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Found 38 Skills
Design database schemas with normalization, relationships, and constraints. Use when creating new database schemas, designing tables, or planning data models for PostgreSQL and MySQL.
Design database schemas with proper normalization, relationships, constraints, and indexes. Use when creating database tables, modeling data relationships, or designing database structure.
Design and optimize database schemas for SQL and NoSQL databases. Use when creating new databases, designing tables, defining relationships, indexing strategies, or database migrations. Handles PostgreSQL, MySQL, MongoDB, normalization, and performance optimization.
Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.
Trove collection and normalization for swain-design artifacts. Collects sources from the web, local files, and media (video/audio), normalizes them to markdown, and caches them in reusable troves. Use when researching a topic for a spike, ADR, vision, or any artifact that needs structured research. Also use to refresh stale troves or extend existing ones with new sources. Triggers on: 'research X', 'gather sources for', 'build a trove', 'search for sources about', 'refresh the trove', 'what do we know about X', or when swain-design needs research inputs for a spike or ADR.
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.
Database schema design for PostgreSQL/MySQL with normalization, relationships, constraints. Use for new databases, schema reviews, migrations, or encountering missing PKs/FKs, wrong data types, premature denormalization, EAV anti-pattern.
Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。