dataset-integrity-assessment
From the author
Audits dataset sources, labels, transformations, review, versioning, split integrity, retraining triggers, and rollback controls for poisoning and targeted degradation risk. Use when reviewing an ML training or evaluation data supply chain.
Install
npx skills add zero-lab-ai/skills --skill dataset-integrity-assessment -a claude-code
Installs to .claude/skills/dataset-integrity-assessment/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
Want your agent to suggest skills while you type? See how to use skills from your IDE.
Safety checks on SKILL.md
No warnings.
Why 85
aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.
Similar skills
Closest to this one by what they do and when they are used.
- stata-data-auditbrycewang-stanford/Auto-Empirical-Research-SkillsAudit datasets for structure, missingness, labeling, suspicious values, duplicate identifiers, and documentation readiness. Use when a researcher asks for data QA, codebook review, sanity checks, or pre-analysis cleanup guidance.Testing★ 4.8K
- Data Integrity TestingPramodDutta/qaskillsVerifying data integrity constraints, referential integrity, data type validation, and consistency checks across database operations.Testing★ 240
- retrieval-integrity-assessmentzero-lab-ai/skillsAssesses whether an authorized retrieval-augmented AI system preserves source, index, retrieval, context, and output integrity. Use when testing RAG poisoning, retrieval hijacking, context saturation, semantic substitution, or citation controls with benign canaries.Testing★ 281
- dataset-quality-auditzebbern/claude-code-guideRun comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and actionable suggestions. Triggered when users ask to check data quality, find missing or duplicate values, detect outliers, validate formats, profile data, or clean data.Data★ 4.7K
- data-source-auditdatadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionComprehensive audit of all construction data sources and systems. Map data flows, identify silos, assess quality, and create integration roadmap.Planning★ 371
- edbt-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing EDBT empirical evaluations for database-systems work, covering real workloads and datasets, fair and tuned baselines, honest measurement across realistic scales, reproducible harnesses, and the higher bar of the Experiments & Analysis paper where the measurement itself is the contribution.Data★ 1.3K
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in