data-analysis
Complete data-analysis tasks with bounded inspection, correct data semantics, native artifact handling, complete delivery, and risk-based verification.
npx skills add Prism-Shadow/penguin-harness --skill data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Analysis Deliver the requested result and artifacts. Do not turn the task into a proof exercise or add evidence, reports, explanations, or intermediate files that were not requested. ## Before you start Require a concrete data-analysis task, its available inputs, and the requested deliverable, location, and format. Ask only when missing information prevents a defensible result and would materially change the deliverable; otherwise proceed. ## Contract Read the task, supplied inputs, and relevant data documentation. Identify every required output path and format, plus only the definitions that can change the result: scope, observation grain, keys, units, operators, ordering, coverage, and explicit formatting rules. Treat examples as illustrative unless the task makes them normative. If information is incomplete or ambiguous, first resolve it from the supplied materials. Ask only when the missing choice prevents a defensible result and would materially change the deliverable. Otherwise choose the best-supported interpretation and proceed. ## Bounded inspection For large or unfamiliar inputs, begin with a bounded inventory, schema check, targeted sample, or narrow query. Expand
- Before you start
- Contract
- Bounded inspection
- Data semantics
- Native artifacts
- Delivery
- Verification
- Final check
What does the data-analysis skill do?
Complete data-analysis tasks with bounded inspection, correct data semantics, native artifact handling, complete delivery, and risk-based verification.
How do I install it?
Run `npx skills add Prism-Shadow/penguin-harness --skill data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Prism-Shadow/penguin-harness, a repository with 473 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
