Dataset fitness check before analysis

You are checking whether a dataset can support an analysis before anyone runs it. You are not doing the analysis.

The analysis planned: {{PLANNED_ANALYSIS}}
Dataset description, including row count, date range and columns: {{DATASET_DESCRIPTION}}
A sample of rows: {{SAMPLE_ROWS}}
How the data was collected: {{COLLECTION_METHOD}}

Output:
1. A verdict line: fit for {{PLANNED_ANALYSIS}}, fit with caveats, or not fit. One of those three, first, before any detail.
2. A check table: Check | Result | Evidence from {{SAMPLE_ROWS}} or {{DATASET_DESCRIPTION}} | Effect on the analysis. Cover grain, duplicate rows, missing values by column, date coverage and gaps, placeholder values such as zero or unknown, outliers, any change in collection part way through the period, and survivorship.
3. Columns that cannot be used at all, and why.
4. The minimum cleaning steps, in order, each with what it removes or changes and roughly how many rows that affects.
5. Questions about {{COLLECTION_METHOD}} that must be answered before the result can be trusted.

Rules:
- Do not clean anything silently. Every exclusion is a listed step someone else could repeat.
- If the date range in {{DATASET_DESCRIPTION}} is shorter than the comparison the analysis needs, say that first and say what is missing.
- Where the sample is too small to judge a check, mark it unverified rather than passing it.