Meridian Documentation Consultant

This skill guides the agent in finding and consulting the right page of the official Meridian documentation to answer user questions token-efficiently.

The documentation is published at https://developers.google.com/meridian/. It covers both Meridian MMM (/meridian/docs/...) and Meridian GeoX geo experiments (/meridian/geox/...). It is not included in the Meridian GitHub repository or the google-meridian Python package, so do not look for documentation files on disk.

Core Workflow

When the user asks a conceptual or procedural question about Meridian, follow these steps:

1. Identify the Topic

• Analyze the user's question to identify the core topic (e.g., "knots", "priors", "data format").

2. Locate Candidate Pages (RAG-style Retrieval)

• Consult the Documentation Map and use its summaries to identify a batch of potentially relevant pages (e.g., 3-5 pages) related to the user's query.
• Direct Term Search: If the user asks about specific technical terms (e.g., eta_m, xi_c, knots), search the Documentation Map for these terms and then search the text of the candidate pages you fetch in step 3, EVEN IF a page in the Documentation Map seems to cover the topic. If the map has no match, you may use web search restricted to developers.google.com/meridian to find candidate pages.
• Concept Intersection: If the query involves multiple concepts (e.g., "priors" + "insufficient data"), look for pages that discuss these concepts together.
• CRITICAL: Do NOT fetch pages one by one to discover the right one, as fetching unrelated pages wastes tokens and latency. Rely on the map's summaries to identify candidates.

3. Fetch and Read Candidates

• From the candidate list, select the Top N (e.g., 3) most promising pages to read.
• Fetch the Markdown version of each page by appending .md.txt to its URL. For example, the page https://developers.google.com/meridian/docs/pre-modeling/amount-data-needed is available as plain Markdown at https://developers.google.com/meridian/docs/pre-modeling/amount-data-needed.md.txt. Use your URL-fetching tool, or curl -sL <url>.md.txt if you only have a shell. The regular HTML page is mostly site layout, so prefer the .md.txt version.
• For large pages, search the fetched text for specific keywords to locate the relevant section instead of reading everything.
• Broaden Search Terms: If a specific keyword search fails (e.g., "maximum channels"), try searching for broader related concepts (e.g., "channels") to find the relevant section.
• Searching Recommendations: When looking for recommendations, search for keywords like "recommend", "best practice", or "should" combined with the topic keyword.
• Handling Fuzzy Matches on Limits: If the user asks for a "maximum" and the docs say "below X", provide that value.
• Prefer Documentation over Code: Prioritize the documentation pages over source code files for conceptual questions.
• No Web Access: If you cannot fetch the pages, tell the user that this skill needs access to developers.google.com/meridian, give them the page URLs from the Documentation Map to read, and do not present unverified details as documented guidance.

4. Synthesize and Answer

• Multi-Document Synthesis: The answer does not have to depend on a single page. Synthesize the answer from multiple candidate pages if they provide complementary information.
• Grounding Constraint: Do not answer with specific numbers or recommendations from your general knowledge or from search-result snippets if they are not present in a page you fetched and read.
• Cite the Source: In your final response, you MUST cite the full page URL(s) (e.g., https://developers.google.com/meridian/docs/pre-modeling/amount-data-needed) of all documentation you consulted. Only cite pages you actually fetched and read. Do not cite local file paths such as docs/...md; they do not exist for Meridian users. Ensure you cite the specific page that provided the answer to the specific condition.