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Skillsclean-data

clean-data

Data & analytics

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Updated 10-15 16:51

About this skill

Data Profiling and Cleaning Skill

Profile, flag, and generate cleaning code for a clinical dataset in three stages, each ending at a user-approval gate. You generate code and reports; you do NOT auto-clean data. Every cleaning decision needs the researcher's explicit confirmation, because clinical cleaning calls need domain knowledge.

PHI first. If the dataset contains PHI or PII, run /deidentify before proceeding, and use *_deidentified.* files when they exist in the working directory. Otherwise work from the data dictionary / codebook alone, or in a local-only environment with no network access. The skill writes code that runs on the data; it does not need to see the raw rows to write it.

Three-Stage Workflow

Stage 1: Profiling

Input: CSV/Excel file path OR data dictionary/codebook

  1. Adapt ${CLAUDE_SKILL_DIR}/references/profiling_template.py (pandas) to the dataset. It reports: variable and row counts, data types; missing count and percentage per variable; unique counts for categorical variables; min/max/mean/median/SD for numeric variables; histograms and bar charts.
  2. If the user provides a codebook, cross-reference variable names, expected types, and expected ranges. Never guess a column name or coding: when a mapping is uncertain, write [VERIFY: variable_name] and ask the user to confirm it against the data dictionary.
  3. Present the summary (see Output Format). Every count and statistic comes from the executed script's output.

Gate: The user reviews the profile. Ask whether to proceed to Stage 2 (Flagging) and whether any variables should be excluded or focused on.

Stage 2: Flagging

Read ${CLAUDE_SKILL_DIR}/references/cleaning_patterns.md for missing-data mechanisms, outlier decision rules, duplicate detection, date handling, and clinical pitfalls (inequality-prefixed lab values, mixed units, sentinel values). Flag issues in these categories:

  1. Missing values: variables with >5% missing; pattern analysis (MCAR/MAR/MNAR heuristic).
  2. Statistical outliers: IQR method (Q1 - 1.5IQR, Q3 + 1.5IQR) and Z-score (|z| > 3).
  3. Duplicates: exact row duplicates AND near-duplicates (same patient ID, different dates).
  4. Type mismatches: numeric stored as string, dates in inconsistent formats.
  5. Implausible values: flag against the codebook's valid range or, when the codebook is silent, the domain-default hard physiologic bounds in references/implausible_value_rules.md §1. An implausible value is a likely data-entry/unit/sentinel error (correct or set missing); a statistical outlier (#2) is biologically possible (keep + sensitivity analysis). Check units before calling a bound violation an error. Never auto-fix. 5b. Cross-field inconsistencies: logical contradictions per references/implausible_value_rules.md §2 — temporal ordering (birth ≤ event ≤ death, admission ≤ discharge), derived-vs-source (recomputed BMI/age; subset ≤ superset; total = sum of parts), sex-/state-specific fields, and min ≤ max / diastolic < systolic pairs. Name the rule that fired; a hard contradiction is High severity.
  6. Category inconsistencies: typos and variants in categorical values ("Male", "male", "M", "MALE").
  7. Categorical-implied zeros: when a category defines a natural zero for a dose/duration variable (smoking_status == 'never' implies pack_years == 0; alcohol_use == 'never' implies grams_per_week == 0), flag records that store the implied zero as NULL. This is a contradiction, not a missing-data pattern: complete-case models silently drop those never-smokers and MICE imputes them a non-zero dose, corrupting the exposure contrast. Suggested action: "Set dose = 0 where category == reference level; impute only the residual missingness among the exposed." Detected by scripts/check_structural_zero.py given the category↔dose mapping; pairs with /analyze-stats "Covariate Pitfalls: Structural Zeros & Dose/Duration Variables".
  8. Reverse-coded scale items: in a multi-item Likert scale that mixes positively and negatively worded items, every reverse item must be recoded (min+max) - x before the scale total or Cronbach's alpha is computed. An un-recoded reverse item correlates negatively with the rest and collapses alpha, often to a negative value. A negative alpha is a reverse-coding bug, not "multidimensional structure". Suggested action: "Recode reverse-worded items, then recompute reliability." Detected by scripts/check_reverse_coding.py (negative item-rest correlation and negative raw alpha, given the scale item columns); the recode itself is applied downstream by /analyze-stats likert_summary.py --reverse-items.

Present the flag report as a table:

Variable Issue Type Count Severity Suggested Action
age Outlier (IQR) 3 Medium Review: values 150, 200, -5
pack_years Categorical-implied zero 12421 High Set 0 where smoking_status=='never' (structural zero, not missing)

Severity levels:

  • High: likely data errors that will affect analysis (type mismatches, impossible values)
  • Medium: potential issues that need expert review (statistical outliers, moderate missingness)
  • Low: minor inconsistencies that are easy to fix (category labels, trailing whitespace)

Gate: The user marks each row (A) Approve the suggested action, (R) Reject / keep as-is, or (M) Modify the action. Only approved actions generate cleaning code.

Stage 3: Code Generation

For ONLY user-approved actions, generate Python (or R if requested) code:

  • Missing value handling: listwise deletion, mean/median imputation, or MICE setup (code only, the user runs it and picks the variables and method)
  • Outlier handling: winsorization, removal, or keep-and-flag
  • Duplicate removal: exact dedup with logging
  • Type conversion: standardize dates, numeric parsing
  • Category harmonization: mapping table for inconsistent labels

All generated code MUST include:

  • Before/after row counts printed to console
  • Logging of every modification to a cleaning log DataFrame
  • Reproducibility: np.random.seed(42) and random.seed(42) where applicable
  • Output: cleaned CSV (a new file; the input file is never overwritten) + cleaning_log.csv
  • Clear comments explaining each cleaning step

End the generated script with this notice:

"This code implements ONLY the cleaning rules you approved. Review the cleaning_log.csv output to verify all changes before proceeding to analysis."

Out of scope: free-text extraction from clinical notes, and image data or DICOM metadata. After cleaning, hand off to /analyze-stats. Take any citation from /search-lit, never from memory.

Output Format

Structure all reports using this template:

## Data Profiling Report

### Dataset Overview
- Rows: [N]
- Columns: [N]
- File size: [size]
- Date range: [if applicable]

### Variable Summary
| Variable | Type | Missing N (%) | Unique | Min | Max | Mean | SD |
|----------|------|---------------|--------|-----|-----|------|-----|
| ...      | ...  | ...           | ...    | ... | ... | ...  | ... |

### Flags
| Variable | Issue | Count | Severity | Suggested Action |
|----------|-------|-------|----------|-----------------|
| ...      | ...   | ...   | ...      | ...             |

### Cleaning Code
[Python/R script -- only for approved actions]

### Cleaning Log
[What was changed, how many rows affected, before/after counts]