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SkillsMP 已收集 28sungmin/m4-add-skills 中的 23 个 Skill。打开任一 Skill 可查看来源和详情。
已展示 23 / 23 个已收集 Skill。
Inspect DICOM (X-ray) header labels — verify presence/missingness of 21 clinically important header tags and compute a curation score (0-100). Use for LYDUS data quality assessment of DICOM imaging metadata before large-scale analysis.
原文语言：英语
Detect and label de-identification–target personal information (names, IDs, addresses, dates/times/periods, other hospitals) in unstructured Korean clinical notes using regular-expression rules, wrapping each match as <LABEL>string</LABEL>.…
原文语言：英语
Check the label-curation quality of GE MUSE ECG exports — verify presence/missingness of 35 ECG header attributes (3 classes) and compute a curation score (0-100), optionally rendering the 12-lead waveform. Use for LYDUS data quality assessment of ECG-derived…
原文语言：英语
Detect demographic bias in LYDUS data quality metrics by computing all 16 QUIQ quality metrics (completeness, range/date/format/sequence validity, preciseness, fidelity, class/instance diversity, sentence/vocabulary diversity, logical accuracy,…
原文语言：英语
Validate format of medical codes (ICD-9/10/11, SNOMED-CT, RxNorm, LOINC, ATC) in a QUIQ-format table. Identifies code type from variable name/description, then validates each value against the corresponding regex. Use for data quality assessment of medical…
原文语言：多语言混合
Convert MIMIC-IV clinical data into QUIQ (Quality Intelligence Unified Query) long-format table. Use when transforming MIMIC-IV tables to QUIQ format for data quality assessment, when applying QUIQ Mapping_info rules to MIMIC-IV variables, or when building a…
原文语言：英语
Detect IQR outliers (1.5×IQR rule) in numeric variables of a QUIQ-format table and compute Range Validity (% of non-outlier values). SQL version available via DuckDB PERCENTILE_CONT. No API key required. Use for LYDUS data quality assessment of numeric value…
原文语言：英语
Generate VIA (Variable Information Archive) table from a user-provided documentation file (PDF or text). Requires a data dictionary or schema documentation to produce meaningful variable descriptions. If no document is provided, the skill will ask for one…
原文语言：多语言混合
Calculate cross-sectional consistency for categorical string variables in a QUIQ-format table using Claude CLI to group semantically equivalent values. No API key required. Use for data quality assessment when the same real-world concept may be expressed in…
原文语言：多语言混合
Validate date values in a QUIQ-format table. Checks Event_date column and Mapping_info_1='date' rows using standard format parsing, Korean date formats, and optional LLM fallback. Use for data quality assessment of temporal data.
原文语言：多语言混合
Detect logical outliers in clinical variables using a multi-model ensemble (Quantile Regression + Gradient Boosting + Autoencoder for numeric; OneClassSVM + IsolationForest + Autoencoder for categorical). Uses Claude CLI (no API key required). Use for LYDUS…
原文语言：英语
Evaluate accuracy of unstructured clinical notes and radiology reports in a QUIQ-format table using Claude CLI. Detects diagnostic, procedural, drug, demographic, and date errors in clinical notes; identifies critical errors in radiology impressions. No API…
原文语言：英语
Evaluate completeness (fidelity) of unstructured clinical notes and radiology reports in a QUIQ-format table. Uses Claude CLI to check whether required template items are mentioned in each note. No API key required. Use for LYDUS data quality assessment of…
原文语言：英语
Validate temporal ordering of date variables in a QUIQ-format table. Uses Claude CLI to automatically identify start/end date variable pairs, then checks whether start_date <= end_date for each matched record. No API key required. Use for LYDUS data quality…
原文语言：多语言混合
Measure diversity of verb-containing sentences extracted from clinical notes (Mapping_info_1 contains 'note') in a QUIQ-format table. Uses NLTK POS tagging to identify sentences with verbs, then computes unique/total ratio and coverage scores. No API key…
原文语言：多语言混合
Detect temporal distribution shifts (change points) in clinical data across years using GradientBoosting classifier + SHAP. For each year, trains a model to distinguish year-1 vs year data — AUROC ≥ 0.8 indicates a change point. No API key required. Use for…
原文语言：多语言混合
Measure diversity of noun words in clinical notes using NLTK POS tagging; computes unique/total noun ratio and top-5/10/20% coverage scores. No API key required. Use for LYDUS data quality assessment of vocabulary richness in unstructured text.
原文语言：多语言混合
Calculate class diversity for categorical variables in a QUIQ-format table. Use for data quality assessment of categorical variable distributions, detecting overly homogeneous or heterogeneous value distributions, and generating per-variable diversity…
原文语言：英语
Calculate classification metrics (Accuracy, Precision, Recall, F1, AUROC) per variable in a QUIQ-format table with Ground_truth labels. Use for data quality assessment when predicted values can be compared against ground truth, generating per-variable…
原文语言：多语言混合
Calculate data completeness (non-null rate) per variable in a QUIQ-format table. Use for data quality assessment to identify variables with missing values and compute overall completeness ratio.
原文语言：多语言混合
Calculate structured fidelity for clinical variables in a QUIQ-format table. Measures how frequently each clinical concept (event, diagnosis, prescription, procedure) appears per patient. Use for data quality assessment of clinical record completeness and…
原文语言：多语言混合
Calculate instance diversity and Gini-Simpson index per (variable, value) combination in a QUIQ-format table. Measures how diversely distributed patients are across occurrences of each clinical value. Use for data quality assessment of recording pattern…
原文语言：多语言混合
Calculate numeric preciseness (decimal-place consistency) for numeric variables in a QUIQ-format table. Detects the actual decimal precision of each variable and measures last-digit distribution diversity using the Gini-Simpson index. No API key required. Use…
原文语言：英语