data-analysis-1（数据分析 1）

When to Use

Use this skill when the user needs to analyze, explain, or visualize data from SQL, spreadsheets, notebooks, dashboards, exports, or ad hoc tables.

Use it for KPI debugging, experiment readouts, funnel or cohort analysis, anomaly reviews, executive reporting, and quality checks on metrics or query logic.

Prefer this skill over generic coding or spreadsheet help when the hard part is analytical judgment: metric definition, comparison design, interpretation, or recommendation.

User asks about: analyzing data, finding patterns, understanding metrics, testing hypotheses, cohort analysis, A/B testing, churn analysis, or statistical significance.

Core Principle

Analysis without a decision is just arithmetic. Always clarify: What would change if this analysis shows X vs Y?

Methodology First

Before touching data:

1. What decision is this analysis supporting?
2. What would change your mind? (the real question)
3. What data do you actually have vs what you wish you had?
4. What timeframe is relevant?

Statistical Rigor Checklist

• Sample size sufficient? (small N = wide confidence intervals)
• Comparison groups fair? (same time period, similar conditions)
• Multiple comparisons? (20 tests = 1 "significant" by chance)
• Effect size meaningful? (statistically significant!= practically important)
• Uncertainty quantified? ("12-18% lift" not just "15% lift")

Architecture

This skill does not require local folders, persistent memory, or setup state.

Use the included reference files as lightweight guides:

• metric-contracts.md for KPI definitions and caveats
• chart-selection.md for visual choice and chart anti-patterns
• decision-briefs.md for stakeholder-facing outputs
• pitfalls.md and techniques.md for analytical rigor and method choice

Quick Reference

Load only the smallest relevant file to keep context focused.

Core Rules

1. Start from the decision, not the dataset

• Identify the decision owner, the question that could change a decision, and the deadline before doing analysis.
• If no decision would change, reframe the request before computing anything.

2. Lock the metric contract before calculating

• Define entity, grain, numerator, denominator, time window, timezone, filters, exclusions, and source of truth.
• If any of those are ambiguous, state the ambiguity explicitly before presenting results.

3. Separate extraction, transformation, and interpretation

• Keep query logic, cleanup assumptions, and analytical conclusions distinguishable.
• Never hide business assumptions inside SQL, formulas, or notebook code without naming them in the write-up.

4. Choose visuals to answer a question

• Select charts based on the analytical question: trend, comparison, distribution, relationship, composition, funnel, or cohort retention.
• Do not add charts that make the deck look fuller but do not change the decision.

5. Brief every result in decision format

• Every output should include the answer, evidence, confidence, caveats, and recommended next action.
• If the output is going to a stakeholder, translate the method into business implications instead of leading with technical detail.

6. Stress-test claims before recommending action

• Segment by obvious confounders, compare the right baseline, quantify uncertainty, and check sensitivity to exclusions or time windows.
• Strong-looking numbers without robustness checks are not decision-ready.

7. Escalate when the data cannot support the claim

• Block or downgrade conclusions when sample size is weak, the source is unreliable, definitions drifted, or confounding is unresolved.
• It is better to say "unknown yet" than to produce false confidence.

Common Traps

• Reusing a KPI name after changing numerator, denominator, or exclusions -> trend comparisons become invalid.
• Comparing daily, weekly, and monthly grains in one chart -> movement looks real but is mostly aggregation noise.
• Showing percentages without underlying counts -> leadership overreacts to tiny denominators.
• Using a pretty chart instead of the right chart -> the output looks polished but hides the actual decision signal.
• Hunting for interesting cuts after seeing the result -> narrative follows chance instead of evidence.
• Shipping automated reports without metric owners or caveats -> bad numbers spread faster than they can be corrected.
• Treating observational patterns as causal proof -> action plans get built on correlation alone.

Approach Selection

For technique details and when to use each, see techniques.md.

Output Standards

1. Lead with the insight, not the methodology
2. Quantify uncertainty - ranges, not point estimates
3. State limitations - what this analysis can't tell you
4. Recommend next steps - what would strengthen the conclusion

Red Flags to Escalate

• User wants to "prove" a predetermined conclusion
• Sample size too small for reliable inference
• Data quality issues that invalidate analysis
• Confounders that can't be controlled for

External Endpoints

This skill makes no external network requests.

No data is sent externally.

Security & Privacy

Data that leaves your machine:

• Nothing by default.

Data that stays local:

• Nothing by default.

This skill does NOT:

• Access undeclared external endpoints.
• Store credentials or raw exports in hidden local memory files.
• Create or depend on local folder systems for persistence.
• Create automations or background jobs without explicit user confirmation.
• Rewrite its own instruction source files.

Related Skills

Install with clawhub install <slug> if user confirms:

• sql - query design and review for reliable data extraction.
• csv - cleanup and normalization for tabular inputs before analysis.
• dashboard - implementation patterns for KPI visualization layers.
• report - structured stakeholder-facing deliverables after analysis.
• business-intelligence - KPI systems and operating cadence beyond one-off analysis.

Feedback

• If useful: clawhub star data-analysis
• Stay updated: clawhub sync