Database Claude Skills
1 639 database skills from 232 authors. They work on schemas, queries and moving data between them. Half of them fit into 2 210 tokens or less — that is what one costs your context window when the agent loads it. 286 ship runnable scripts rather than instructions alone. 7 of them cannot work without an MCP server, most often rube. We also found 387 copies of these same skills sitting in other people's repositories — counted once here, not 387 times.
1 639 unique 232 authors 5 updated this month 163 from vendors
> Query the OREGANO knowledge graph for computational drug repurposing. Use whenever the user asks about drug–target–disease–gene–pathway relationships, compound cross-references, drug repurposing hypotheses, or wants to explore neighbors of any biomedical entity in a knowledge graph that includes natural compounds.
> Query the PharmKG knowledge graph (180k entities, 39 relation types, >1M triples). Use whenever the user asks about biomedical relationships among genes, drugs/chemicals, and diseases — e.g. drug–gene interactions, drug–disease associations, gene–disease links, or drug–drug relationships derived from literature and curated databases.
> Query the PHEE pharmacovigilance event extraction dataset. Use whenever the user asks about annotated adverse drug events, pharmacovigilance case reports, drug–effect associations from medical literature, or wants to find PHEE examples mentioning a drug name, adverse effect, or condition.
> Query the PsyTAR psychiatric adverse-reaction corpus. Use when the user asks about patient-reported ADRs, withdrawal symptoms, drug indications, or effectiveness for Zoloft, Lexapro, Cymbalta, or Effexor XR. Accepts drug names (brand or generic), symptom terms, or UMLS CUIs.
> Query the RepoDB drug repurposing database. Use whenever the user asks about drug-disease associations, drug repurposing candidates, or wants to look up any entity (drug name, indication, DrugBank ID, UMLS CUI, NCT ID) in RepoDB.
> Query the RepurposeDrugs single-agent drug repurposing database. Use whenever the user asks about drug-disease repurposing associations, clinical trial phases for repurposed drugs, or wants to look up any entity (drug name, disease name, NCT ID) in RepurposeDrugs.
> Query the RxNorm drug naming and normalization API. Use whenever the user asks to look up an RxCUI, normalize a drug name, find drug interactions, retrieve brand/trade names, or resolve any clinical drug name via RxNorm. Supports single drug or batch queries. Trigger on mentions of RxNorm, RxCUI, drug normalization, drug interaction lookup, or brand-name resolution.
> Query the STITCH chemical-protein interaction database. Use whenever the user asks about chemical-protein interactions, drug-target binding, compound action modes, or wants to look up any entity (chemical name, STITCH CID, STRING protein ID) in STITCH.
> Query the Therapeutic Target Database (TTD) for drug-target-disease interaction data. Use this skill when the user asks about therapeutic targets, drugs, diseases, or their relationships — including target-drug mappings, clinical status of drugs, disease indications, UniProt/gene associations, and pathway annotations. Triggers on queries like "what drugs target EGFR", "which diseases is Imatinib used for", "find targets for lung cancer", or any lookup involving TTD IDs, gene symbols, drug names, or disease names.
> Query the UniTox drug toxicity database. Use whenever the user asks about organ-system toxicity ratings for a drug, multi-organ toxicity profiles, or wants to look up any entity (drug name, SMILES, SPL_ID) in UniTox.
> Query the WHO Model List of Essential Medicines (23rd list, 2023). Use whenever the user asks about essential medicines, WHO-recommended drugs, dosage forms, therapeutic sections, or AWaRe antibiotic classification.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs. Covers physics/astronomy (NASA, NIST, SDSS, SIMBAD), earth/environment (USGS, NOAA, EPA), chemistry/drugs (PubChem, ChEMBL, DrugBank, FDA, KEGG, ZINC, BindingDB), materials (Materials Project, COD), biology/genomics (Reactome, UniProt, STRING, Ensembl, NCBI Gene, GEO, GTEx, PDB, AlphaFold, InterPro, BioGRID, Gene Ontology, dbSNP, gnomAD, ENCODE, Human Protein Atlas, Human Cell Atlas), disease/clinical (COSMIC, Open Targets, ClinicalTrials.gov, OMIM, ClinVar, GDC/TCGA, cBioPortal, DisGeNET, GWAS Catalog), regulatory (FDA, USPTO, SEC EDGAR), economics/finance (FRED, World Bank, US Treasury), demographics (US Census, Eurostat, WHO). Use when looking up compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, or any public database API query.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
> CHARLS (China Health and Retirement Longitudinal Study) database-specific knowledge for reproducing published papers. Use when reproducing or analyzing papers that use CHARLS data, including variable mapping from harmonized to raw questionnaire items, cognitive function scoring (episodic memory, mental status, TICS), CESD-10 depression screening, social isolation index construction, and chronic disease coding. Also use for any CHARLS data cleaning, variable construction, or cohort selection task.
Search ChEMBL bioactive molecules database with natural language queries. Find compounds and assay data with Valyu semantic search.
Search DrugBank comprehensive drug database with natural language queries. Drug mechanisms, interactions, and safety data powered by Valyu.
Gene set enrichment analysis with correct geneset format handling. Critical guidance for loading pathway databases and running enrichment in OmicVerse.
Provide comprehensive clinical interpretation of somatic mutations in cancer. Given a gene symbol + variant (e.g., EGFR L858R, BRAF V600E) and optional cancer type, performs multi-database analysis covering clinical evidence (CIViC), mutation prevalence (cBioPortal), therapeutic associations (OpenTargets, ChEMBL, FDA), resistance mechanisms, clinical trials, prognostic impact, and pathway context. Generates an evidence-graded markdown report with actionable recommendations for precision oncology. Use when oncologists, molecular tumor boards, or researchers ask about treatment options for specific cancer mutations, resistance mechanisms, or clinical trial matching.
Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".
Comprehensive metabolomics research skill for identifying metabolites, analyzing studies, and searching metabolomics databases. Integrates HMDB (220k+ metabolites), MetaboLights, Metabolomics Workbench, and PubChem. Use when asked to identify or annotate metabolites (HMDB IDs, chemical properties, pathways), retrieve metabolomics study information from MetaboLights (MTBLS*) or Metabolomics Workbench (ST*), search for studies by keywords or disease, or generate comprehensive metabolomics research reports.
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
Retrieves biological sequences (DNA, RNA, protein) from NCBI and ENA with gene disambiguation, accession type handling, and comprehensive sequence profiles. Creates detailed reports with sequence metadata, cross-database references, and download options. Use when users need nucleotide sequences, protein sequences, genome data, or mention GenBank, RefSeq, EMBL accessions.
Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.
| Skills for querying and downloading data from genomic, transcriptomic, 3D-genome, and cancer-genomics databases. Covers programmatic access to public repositories, gene annotation, sequence retrieval, processed functional-genomics tracks, Hi-C / Micro-C contact matrices, TCGA-style cohorts, and large-scale single-cell data.
> pathways, ChEMBL/ChEBI/PubChem, BLAST, cross-database ID mapping, GO annotations, PPI. For deep single-DB queries use dedicated tools (gget for Ensembl, pubchempy for PubChem); bioservices excels at cross-database workflows.
Cancer genomics (TCGA et al.) via cBioPortal REST API. Retrieve somatic mutations, CNAs, expression, clinical data (survival/stage/treatment) across thousands of studies. Use for TMB, oncoprints, survival analysis. For population frequencies use gnomad-database; for drug-gene interactions use dgidb-database.
Query PubChem (110M+ compounds) directly via the PUG-REST/JSON API with plain `requests` — no SDK install required. Search by name/CID/SMILES/InChIKey/formula, retrieve properties (MW, XLogP, TPSA, H-bond counts), do similarity/substructure searches with async ListKey polling, fetch synonyms, descriptions, assay summaries, and download SDF/PNG. For local cheminformatics use rdkit; for bioactivity-centric workflows use chembl-database-bioactivity.
Query UniProt REST API: search by gene/protein name, fetch FASTA, map IDs (Ensembl, PDB, RefSeq), access Swiss-Prot annotations. Use bioservices for multi-DB access; alphafold-database for structures.
Log all chat messages to a SQLite database for searchable history and audit. Use when: (1) Building chat history, (2) Auditing conversations, (3) Searching past messages, or (4) User asks to log chats.
Query SQLite, PostgreSQL, and MySQL databases and export results to CSV/JSON. Use when: (1) Extracting data for reports, (2) Database backup and migration, (3) Data analysis workflows, or (4) Automated database queries.
Log all file changes (write, edit, delete) to a SQLite database for debugging and audit. Use when: (1) Tracking code changes, (2) Debugging issues, (3) Auditing file modifications, or (4) The user asks to track file changes.
>- Distributed traces, spans, service dependencies, and request flow analysis. Use when investigating span-level details, failures, performance bottlenecks, or trace correlation. "distributed trace", "span details", "HTTP status codes in traces", "database query spans", "messaging spans", "gRPC calls", "Lambda cold starts", "trace ID lookup", "exception analysis", "correlate logs and traces", "request attributes". Do NOT use for explaining existing queries, product documentation or configuration questions, service-level RED metrics (use dt-obs-services), log searching (use dt-obs-logs), or problem analysis (use dt-obs-problems).
> Odoo 17 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 17 or custom addons—even if the user only pastes a traceback, mentions addons/ or __manifest__.py, describes form/tree/kanban/XML errors, HTTP controllers, or business rules on models—including building features, fixing bugs, refactoring, or reviewing addon code.
> Odoo 16 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 16 or custom addons—even if the user only pastes a traceback, mentions addons/ or __manifest__.py, describes form/tree/kanban/XML errors, HTTP controllers, or business rules on models—including building features, fixing bugs, refactoring, or reviewing addon code.
> Odoo 18 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 18 or custom addons—even if the user only pastes a traceback, mentions addons/ or __manifest__.py, describes form/list/kanban/XML errors, HTTP controllers, or business rules on models—including building features, fixing bugs, refactoring, or reviewing addon code.
Everything about the data layer — pick one of six ready-to-run backend templates (TanStack Start + Drizzle + better-auth, Hono + Drizzle + better-auth, Hono + Prisma + better-auth, Hono + Drizzle + Auth.js/NextAuth, FastAPI + SQLAlchemy + JWT, Supabase) and wire the frontend to it through the Repository + AuthProvider seams; scaffold a CRUD resource (Postgres table + server fns + query hooks + DataTable page + create/edit dialog + sidebar entry); bind or swap one resource's data source (Drizzle / REST / GraphQL / in-memory); or re-point the whole app's data + auth at a different backend. Use when adding a data entity or pointing the app at a backend other than the default Postgres + better-auth.
Delegates dbt and warehouse work to altimate-code, a specialized CLI agent with 100+ purpose-built data tools. USE THIS SKILL FIRST whenever the task mentions or implies: warehouse access (Snowflake, BigQuery, Redshift, Databricks, Postgres, MySQL, DuckDB), column-level lineage, downstream-impact analysis, dbt builds against a real warehouse, cross-database migration or validation, query cost attribution, schema diff, data parity checking, PII detection from sampled rows, incremental/SCD2/backfill verification, FinOps reporting, model documentation generation, query optimization, anomaly detection, dev-vs-prod diffing, or when the user says "altimate", "altimate-code", or "the data agent". Strongly preferred over Read/Write/Edit whenever the task touches live data, lineage, or warehouse state — consult before doing the work yourself.
| (1) Task mentions "fix", "error", "broken", "failing", "debug", "wrong", or "not working" (2) Compilation Error, Database Error, or test failures occur (3) Model produces incorrect output or unexpected results (4) Need to troubleshoot why a dbt command failed Reads full error, checks upstream first, runs dbt build (not just compile) to verify fix.
| (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.
| (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions "slow query", "make faster", "improve performance", "optimize SQL", or "query tuning" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up
Agent-callable Notion tools for searching pages and databases, reading and creating pages, querying data sources, appending content, and managing schemas. Use when the user mentions Notion or wants to find, read, create, or edit Notion content, even if they don't name Notion explicitly.
Fix bugs and broken behavior when there is enough evidence to act on a repair path. Use for errors, crashes, incorrect results, API failures (500, 404, 403), CORS problems, database exceptions, broken rendering, duplicated or wrong data, off-by-one mistakes, timezone/date bugs, broken forms, config-caused runtime failures, and regressions. Trigger when the user wants the bug repaired and the conversation already contains a clear failing area, a reproducible failing test, a concrete error path, or a prior diagnosis to implement. Do NOT use for new features, pure explanation, architecture discussion, broad research, or bug reports where the main need is figuring out why the behavior happens — use diagnose for that.
Database migration patterns and schema versioning
This skill should be used when the user asks "SQL or NoSQL", "which database", how to design a "data model" or "schema design", picks an "indexing" strategy, needs "sharding" or "partitioning", sets up "replication" (leader-follower / multi-leader), defines a "primary key"/"sort key", asks whether to "denormalize", or weighs "polyglot persistence". Use it whenever a design must decide where records live, how they are keyed and accessed, and how the store scales past one node — even if the user just says "store the data".
This skill should be used when the user asks about a "caching strategy", "cache invalidation", "what to cache", "read-through vs write-through vs write-back", "cache eviction" (LRU/LFU/TTL), "Redis vs Memcached", "stale reads", or hits "thundering herd", "cache stampede", "cache penetration", or "hot key" problems, or is choosing a Redis `maxmemory` eviction policy (allkeys-lru, volatile-lru, noeviction) vs TTL expiry for sessions or other cached state. Use it whenever a design is read-heavy or a datastore is overloaded by reads, even if the user doesn't say "cache".
>- Build, fix, audit, and migrate Drift persistence in Dart CLI, server-side, and non-Flutter desktop apps. Use when adding SQLite with migrations, resolving build_runner or drift_dev failures, or validating Dart database code with code generation, analysis, tests, and migration checks.