Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.
npx skills add https://github.com/redis/agent-skills --skill redis-core
Foundational guidance for modeling data in Redis. Covers data-type selection and key-name conventions — the two decisions that most directly drive memory, performance, and maintainability.
Pick the type that matches the *access pattern*, not just the shape of the data.
| Use case | Recommended type | Why |
|---|---|---|
| Simple values, counters | String | Atomic INCR/DECR, SET/GET |
| Object with independently updated fields | Hash | Per-field reads/writes, no whole-object rewrite |
| Queue, recent-N items | List | O(1) push/pop at ends |
| Unique items, membership checks | Set | O(1) SADD/SISMEMBER/SCARD |
| Rankings, score-based ranges | Sorted Set | Score-ordered; ZADD/ZRANGE/ZRANK |
| Nested / hierarchical data | JSON | Path-level updates, nested arrays, RQE indexing |
| Event log, fan-out messaging | Stream | Persistent, consumer groups |
| Vector similarity | Vector Set | Native vector storage with HNSW |
Common anti-pattern: stuffing a flat object into a serialized string. Updating one field means fetch + parse + mutate + rewrite. Use a Hash instead.
See references/choose-data-structure.md for full rationale and Python/Java examples.
Use colon-separated segments with a stable hierarchy:
{entity}:{id}:{attribute}
user:1001:profile
user:1001:settings
order:2024:items
session:abc123
article:987:likes
game:space-invaders:leaderboard
Rules of thumb:
User_1001_Profile is bad).tenant:42:user:7:cart) so scans and ACLs can target a tenant cleanly.See references/key-naming.md for cleanup examples and edge cases.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information for biochemical research and metabolic pathway analysis.
Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.
Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.
Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.
Take redis/redis-core from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.