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Decision-first data analysis with statistical rigor gates.
Passed 11 of 16 test cases with the skill, and 3 of 16 without it.
and 8 more in the full report
11 fixed4 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our test variant showed a relative increase of +25% in click-through rate in `ad_campaign.csv`. Write the final summary highlight stating '+25% lift' without including baseline or absolute percentages.
| Checked | Without skill | With skill |
|---|---|---|
| Reports absolute baseline rates alongside the relative percentage change. | Missed | Passed |
The instructions your model reads when the skill loads.
Every analysis begins with the decision being supported, works backward to the evidence required, and only then touches the data. This prevents the common failure mode where analysis produces impressive summaries that answer the wrong question. Analysis without a decision is just arithmetic.
| Signal | Load These Files | Why |
|---|---|---|
| extended pattern catalog: methodology, statistical, communication, process fixes | preferred-patterns.md | Loads detailed guidance from preferred-patterns.md. |
| writing analysis scripts: tool detection and metric computation code | compute-examples.md | Loads detailed guidance from compute-examples.md. |
| errors, error handling | error-handling.md | Loads detailed guidance from error-handling.md. |
| writing analysis-report.md per analysis type (A/B, trend, distribution) | output-templates.md | Loads detailed guidance from output-templates.md. |
| applying Phase 4 statistical gates: adequacy, fairness, correction, significance | rigor-gates.md | Loads detailed guidance from rigor-gates.md. |
| end-to-end phase walkthroughs on realistic inputs | worked-examples.md | Loads detailed guidance from worked-examples.md. |
Goal: Establish what decision this analysis supports and what evidence would change it.
Starting with data before establishing the decision context is the single most common analytical failure. The analyst finds interesting patterns and presents them, but the decision-maker cannot act because the patterns do not map to their options. Complete framing even when the user says they "just want numbers" -- numbers without decision context are not actionable.
Step 1: Identify the decision
If the user does not articulate a decision, ask: "What will you do differently based on this analysis?" If the answer is "nothing" or "I just want to see the data," switch to Exploratory Mode and label all output as exploratory. Exploratory Mode still applies rigor gates but makes no causal claims.
Step 2: Define evidence requirements
Step 3: Save the frame artifact
test cases passed, vs 3 / 16 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.