| name | google-search-ads-analytics-docs |
| version | 2.0.0 |
| description | 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. |
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
-
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".
-
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.
-
Cite the source_url from the result for every claim, e.g.
(source: https://developers.google.com/search/docs/...).
-
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.)
-
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).
