Databases Skills

Use for bun:sqlite, SQLite operations, prepared statements, transactions, and queries.

Cloudflare D1 serverless SQLite on edge. Use for databases, migrations, bindings, or encountering D1_ERROR, statement too long, too many requests queued errors.

Cloudflare Hyperdrive for Workers-to-database connections with pooling and caching. Use for PostgreSQL/MySQL, Drizzle/Prisma, or encountering pool errors, TLS issues, connection refused.

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.

Database sharding for PostgreSQL/MySQL with hash/range/directory strategies. Use for horizontal scaling, multi-tenant isolation, billions of records, or encountering wrong shard keys, hotspots, cross-shard transactions, rebalancing issues.

| drizzle kit, orm cloudflare, d1 orm, drizzle typescript, drizzle relations, drizzle transactions, drizzle query builder, schema definition, prepared statements, drizzle batch, migration management, relational queries, drizzle joins, D1_ERROR, BEGIN TRANSACTION d1, foreign key constraint, migration failed, schema not found, d1 binding error, schema design, database indexes, soft deletes, uuid primary keys, enum constraints, performance optimization, naming conventions, schema testing

FastMCP Python framework for MCP servers with tools, resources, storage backends (memory/disk/Redis/DynamoDB). Use for Claude tool exposure, OAuth Proxy, cloud deployment, or encountering storage, lifespan, middleware, circular import, async errors.

Answer a question using the compiled wiki knowledge base, synthesising a response with [[wikilink]] citations. Always use this skill when the user says /wiki-query, 'what does my wiki say about', 'what do I know about', 'check my notes on', 'search my wiki for', or 'what's the current state of [topic] in my notes'. Also use when the user asks a question and signals they want to draw on their own accumulated knowledge rather than general knowledge - phrases like 'what have we worked on regarding', 'based on my notes', 'what's my thinking on', or any question where the answer should come from personal knowledge rather than the model. Optionally files valuable answers as new wiki pages. When in doubt whether personal knowledge retrieval is being requested, use this skill. Requires filesystem read access.

>- Use when verifying information (fact, number, quote, event, statement) against authoritative primary sources, or cross-checking a number via true", "find the original source", "where is this number from", "two sources truthfulness check, (2) completeness / out-of-context quoting, (3) one-level reasoning verification, (4) negative-statement handling, (5) multi-source conflict side-by-side output. Dig into whitelisted primary sources only (user-supplied files, official websites & databases, authoritative industry sources); cited reports / charts / datasets must be downloaded and read locally to count as verified — if download is blocked, hand the link to the user. If nothing can be found, plainly state "cannot verify" rather than guessing, patching, or citing secondary paraphrases. Always reply in the user's question language.

Proactively suggest diagrams when explaining complex systems. Triggers on diagrams, charts, visualizations, flowcharts, sequence diagrams, architecture diagrams, ER diagrams, state machines, Gantt charts, mindmaps, C4, class diagrams, git graphs, kanban boards, sankey, timelines, quadrant charts, XY charts, packet diagrams. Use when user asks for visual representations of code, systems, processes, data structures, database schemas, workflows, or API flows. Generate Mermaid diagrams in markdown.

Proactively apply when creating APIs, backends, or data models. Triggers on PostgreSQL, Postgres, Drizzle, drizzle-orm, drizzle-kit, database, schema, pgTable, tables, columns, indexes, queries, migrations, ORM, relations, relational queries, joins, transactions, SQL, connection pooling, PgBouncer, N+1, JSONB, RLS, full-text search, partitioning. Use when writing database schemas, queries, migrations, connection setup, or any database-related code. PostgreSQL and Drizzle ORM best practices.

Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.

| PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector indexing, hybrid FTS/BM25 + RRF, ParadeDB, reranking, halfvec, multilingual search, query translation, and domain evals. embedding search, PostgreSQL RAG, BM25, RRF, HNSW, IVFFlat, ParadeDB, pg_search, reranking, iterative_scan, filtered HNSW, halfvec, websearch_to_tsquery, unaccent, multilingual FTS, pg_trgm, trigram, fuzzy search, ILIKE, autocomplete, typo tolerance, fuzzystrmatch, Hit@K, MRR, retrieval evals, cross-lingual retrieval, non-English corpus, per-language indexing, query translation For general Postgres schema, index, RLS or query tuning unrelated to retrieval, use supabase-postgres-best-practices instead.

> Diagnose why a data warehouse sync is failing and recommend the right recovery action. Use when the user asks "why isn't my Stripe/Postgres/Hubspot sync working?", "this table has been stuck for hours", "the data in the warehouse looks wrong", or wants to troubleshoot a specific source or schema. Covers source-level vs schema-level failures, stuck Running states, credential and schema-drift errors, incremental-field misconfig, CDC prerequisite failures, and the cancel / reload / resync / delete-data recovery actions.

> Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, trace IDs, or span

Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.

> Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.

Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, production database tables, or other data that PostHog does not collect natively. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, etc.), production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.

Query 40+ bioinformatics web services through one consistent Python API with bioservices (UniProt, KEGG, ChEMBL, Reactome, Ensembl, NCBI and more). Use when a workflow must hit multiple databases together, map identifiers across services, or run cross-database analyses — for quick single-database lookups use gget, for sequence and file manipulation use biopython. Part of the AlterLab Academic Skills suite.

Runs NCBI BLAST+ 2.17.0 sequence searches from the command line: makeblastdb (with -parse_seqids), blastn/blastp/blastx/tblastn with tabular -outfmt 6/7 for parsing, correct -task choice (megablast vs blastn vs blastn-short), -taxids/-negative_taxids taxonomic scoping, and -mt_mode multithreading; plus a DIAMOND blastp --ultra-sensitive path for large protein searches. Warns that -max_target_seqs is a heuristic keep-count, not a top-N best-hits filter. Use when the user wants command-line BLAST, makeblastdb, a local BLAST database, blastn/blastp/blastx/tblastn searches, or DIAMOND protein search. For the Bio.Blast web NCBIWWW API prefer alterlab-biopython; for quick one-liner database lookups prefer alterlab-gget. Part of the AlterLab Academic Skills suite.

Manipulate biological sequences, parse FASTA/GenBank/PDB files, run phylogenetics, and access NCBI/PubMed programmatically via Biopython (Bio.SeqIO, Bio.Entrez, Bio.PDB, Bio.Blast). Use when scripting custom bioinformatics pipelines, batch-processing sequence files, automating BLAST, or fetching records from Entrez — for quick one-off database lookups use gget, for unified multi-service integration use bioservices. Part of the AlterLab Academic Skills suite.

Query the Ensembl genome database REST API across 250+ species for gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, and Variant Effect Predictor (VEP) annotations. Use when mapping gene IDs or coordinates, fetching genomic sequence, finding orthologs across species, or predicting variant consequences for genomic research. Part of the AlterLab Academic Skills suite.

Query gnomAD (Genome Aggregation Database) for population allele frequencies and gene constraint scores (pLI, LOEUF) reflecting loss-of-function intolerance. Use when checking how common a variant is across populations, filtering rare-disease candidate variants, assessing variant pathogenicity, or identifying loss-of-function intolerant genes. Part of the AlterLab Academic Skills suite.

Access the Human Metabolome Database (HMDB, 220K+ metabolites), searching by name, HMDB ID, or structure to retrieve chemical properties, biomarker data, NMR/MS reference spectra, and associated pathways. Use when identifying a human metabolite, looking up its biomarker or disease associations, matching NMR/MS spectra, or running metabolomics annotation. Part of the AlterLab Academic Skills suite.

Query the EMBL-EBI InterPro REST API for protein family, domain, and functional-site annotations integrated from member databases (Pfam, PANTHER, PRINTS, SMART, SUPERFAMILY, CDD, ProSite, NCBIfam, and others). Use when predicting protein function, analyzing or comparing domain architecture, classifying a protein by family or homologous superfamily, resolving a Pfam/InterPro accession, or mapping a protein's signatures to GO terms. Not for raw UniProt entry/FASTA retrieval or AlphaFold 3D structures. Part of the AlterLab Academic Skills suite.

Provide direct REST API access to KEGG (academic use only) for pathway analysis, gene-to-pathway and compound-to-pathway mapping, metabolic reactions, KEGG Orthology (KO), drug-drug interactions, and ID conversion. Use when querying KEGG pathways, mapping genes/compounds to metabolic maps, or running KEGG pathway enrichment via raw HTTP/REST; for protein-protein interaction networks prefer alterlab-string-db, for protein sequences and annotations prefer alterlab-uniprot, and for Python workflows spanning many databases prefer bioservices instead. Part of the AlterLab Academic Skills suite.

Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations across species, integrating OMIM, ORPHANET, HPO, ClinVar, and model organism databases. Use when discovering rare disease genes, mapping phenotypes to genes, modeling disease across species, or looking up HPO terms. Part of the AlterLab Academic Skills suite.

Access the ZINC database of 230M+ commercially available (purchasable) compounds, searching by ZINC ID or SMILES, running similarity searches, and downloading 3D-ready structures. Use when assembling a compound library for virtual screening, finding purchasable analogs, or obtaining docking-ready 3D structures for drug discovery. Part of the AlterLab Academic Skills suite.

Analyzes and manipulates materials with the pymatgen toolkit — crystal structures and molecules, phase diagrams and thermodynamic stability, electronic structure (band structures, DOS), surfaces and interfaces, and Materials Project database access. Use when working with crystal structures in materials science, converting between structure formats (CIF, POSCAR, XYZ), analyzing symmetry or space groups, computing phase diagrams, querying the Materials Project API, or handling VASP, Gaussian, or Quantum ESPRESSO output. Part of the AlterLab Academic Skills suite.

Simple Pomodoro timer for focused work sessions with session tracking and productivity analytics. Use when users request focus timers, ask about productivity patterns, or want to track work sessions over time. Demonstrates the System Skill Pattern (CLI + SKILL.md + Database).

Generates service-specific configuration files for GitLab stack projects in ./config directory, using .env as the primary configuration source. Creates nginx, PostgreSQL, Redis, and custom service configs with strict validation for secrets, paths, and Docker best practices. Use when setting up service configurations, creating config templates, or ensuring configs follow stack patterns.

Connect to and query WRDS (Wharton Research Data Services) from any research project. Use this skill whenever the user needs to download, query, or explore data from WRDS — including Compustat, CRSP, FactSet, I/B/E/S, or any other WRDS-hosted database. Also trigger when the user mentions WRDS tables, WRDS libraries, or wants to look up variable definitions or coverage in WRDS datasets. Do NOT trigger for general SQL or database questions unrelated to WRDS.

DuckDB SQL reference for MotherDuck. Use when you need exact DuckDB syntax or function behavior, friendly SQL features like QUALIFY, GROUP BY ALL, or list/struct types, MotherDuck-specific SQL such as shares, secrets, snapshots, or UNDROP, or to fix SQL errors and PostgreSQL-style SQL that fails on MotherDuck.

Connect to MotherDuck from any application. Use when setting up database connectivity via the Postgres endpoint (recommended), pg_duckdb, native DuckDB API, or JDBC. Covers connection strings, authentication, SSL, and environment variable configuration.

Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or transformations on a cron, or operate flights with get_flight_guide, create_flight, run_flight, flight logs, secrets, schedules, and versions.

Design and build database schemas and data models in MotherDuck. Produces a file-based SQL project scaffold with a model manifest. Use for any schema design or data modeling task — creating tables, choosing data types, star schemas, wide denormalized tables, raw/staging/analytics layers, dbt-style transformation projects, or restructuring data for analytics workloads.

Load and ingest data into MotherDuck from local files, object storage (S3, GCS, Azure, R2), HTTPS, dataframes, or external databases. Use for any import or bulk-load task — CSV, Parquet, JSON, Delta, Iceberg, local DuckDB database upload — and for choosing between CTAS, INSERT...SELECT, COPY, cloud-storage secrets, and Postgres-endpoint versus native DuckDB-client paths.

Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.

Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.

Execute DuckDB SQL queries against MotherDuck databases. Use when running analytics, aggregations, transformations, or any SQL operation. Covers query best practices, CTEs, window functions, QUALIFY, and performance optimization.

Create and manage MotherDuck data shares for zero-copy, read-only data distribution. Use whenever someone wants to share a database with team members, another organization, or the public — covers CREATE SHARE, access/visibility/update modes, GRANT READ ON SHARE, attaching share URLs, UPDATE SHARE, and REFRESH DATABASE.

Connect to MotherDuck from any application. Use when setting up database connectivity via the Postgres endpoint (recommended), pg_duckdb, native DuckDB API, or JDBC. Covers connection strings, authentication, SSL, and environment variable configuration.

Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or transformations on a cron, or operate flights with get_flight_guide, create_flight, run_flight, flight logs, secrets, schedules, and versions.

DuckDB SQL reference for MotherDuck. Use when you need exact DuckDB syntax or function behavior, friendly SQL features like QUALIFY, GROUP BY ALL, or list/struct types, MotherDuck-specific SQL such as shares, secrets, snapshots, or UNDROP, or to fix SQL errors and PostgreSQL-style SQL that fails on MotherDuck.

Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.

Load and ingest data into MotherDuck from local files, object storage (S3, GCS, Azure, R2), HTTPS, dataframes, or external databases. Use for any import or bulk-load task — CSV, Parquet, JSON, Delta, Iceberg, local DuckDB database upload — and for choosing between CTAS, INSERT...SELECT, COPY, cloud-storage secrets, and Postgres-endpoint versus native DuckDB-client paths.

Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.