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Investigate slow SQL, write complex joins and summaries, and design database structures. Uses execution plans, which show how queries run, to guide index choices and query rewrites, with before-and-after performance measurements.
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Predict structures of complexes containing proteins, DNA, RNA, and ligands with OpenFold3, an AlphaFold3 reproduction with publicly available model weights. Prepare JSON inputs, generate candidate structures, and inspect confidence scores and molecular clashes.
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Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals. Not for applying optimization patterns once profiling has identified a bottleneck (→ See `samber/cc-skills-golang@golang-performance` skill).
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Query RCSB PDB (200K+ structures) via the public REST + GraphQL APIs with plain `requests` (no SDK). Search by text, attribute, sequence, or 3D structure similarity (Search API); retrieve metadata via GraphQL (Data API); download PDB/mmCIF from files.rcsb.org. For AlphaFold predictions use alphafold-database-access; for protein sequences only use uniprot-protein-database.
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Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.
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配置Claude工具执行前的检查钩子，暂时拒绝首次编辑或创建文件，要求先核实调用方、数据结构和用户请求，将修改前调查纳入流程，并提示检查破坏性命令。
将系统结构和数据流整理成交互式立体 HTML 地图，点击即可查看细节。还能从同一份数据生成说明文档，记录设计决策并跟踪尚未解决的问题。
Read, analyze, modify, and visualize molecular structures with RDKit in Python. Calculate molecular properties, search for similar structures or specific structural patterns, apply reaction rules, and generate 2D or 3D coordinates with detailed control over processing.
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Look up genes, sequences, protein structures, expression patterns, and disease associations across more than 20 bioinformatics resources using command-line tools or Python. Useful for interactive exploration, with results that can be saved for further analysis.
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Build molecular property prediction workflows with DeepChem for solubility, toxicity, and related chemistry tasks. Prepare molecular representations, choose compatible models, handle missing labels, and evaluate predictions using data splits designed for the intended scientific question.
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Write, debug, and optimize SQL queries for data analysis and database design. The skill covers aggregations, rankings, missing values, and indexes, using execution plans to understand performance and adapting syntax to the database in use.
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Design Redis caching, leaderboards, notifications, and message processing. Helps choose data structures, write Lua scripts for operations that run together without interference, and plan expiration, memory use, and persistence settings.
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Search NIH Metabolomics Workbench for metabolite structures, study details, and experimental data. Standardize metabolite names with RefMet, find candidate compounds from mass spectrometry measurements, and filter studies by disease, sample source, or analytical method.
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Work with DNA, RNA, and protein sequences in Python. Read and convert biological files, compare sequences, search for similar sequences with BLAST, retrieve NCBI records, analyze protein structures, and build or visualize evolutionary trees.
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Search ChEMBL for compounds by name, chemical structure, or properties. Retrieve biological activity measurements, target information, and drug mechanisms to investigate inhibitors and compare how structural differences relate to measured activity.
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Fine-tune a pretrained KERMT model on a CSV of molecular structures and labeled targets. The workflow checks inputs, prepares features, launches training in a container, and saves trained models, logs, and test results.
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Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data structures for memory, handle large datasets efficiently, or diagnose memory issues. Covers object sizing, generator patterns, efficient data structures, and memory profiling strategies.
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Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
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Query openFDA REST API for adverse events (FAERS), labeling, product info, recalls, enforcement. Search by drug name, ingredient, MedDRA, or NDC. 1k req/day no key; 120k with free key. For trials use clinicaltrials-database-search; for structures use drugbank-database-access or chembl-database-bioactivity.
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Query IUPHAR/BPS Guide to Pharmacology (GtoPdb) for receptor-ligand interactions, target/ligand metadata, families, and approved drugs. Affinities (pKi/pIC50/pKd), action (Agonist/Antagonist/etc.), species, structures (SMILES/InChI). No auth. Always resolve targets via geneSymbol/accession; most metadata lives in sub-resources (/databaseLinks, /structure, /synonyms).
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Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.
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Use when designing, querying, or troubleshooting the Financial Services Cloud data model — including managed-package (FinServ__ namespace) and Core FSC (standard objects, no namespace) object structures, household relationship modeling, financial account ownership, and the FSC rollup framework. Trigger keywords: FSC data model, FinancialAccount, FinancialHolding, FinancialAccountRole, FinancialAccountParty, FinServ namespace, household rollup, AssetsAndLiabilities, FinancialGoal, LifeEvent, AccountContactRelation FSC. NOT for setting up financial accounts and roles in Setup — use admin/financial-account-setup. NOT for household membership and rollup scheduling — use admin/household-model-configuration.
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Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.
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Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable. Use when auditing models for bias, designing privacy-preserving systems, establishing governance processes, or evaluating the social impact of data-driven decisions.
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