data-validation-magnitude-checks
Sub-skill of data-validation: Magnitude Checks (+2).
Best use case
data-validation-magnitude-checks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-validation: Magnitude Checks (+2).
Teams using data-validation-magnitude-checks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/magnitude-checks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-validation-magnitude-checks Compares
| Feature / Agent | data-validation-magnitude-checks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of data-validation: Magnitude Checks (+2).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Magnitude Checks (+2) ## Magnitude Checks For any key number in your analysis, verify it passes the "smell test": | Metric Type | Sanity Check | |---|---| | User counts | Does this match known MAU/DAU figures? | | Revenue | Is this in the right order of magnitude vs. known ARR? | | Conversion rates | Is this between 0% and 100%? Does it match dashboard figures? | | Growth rates | Is 50%+ MoM growth realistic, or is there a data issue? | | Averages | Is the average reasonable given what you know about the distribution? | | Percentages | Do segment percentages sum to ~100%? | ## Cross-Validation Techniques 1. **Calculate the same metric two different ways** and verify they match 2. **Spot-check individual records** -- pick a few specific entities and trace their data manually 3. **Compare to known benchmarks** -- match against published dashboards, finance reports, or prior analyses 4. **Reverse engineer** -- if total revenue is X, does per-user revenue times user count approximately equal X? 5. **Boundary checks** -- what happens when you filter to a single day, a single user, or a single category? Are those micro-results sensible? ## Red Flags That Warrant Investigation - Any metric that changed by more than 50% period-over-period without an obvious cause - Counts or sums that are exact round numbers (suggests a filter or default value issue) - Rates exactly at 0% or 100% (may indicate incomplete data) - Results that perfectly confirm the hypothesis (reality is usually messier) - Identical values across time periods or segments (suggests the query is ignoring a dimension)
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