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/meridiandocconsultant

@a000de2
by googlegoogle/meridian1.6k stars
302

Assists users with questions about Meridian Marketing Mix Modeling concepts, parameters, and best practices by finding and consulting the relevant documentation. Use when the user asks for explanations or guidance on Meridian topics. Don't use for writing code or running models directly.

  • 2 files
  • 17.9 KB
  • Updated last month
  • GitHub

Use this Skill: https://skilld.dev/gh/google/meridian/meridiandocconsultant

This session only. Nothing lands on disk.

SKILL.md

≈78 tokens always: the name and description. ≈810 when used: this file. ≈3.7k more on demand in 1 file.

Meridian Documentation Consultant

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

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 Documents (RAG-style Retrieval)

  • Consult the Documentation Map and use its summaries to identify a batch of potentially relevant documents (e.g., 3-5 files) related to the user's query.
  • Direct Term Search: If the user asks about specific technical terms (e.g., eta_m, xi_c, knots), you MUST use grep_search or text search to search for these terms directly across all documentation files to find candidates, EVEN IF a file in the Documentation Map seems to cover the topic.
  • Concept Intersection: If the query involves multiple concepts (e.g., "priors" + "insufficient data"), look for files that discuss these concepts together.
  • CRITICAL: Do NOT read documentation files one by one to discover the right one. Rely on the map's summaries and keyword search to identify candidates.

3. Select and Read Candidates

  • From the candidate list, select the Top N (e.g., 3) most promising files to read.
  • For each selected file, do NOT read the whole file if it is large.
  • Use grep_search or text search to find specific keywords within the file to locate the relevant section.
  • 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 reading documentation files over source code files for conceptual questions.
  • Use view_file with StartLine and EndLine to read only a window of lines around the match (e.g., 50 lines before and after).
  • Expand the Window if Needed: If the viewed content seems to continue or if you need more context, use view_file again to read subsequent or preceding lines.

4. Synthesize and Answer

  • Multi-Document Synthesis: The answer does not have to depend on a single document. Synthesize the answer from multiple candidate documents if they provide complementary information.
  • Grounding Constraint: Do not answer with specific numbers or recommendations from your general knowledge if they are not present in the consulted documentation.
  • Cite the Source: In your final response, you MUST explicitly cite the full file path(s) of all documentation you consulted. Ensure you cite the specific file that provided the answer to the specific condition.

Source: SKILL.md on GitHub

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Signed by skilld at a000de2. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 1 hour ago.

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