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SKILL.md

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+---
+name: Data Analysis
+slug: data-analysis
+version: 1.0.2
+homepage: https://clawic.com/skills/data-analysis
+description: "数据分析和可视化。"
+changelog: Added metric contracts, chart guidance, and decision brief templates for more reliable analysis.
+metadata: {"clawdbot":{"emoji":"D","requires":{"bins":[]},"os":["linux","darwin","win32"]}}
+---
+
+## 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.
+
+| Topic | File |
+|-------|------|
+| Metric definition contracts | `metric-contracts.md` |
+| Visual selection and chart anti-patterns | `chart-selection.md` |
+| Decision-ready output formats | `decision-briefs.md` |
+| Failure modes to catch early | `pitfalls.md` |
+| Method selection by question type | `techniques.md` |
+
+## 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
+
+| Question type | Approach | Key output |
+|---------------|----------|------------|
+| "Is X different from Y?" | Hypothesis test | p-value + effect size + CI |
+| "What predicts Z?" | Regression/correlation | Coefficients + R² + residual check |
+| "How do users behave over time?" | Cohort analysis | Retention curves by cohort |
+| "Are these groups different?" | Segmentation | Profiles + statistical comparison |
+| "What's unusual?" | Anomaly detection | Flagged points + context |
+
+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.
+
+| Endpoint | Data Sent | Purpose |
+|----------|-----------|---------|
+| None | None | N/A |
+
+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`

_meta.json

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+{
+  "ownerId": "kn73vp5rarc3b14rc7wjcw8f8580t5d1",
+  "slug": "data-analysis",
+  "version": "1.0.2",
+  "publishedAt": 1773241910484
+}

chart-selection.md

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+# Chart Selection
+
+Choose visuals based on the question, not on what is easiest to render.
+
+## Question to Chart Map
+
+| Question | Preferred chart | Notes |
+|----------|-----------------|-------|
+| How is a metric changing over time? | line chart | annotate structural breaks and missing data |
+| Which groups are highest or lowest? | sorted bar chart | keep a shared baseline |
+| How is the distribution shaped? | histogram or box plot | avoid average-only summaries |
+| Are two variables related? | scatter plot | show trend and outliers separately |
+| How do parts contribute to the whole? | stacked bar with totals | keep category count low |
+| Where are users dropping? | funnel chart | define the time window explicitly |
+| How do cohorts retain over time? | cohort table or heatmap | show cohort size alongside retention |
+
+## Default Rules
+
+- Bars start at zero unless there is a strong reason not to.
+- Show underlying counts next to percentages when denominators are small.
+- Prefer direct labels over legends when possible.
+- Use one chart per decision question, not one chart per available metric.
+
+## Visual Anti-Patterns
+
+- Pie charts with many slices -> comparisons become guesswork.
+- Dual-axis charts -> viewers infer relationships that are not there.
+- Cumulative-only charts -> hide recent deterioration or recovery.
+- Truncated bar axes -> exaggerate small differences.
+- Stacked areas with many categories -> impossible to compare layers.
+
+## Before Shipping a Chart
+
+Check:
+
+1. What decision question this chart answers.
+2. Whether the baseline is visible.
+3. Whether the grain and time window match the narrative.
+4. Whether annotations explain outages, launches, or missing data.
+5. Whether a table would be clearer than the chart.

decision-briefs.md

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+# Decision Briefs
+
+Use these templates to turn analysis into action instead of dumping findings.
+
+## Standard Decision Brief
+
+1. Decision question.
+2. Short answer.
+3. Evidence: key numbers and comparison baseline.
+4. Confidence: high, medium, or low, with one sentence why.
+5. Caveats and what could still change the conclusion.
+6. Recommended next action, owner, and due date.
+
+## Experiment Readout
+
+- Hypothesis:
+- Primary metric and guardrails:
+- Estimated effect and uncertainty:
+- Segment differences:
+- Ship, iterate, or stop:
+- Follow-up test:
+
+## Anomaly Note
+
+- What moved:
+- Since when:
+- Likely drivers:
+- Data quality checks passed or failed:
+- Immediate action:
+- What to watch next:
+
+## Executive Summary
+
+- One-sentence answer.
+- Two or three supporting bullets with numbers.
+- One caveat.
+- One decision or escalation request.
+
+## Writing Rules
+
+- Lead with the answer, not the method.
+- Translate statistics into business implications.
+- Separate observations from recommendations.
+- If confidence is low, say what would raise confidence.
+- Avoid dumping every cut you explored; keep only evidence that changes the decision.

metric-contracts.md

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+# Metric Contracts
+
+Use this when a KPI, dashboard tile, or report number could be interpreted in more than one way.
+
+## Contract Template
+
+Capture each metric in this order before trusting comparisons:
+
+1. Business question the metric is meant to answer.
+2. Entity and grain: user, account, order, session, day, week, month.
+3. Numerator and denominator with exact inclusion logic.
+4. Filters and exclusions: internal traffic, refunds, test accounts, paused users.
+5. Time window, timezone, and refresh cadence.
+6. Source of truth and owner.
+7. Known caveats, version changes, and safe interpretation range.
+
+## Minimum Contract Output
+
+| Field | Example |
+|-------|---------|
+| Metric | Paid conversion rate |
+| Question | Is onboarding quality improving? |
+| Grain | weekly |
+| Numerator | first paid subscriptions |
+| Denominator | qualified onboarding starts |
+| Filters | excludes employees and QA accounts |
+| Timezone | UTC |
+| Source | warehouse.subscriptions_daily |
+| Owner | Growth lead |
+| Caveat | Launch week excluded because tracking was partial |
+
+## Stop Conditions
+
+Do not present a metric as stable if:
+
+- Numerator or denominator changed between periods.
+- Source ownership is unclear.
+- Filters were applied ad hoc and not documented.
+- Time windows or timezones differ across comparisons.
+- A dashboard label hides a formula change.
+
+## Fast Questions to Ask
+
+- "What exactly counts in the numerator?"
+- "Who is excluded and why?"
+- "What is the comparison baseline?"
+- "Has this definition changed over time?"
+- "Who would dispute this number internally?"

pitfalls.md

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+# Analytical Pitfalls — Detailed Examples
+
+## Simpson's Paradox
+
+**What it is:** A trend that appears in aggregated data reverses when you segment by a key variable.
+
+**Example:**
+- Overall: Treatment A has 80% success, Treatment B has 85% -> "B is better"
+- But segmented by severity:
+  - Mild cases: A=90%, B=85% -> A is better
+  - Severe cases: A=70%, B=65% -> A is better
+- Paradox: A is better in BOTH groups, but B looks better overall because B got more mild cases
+
+**How to catch:** Always segment by obvious confounders (user type, time period, source, severity) before concluding.
+
+---
+
+## Survivorship Bias
+
+**What it is:** Drawing conclusions only from "survivors" while ignoring those who dropped out.
+
+**Example:**
+- "Users who completed onboarding have 80% retention!"
+- Problem: You're only looking at users who already demonstrated commitment by completing onboarding
+- The 60% who abandoned onboarding aren't in your "user" dataset
+
+**How to catch:** Ask "Who is NOT in this dataset that should be?" Include churned users, failed attempts, non-converters.
+
+---
+
+## Comparing Unequal Periods
+
+**What it is:** Comparing metrics across time periods of different lengths or characteristics.
+
+**Examples:**
+- February (28 days) vs January (31 days) revenue
+- Holiday week vs normal week traffic
+- Q4 (holiday season) vs Q1 for e-commerce
+
+**How to catch:**
+- Normalize to per-day, per-user, or per-session
+- Compare same period last year (YoY) not sequential months
+- Flag seasonal factors explicitly
+
+---
+
+## p-Hacking (Multiple Comparisons)
+
+**What it is:** Running many statistical tests until finding a "significant" result, then reporting only that one.
+
+**Example:**
+- Test 20 different user segments for conversion difference
+- At p=0.05, expect 1 "significant" result by chance alone
+- Report: "Segment X shows significant improvement!" (cherry-picked)
+
+**How to catch:**
+- Apply Bonferroni correction (divide alpha by number of tests)
+- Pre-register hypotheses before looking at data
+- Report ALL tests run, not just significant ones
+
+---
+
+## Spurious Correlation in Time Series
+
+**What it is:** Two variables both trending over time appear correlated, but the relationship is meaningless.
+
+**Example:**
+- "Revenue and employee count are 95% correlated!"
+- Both grew over time. Controlling for time, there's no relationship.
+- Classic: "Ice cream sales correlate with drowning deaths" (both rise in summer)
+
+**How to catch:**
+- Detrend both series before correlating
+- Check if relationship holds within time periods
+- Ask: "Is there a causal mechanism, or just shared time trend?"
+
+---
+
+## Aggregating Percentages
+
+**What it is:** Averaging percentages instead of recalculating from underlying totals.
+
+**Example:**
+- Store A: 10/100 = 10% conversion
+- Store B: 5/10 = 50% conversion
+- Wrong: "Average conversion is 30%"
+- Right: 15/110 = 13.6% conversion
+
+**How to catch:** Never average percentages. Sum numerators, sum denominators, recalculate.
+
+---
+
+## Selection Bias in A/B Tests
+
+**What it is:** Treatment and control groups differ systematically before treatment is applied.
+
+**Examples:**
+- Users who opted into new feature vs those who didn't
+- Early adopters (Monday signups) vs late week (Friday signups)
+- Users who saw the experiment (loaded fast enough) vs those who didn't
+
+**How to catch:**
+- Verify pre-experiment metrics are balanced
+- Use intention-to-treat analysis
+- Check for differential attrition
+
+---
+
+## Confusing Causation
+
+**What it is:** Assuming X causes Y when the relationship might be: Y causes X, Z causes both, or it's coincidental.
+
+**Example:**
+- "Power users have higher retention"
+- Did power usage cause retention? Or did retained users become power users over time? Or does a third factor (job role) drive both?
+
+**How to catch:**
+- Can you run an experiment? (randomize treatment)
+- Is there a natural experiment? (policy change, feature rollout)
+- At minimum: control for obvious confounders

techniques.md

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+# Analysis Techniques — When to Use Each
+
+## Hypothesis Testing
+
+**Use when:** Comparing two groups to determine if a difference is real or random chance.
+
+**Technique selection:**
+| Data type | Groups | Test |
+|-----------|--------|------|
+| Continuous | 2 | t-test (if normal) or Mann-Whitney |
+| Continuous | 3+ | ANOVA or Kruskal-Wallis |
+| Proportions | 2 | Chi-square or Fisher's exact |
+| Paired data | 2 | Paired t-test or Wilcoxon signed-rank |
+
+**Key outputs:**
+- p-value (probability of seeing this difference by chance)
+- Effect size (how big is the difference - Cohen's d, odds ratio)
+- Confidence interval (range of plausible true values)
+
+**Watch out for:**
+- Large samples make everything "significant" - focus on effect size
+- Multiple comparisons inflate false positives
+- Normality assumptions (use non-parametric if violated)
+
+---
+
+## Cohort Analysis
+
+**Use when:** Understanding how user behavior changes over time, segmented by when they started.
+
+**Types:**
+- **Retention cohorts:** % of users still active N days after signup
+- **Revenue cohorts:** Revenue per cohort over time
+- **Behavioral cohorts:** Feature adoption by signup cohort
+
+**Setup:**
+1. Define cohort (usually signup week/month)
+2. Define event (login, purchase, specific action)
+3. Define time windows (day 1, 7, 30, 90)
+4. Build matrix: cohort × time period
+
+**Key outputs:**
+- Retention curves (line chart by cohort)
+- Cohort comparison (are newer cohorts performing better?)
+- Time-to-event patterns
+
+**Watch out for:**
+- Cohort size differences (small cohorts = noisy data)
+- Seasonality (December cohort behaves differently)
+- Definition consistency (what counts as "active"?)
+
+---
+
+## Funnel Analysis
+
+**Use when:** Understanding conversion through a multi-step process.
+
+**Setup:**
+1. Define stages (visit -> signup -> activate -> purchase)
+2. Count users at each stage
+3. Calculate drop-off rates between stages
+
+**Key outputs:**
+- Conversion rates per stage
+- Biggest drop-off points
+- Segment comparison (mobile vs desktop funnels)
+
+**Watch out for:**
+- Time window (did they convert eventually, or just not today?)
+- Stage ordering (users don't always follow linear paths)
+- Defining "same session" vs "ever"
+
+---
+
+## Regression Analysis
+
+**Use when:** Understanding what predicts an outcome, controlling for other factors.
+
+**Types:**
+- **Linear:** Continuous outcome (revenue, time spent)
+- **Logistic:** Binary outcome (churned/retained, converted/didn't)
+- **Poisson:** Count outcome (purchases, logins)
+
+**Key outputs:**
+- Coefficients (effect of each variable, holding others constant)
+- R² (how much variance is explained)
+- p-values per variable
+- Residual plots (are assumptions met?)
+
+**Watch out for:**
+- Multicollinearity (correlated predictors)
+- Omitted variable bias (missing important controls)
+- Extrapolation beyond data range
+- Causation claims from observational data
+
+---
+
+## Segmentation/Clustering
+
+**Use when:** Discovering natural groups in your data.
+
+**Techniques:**
+- **K-means:** Simple, fast, assumes spherical clusters
+- **Hierarchical:** Shows cluster relationships, good for exploration
+- **RFM:** Business-specific (Recency, Frequency, Monetary)
+
+**Process:**
+1. Select features (what defines a segment?)
+2. Normalize features (so scale doesn't dominate)
+3. Choose number of clusters (elbow method, silhouette score)
+4. Profile each cluster (what makes them different?)
+
+**Key outputs:**
+- Cluster profiles (avg values per segment)
+- Segment sizes
+- Distinguishing characteristics
+
+**Watch out for:**
+- Garbage in, garbage out (feature selection matters)
+- Cluster count is subjective
+- Stability (do clusters hold with different random seeds?)
+
+---
+
+## Anomaly Detection
+
+**Use when:** Finding unusual data points that warrant investigation.
+
+**Approaches:**
+- **Statistical:** Points beyond 2-3 standard deviations
+- **IQR method:** Below Q1-1.5×IQR or above Q3+1.5×IQR
+- **Isolation Forest:** For multivariate anomalies
+- **Domain rules:** Negative revenue, future dates, impossible values
+
+**Key outputs:**
+- Flagged records with anomaly scores
+- Context (why is this unusual?)
+- Severity (how far from normal?)
+
+**Watch out for:**
+- Seasonality (Black Friday isn't an anomaly)
+- Trends (growth makes old "normal" look like anomalies)
+- False positives (investigate before acting)
+
+---
+
+## Time Series Analysis
+
+**Use when:** Understanding patterns in data over time.
+
+**Components:**
+- **Trend:** Long-term direction
+- **Seasonality:** Repeating patterns (daily, weekly, yearly)
+- **Noise:** Random variation
+
+**Techniques:**
+- **Moving averages:** Smooth out noise
+- **Decomposition:** Separate trend, seasonal, residual
+- **Year-over-year:** Compare same period last year
+
+**Key outputs:**
+- Trend direction and strength
+- Seasonal patterns identified
+- Forecast with uncertainty bands
+
+**Watch out for:**
+- Comparing different lengths (months vary in days)
+- Holidays/events (one-time vs recurring)
+- Structural breaks (COVID, product changes)