Individual skill
Turn messy datasets into auditable reports with data-master
Use data-master to preserve raw tabular data, hold ambiguous cleaning decisions for review, run unit-aware statistics and sensitivity checks, and produce Markdown, HTML and PDF reports with machine-verifiable audit evidence.
skills/turn-messy-datasets-into-auditable-reports-with-data-master/SKILL.md
Source commit fc97377adefd53e861c1d456aa1392e4a4e022a2
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Not checked yet
- Last checked
- Nov 6, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
Standard SKILL.md format has not been attested at this commit. No installation command is available; inspect the source.
Repository context
- Repository stars (not skill ratings)
- 62
- Repository last updated
- Nov 9, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- cursor Repository topic
- npx-skills Repository topic
- openclaw Repository topic
- ai-tools Repository topic
- mcp Repository topic
- awesome-list Repository topic
- data Derived category
- security Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 2,487 bytes
Read excerpt
# Turn messy datasets into auditable reports with data-master
Use data-master when a CSV, TSV, Excel, JSON, JSONL or Parquet analysis must be
reproducible and reviewable rather than a one-off notebook. The tool reads
potentially destructive fields as text first, records every cleaning decision,
and refuses to guess when values could have more than one valid meaning, such as
zero-padded identifiers or compact values that could be dates or integers. It
supports declared independent units, repeated measures, paired comparisons,
covariate-adjusted comparisons, sensitivity analysis and descriptive time-series
work. A completed run includes cleaned data, a replayable cleaning program,
machine-readable statistics, charts, Markdown, self-contained HTML, PDF and a
separate audit verdict. Use the saved verification command before relying on a
delivered report. Treat unresolved holds and failed audit gates as questions for
the dataset owner; do not silently replace them with weaker assumptions.