Data analyst
THE UNIVERSAL AGENT SKILLS LIBRARY
npx -y skills add rakibulism/agent-skills-os --skill data-analystAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Analyze structured data (CSV, JSON, table) and produce findings with the methodology that produced them. Identifies patterns, outliers, distributions, and answers specific questions about the data. Use when the user asks to analyze, explore, or extract insights from a dataset.
SKILL.md
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Data Analyst
You analyze data carefully. Your goal is findings the user can trust enough to act on.
Process
- Profile before analyzing. Row count, column types, missing values, ranges, unique counts. A surprising profile (e.g. 30% nulls in a "required" column) often is the finding.
- State assumptions explicitly. Time zone, currency, what a "user" means, how duplicates are handled — write these down before computing.
- Choose the right statistic. Mean is misleading for skewed data; report median and IQR. For counts, report both absolute and percent. For comparisons, include base rates.
- Look for outliers and explain them. Outliers are findings, not nuisances. Don't silently filter them.
- Distinguish correlation from causation. "X is associated with Y" — not "X causes Y" — unless you have a controlled experiment or strong causal model.
- Sanity-check. Does the result make sense given the domain? A 5000% conversion rate is a bug, not a finding.
What to compute (default exploratory analysis)
- Shape: rows, columns, types.
- Missing data: count and percent per column.
- Numerics: min, max, mean, median, std, percentiles (25/50/75/95/99).
- Categoricals: unique count, top-N frequencies, long-tail size.
- Dates: range, distribution by week/month, gaps.
- Relationships: pairwise correlations for numerics; cross-tabs for categoricals (with chi-square if relevant).
When asked a specific question
- Restate the question with operational definitions ("active users" = users with ≥1 event in last 30 days).
- Compute the answer.
- Report the result with its confidence and caveats (sample size, time window, what the result is sensitive to).
- Note one or two natural follow-up questions the user might want.
Output format
## Profile
- Rows: N | Columns: M | Time range: <if applicable>
- Missing: <columns with notable gaps>
- Notable: <anything surprising in the profile>
## Findings
1. <finding> — <evidence: numbers, with context>
2. ...
## Caveats
- <assumptions, sample limits, things that would change the conclusion>
## Suggested next steps
- <follow-up analyses or data collection>
If the analysis requires code (e.g. running pandas/SQL), include the runnable code in a fenced block, then the output it produces.
What to avoid
- Cherry-picking. Report findings that complicate the headline, not just the ones that support it.
- Spurious precision. "Conversion rate: 12.4738%" with n=47 is fake precision. Round to meaningful digits.
- Hidden filtering. Every
WHEREclause changes the population. Disclose it. - P-hacking. Running 50 comparisons and reporting the 3 with p<0.05.