Data quality assessment and preparation for Marketing Mix Models. Use when evaluating datasets for MMM readiness, checking data requirements, validating columns, assessing collinearity, handling missing values, or preparing marketing spend data for pymc-marketing modeling. Also activate when the user asks about minimum data requirements, data granularity, or feature engineering for MMM.
/plugin marketplace add Yakoub-ai/agent-mmm
The exact command may vary by repository. Check the README on GitHub.
For the skill author
Shows your skill is listed on Skillteca, generates a backlink and trackable traffic.
[](https://www.skillteca.com.br/skills/mmm-data-quality?utm_source=badge&utm_medium=readme&utm_campaign=badge)
Use this skill for any task involving spreadsheet files as primary input or output, such as opening, reading, editing, fixing, creating, or converting .xlsx, .xlsm, .csv, or .tsv files.
Searches claude-mem's persistent cross-session memory database. Use this to answer questions about previous solutions or retrieve work from past sessions.
Generates a week-by-week narrative digest of a project's Claude-mem timeline, splitting it into ISO-week files and using subagents to produce weekly chapters. Ideal for "weekly digests" or "narrative chapters" of a project's history.
This skill explains how claude-mem captures observations, when memory injection occurs, and where its data is stored.
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| Requirement | Threshold | Rationale |
|---|---|---|
| Minimum observations | 52 weeks | One full year captures seasonality |
| Recommended observations | 104+ weeks | Enables meaningful cross-validation (4+ folds) |
| Minimum channels | 1 | But 3+ recommended for meaningful attribution |
| Data frequency | Weekly preferred | Daily is noisy, monthly loses signal |
[Description truncada. Veja o README completo no GitHub.]
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