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Found 38 Skills
Generate ASCII mini charts (sparkline/bar/simple line) for plain-text trend inspection, with minimal + annotated variants and normalization notes.
SQL and NoSQL schema design with normalization, indexing, and migration patterns. Use when designing database schemas, creating tables, optimizing slow queries, or planning database migrations.
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
Statistical scoring with z-scores, percentiles, freshness decay, and cross-category normalization. Rank and compare items with confidence scoring.
Batch processing for Obsidian vaults: bulk tag normalization, wikilink extraction/fixing, frontmatter edits, vault analysis, and migration workflows. Use when asked to analyze or modify many notes in an Obsidian vault at scale, or to script/automate vault-wide changes.
Use when writing tests for serialization, validation, normalization, or pure functions - provides property catalog, pattern detection, and library reference for property-based testing
Use when designing databases for data-heavy applications, making schema decisions for performance, choosing between normalization and denormalization, selecting storage/indexing strategies, planning for scale, or evaluating OLTP vs OLAP trade-offs. Also use when encountering N+1 queries, ORM issues, or concurrency problems.
Use this skill when designing database schemas for relational (SQL) or document (NoSQL) databases. Provides normalization guidelines, indexing strategies, migration patterns, and performance optimization techniques. Ensures scalable, maintainable, and performant data models.
Load PROACTIVELY when task involves database design, schemas, or data access. Use when user says "set up the database", "create a schema", "add a migration", "write a query", or "set up Prisma". Covers schema design and normalization, ORM setup (Prisma, Drizzle), migration workflows, connection pooling, query optimization, indexing strategies, seeding, and transaction patterns for PostgreSQL, MySQL, SQLite, and MongoDB.
Database design specialist for schema modeling, query optimization, indexing strategies, and data integrityUse when "database design, schema, indexes, query optimization, migrations, normalization, database scaling, foreign keys, data modeling, database, sql, postgres, mysql, mongodb, schema, indexes, migrations, normalization, optimization" mentioned.
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Dimensional modeling and schema design for data products. Star schema patterns, slowly changing dimensions, denormalization decisions, and architecture decision records. Use when designing data models, reviewing schema designs, choosing between normalization strategies, or when someone asks "how should I model this data?" or "should I denormalize?" For OMOP CDM patterns specifically, see healthcare-data-domain.