries YAML frontmatter with the original source_url on every page, and is validated (0 broken links, 0 duplicates, 0 error pages). The skills retrieve from it and answer with citations back to the official Google page - so an agent never has to guess about SEO, indexing, structured data, Search Console reports, Ads conversions, or GA4 measurement.

Not affiliated with Google. The pages under Docs/ are a local reference copy of official Google documentation, (c) Google, licensed CC BY 4.0. All tooling/code is MIT.

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Why this exists

LLMs hallucinate Google's rules - the exact meta robots syntax, which JSON-LD props are required for a rich result, what a Search Console report column means. This repo grounds an agent in the real docs: ~1.3M tokens of reference content, too big for one context window but perfectly sized for agentic file search (locate 1-5 pages, read them whole, cite the source). A semantic vector sidecar covers conceptual / paraphrased queries where lexical search underperforms.

What's inside

Path	What
Docs/	304 Google doc pages (Markdown): Search Central (184), Search Console (47), GA4 (41), Google Ads (32). URL-mirrored tree + 38 section indexes.
Docs/KNOWLEDGE-BASE-ARCHITECTURE.md	Decision record: how to map a corpus this size for an AI agent (file-search + Skill + vector sidecar), web-grounded.
.claude/skills/google-search-ads-analytics-docs/	Skill - cited retrieval over Docs/. Lexical (search.py, stem-aware, stdlib) + semantic (vec_search.py) + hybrid RRF (hybrid.py).
.claude/skills/google-seo-audit/	Skill - multi-agent SEO/discoverability audit of a page (live URL or local .html), grounded in the docs skill, producing a scored, cited report.
.claude/workflows/	Workflows - seo-audit.js (snapshot -> verify per dimension -> adversarial refute -> synthesize) and docs-research.js (decompose -> retrieve -> verify claims -> synthesize).
commands/	Slash commands - /seo-audit [url] and /google-docs [question].
app/	Web visualizer (Mermaid) of the corpus + retrieval architecture.
scripts/	Reproducible pipeline: scrape -> frontmatter -> index -> embeddings -> validate.
data/	Evaluation query sets (Polish + English cross-lingual).
AUDIT-app-index.md	Example google-seo-audit output (audit of app/index.html).

Quick start (from a clone)

Zero-install lexical search (stdlib only - works immediately):

python3 .claude/skills/google-search-ads-analytics-docs/search.py "block a page from indexing" --no-content

Full setup (semantic + hybrid retrieval, model daemon, the audit scanner):

./setup.sh                 # creates .venv and installs scrapling + sentence-transformers

Install the skills globally for Claude Code (alternative to the plugin, points at this clone):

./install-skills.sh        # copies skills to ~/.claude/skills, wires the corpus path

In Claude Code, the skills auto-trigger:

• ask "how do I block a page from indexing?" -> google-search-ads-analytics-docs
• ask "audit app/index.html for SEO" -> google-seo-audit

Using it

Retrieval (CLI)

# hybrid (best quality - lexical + dense, RRF-fused). Needs ./setup.sh
.venv/bin/python3 .claude/skills/google-search-ads-analytics-docs/hybrid.py "product structured data with price and rating"