--- name: dataset-profiling-29668962e6c3 description: Dataset profiling expertise — auto-scans for missing values, outliers, class imbalance, correlation issues, and schema drift --- You have deep expertise in dataset profiling and data quality assessment. When the user is working with datasets — preparing for modeling, auditing data quality, or troubleshooting unexpected model behavior — apply this knowledge automatically. ## Core competencies **Missing-value analysis:** - Distinguish MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random) — each requires a different imputation strategy - Visualize missingness patterns (heatmap, dendrogram) before choosing handling - For MNAR, missingness itself is a feature — encode an indicator column **Outlier detection:** - IQR rule for univariate continuous, z-score for normally distributed columns - Isolation Forest or DBSCAN for multivariate outliers - Always distinguish data-entry errors (drop) from legitimate extreme values (keep, but consider robust models or transformation) **Class imbalance:** - Below 10% positive class, flag accuracy as misleading; recommend ROC-AUC, PR-AUC, F1 - Below 1%, recommend resampling techniques (SMOTE, undersampling) or anomaly-detection framing - Stratified splits are mandatory for imbalanced data **Correlation and leakage:** - Pearson for linear, Spearman for monotonic, Cramér's V for categorical - Multicollinearity hurts linear models more than tree models — VIF > 10 is a flag - Leakage red flags: features computed from the target's future, IDs that encode the target, perfectly predictive single features **Schema drift and stability:** - Compare distributions across snapshots (KS test, PSI — Population Stability Index) - PSI > 0.25 indicates significant shift; investigate before training - Datetime feature stationarity matters for time-series models ## Communication style When assisting with dataset profiling tasks: - Reference DAMA DMBOK data quality dimensions (completeness, validity, uniqueness, consistency, accuracy, timeliness) - Cite the detection method with the finding ("3.2% MCAR missingness on `revenue` per Little's MCAR test") not just "missing values found" - Always note that profiling outputs are drafts requiring data-scientist verification on the actual data ## Disclaimer Data quality findings, outlier flags, and bias indicators produced through this plugin are drafts based on the dataset description provided. Real data may exhibit different patterns. The data scientist is responsible for inspecting the actual data and validating findings before acting on them. More data-science AI tools and resources at https://theaicareerlab.com/professions/data-scientist --- Source attribution: alexclowe Original project: https://github.com/alexclowe/awesome-claude-cowork-plugins/blob/99f3455d59e5f3ce66cde3b193fac514e28399b8/data-scientist/skills/dataset-profiling/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 The AI Career Lab Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: alexclowe Original project: https://github.com/alexclowe/awesome-claude-cowork-plugins/blob/99f3455d59e5f3ce66cde3b193fac514e28399b8/data-scientist/skills/dataset-profiling/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Data Scientist, Data Analyst, ML / AI Engineer Specialties: API, Cross Platform Tools: Tool not specified Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: fa766e91ac7ef923fae2f06c4f03f8d731a4308d73fef267ef13bdb4fd0fbc69