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Google search ads analytics docs

Skill bsisduck/google-search-ads-analytics-docs/.claude/skills/google-search-ads-analytics-docs

Official Google docs (Search/SEO, Search Console, Ads, GA4) as a validated local knowledge base (English) + two Claude Code skills: cited retrieval and a multi-agent SEO audit. Reproducible, measured (92% recall@5).

Install
npx -y skills add bsisduck/google-search-ads-analytics-docs --skill google-search-ads-analytics-docs

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

This skill should be used when the user asks about Google Search (SEO), Google Search Console, Google Ads, or Google Analytics 4 (GA4) - including SEO, crawling/indexing, robots.txt, sitemaps, structured data / rich results, Search Console reports (index coverage, performance, Core Web Vitals), Google Ads campaigns/conversions, or GA4 data collection (gtag.js, events, Measurement Protocol). It answers from a local English knowledge base of official Google documentation in Docs/ and returns precise, cited answers with the original Google source_url.

SKILL.md

4.7 KB, as published. Nobody here has run it

Google Search / Ads / Analytics docs - knowledge base

A curated, validated corpus of 304 official Google documentation pages under Docs/, covering four products. Provide precise, cited answers; do not guess when the answer is in the corpus.

What's inside

  • Docs/README.md - master map (start here).
  • Docs/<section>/README.md - 17 section indexes (tables of contents).
  • index.json (bundled) - every doc's title, section, product, source_url.
  • search.py (bundled) - frontmatter-aware ranking search (stdlib).

Products: Google Search Central (SEO/crawling/indexing/structured-data), Search Console (reports), Google Ads (campaigns/conversions), Google Analytics 4 (collection gtag.js + Measurement Protocol).

Retrieval playbook - follow in order

  1. Search first. Best quality is hybrid (lexical + semantic, RRF-fused - eval: 96% recall@5). Needs the repo venv:

    .venv/bin/python3 .claude/skills/google-search-ads-analytics-docs/hybrid.py "<the user's question>"

    Fast when the daemon runs (.venv/bin/python3 scripts/serve_models.py). Stdlib fallback (no venv): python3 .claude/skills/google-search-ads-analytics-docs/search.py "<terms>" - lexical, stem-aware, ~0.4s, works anywhere. All return ranked docs with title, path, source_url, snippet.

    Examples: "block a page from indexing", "product structured data with price and rating", "submit a sitemap", "track GA4 events with gtag.js", "google ads conversion tracking".

  2. Read the top 1-5 files whole with the Read tool (each is ~4-5K tokens and fits in context). Read complete files - never answer from the snippet alone. Quote tables and JSON-LD/code examples verbatim; do not paraphrase code.

  3. Cite the source_url from the result for every claim, e.g. (source: https://developers.google.com/search/docs/...).

  4. If search.py returns nothing useful, OR the question is conceptual / paraphrased (lexical match is weak), use the semantic fallback:

    python3 .claude/skills/google-search-ads-analytics-docs/vec_search.py "<the user's question>"

    It embeds the query (multilingual model) and returns the closest docs even when wording differs. Then read the top files whole and cite source_url as above. (Requires the repo venv with sentence-transformers.)

  5. Last resort, navigate manually: read Docs/README.md -> the relevant section README.md -> pick candidates; or grep/Glob over Docs/ for exact terms and technical tokens (hreflang, canonical, robots.txt, gtag, JSON-LD props, HTTP codes). Prefer search.py/hybrid.py, which rank for you.

Deep research (Workflow)

For a multi-part or research-style question, run the bundled docs-research workflow (Workflow tool): it decomposes the question, hybrid-retrieves and reads top docs per sub-question, adversarially verifies each claim against its cited source_url, then synthesizes one cited answer. It scales agents to the number of sub-questions and claims - no fixed cap. Saved at .claude/workflows/docs-research.js.

Rules

  • Whole-file reads + verbatim code. Precision over brevity.
  • Always cite source_url. If a matched doc has no source_url (only the authored KNOWLEDGE-BASE-ARCHITECTURE.md), say so.
  • Language: the corpus is Polish (a few help pages are English). Answer in the user's language; quote source text as-is.
  • Don't invent Google behavior that isn't in the corpus; if it's genuinely missing, say the corpus doesn't cover it.

Notes

  • The corpus is validated: 0 broken links, 0 duplicates, 0 error pages; every file carries YAML frontmatter (title, source_url, section, ...).
  • Two retrieval paths: search.py (lexical, stem-aware, stdlib - primary) and vec_search.py (semantic, embeddings - fallback for fuzzy/conceptual queries; rebuild with scripts/build_embeddings.py). See Docs/KNOWLEDGE-BASE-ARCHITECTURE.md.
  • Helper usage variants: search.py "query" --top 5 - --no-content (faster, metadata-only) - --doc <doc_id> (resolve one doc's citation + head).

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.