Data Cleaning Protocol
$2.99OfficialSystematically clean a messy dataset with profiling, ordered fixes, validation, and a provenance log; use before analyzing or merging untrusted data.
datadata-qualityvalidationprofilingcsvprovenancepandas· by SkillingMain
What you get
- ✓10-step procedure
- ✓1 ready-to-run code block
- ✓8-point quality checklist
- ✓9 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 6 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use when handed a raw or untrusted dataset (CSV, Excel export, JSON dump, database extract) before analysis, machine learning, migration, or merging with other sources; when the same metric shows different numbers in different reports; or when a dataset must be made reproducible and auditable.
Inputs to gather
- The raw file(s), their encoding, and delimiter; a data dictionary or expected schema if one exists.
- Candidate primary key(s) and which columns must never be null.
- Downstream purpose - analytics, ML training, or system migration - because it decides how aggressive imputation and row-dropping may be.
- Which source wins when records conflict.
- Whether dropping
…
🔒 Buy once ($2.99) to unlock the full playbook, download it, and install it in every tool you use.