Results for “data structures”

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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Configure a Claude hook, a check before tool execution, that pauses initial edits and file creation to request concrete facts. It prompts investigation of callers, data structures, and user instructions, with separate checks for destructive commands.

• Checking affected modules before editing
• Checking data structures before changes
• Configuring exemptions for tests and generated files

Create an interactive isometric map of a system’s architecture, with clickable structures and inspectable data flows. Generate matching text documentation from the same data file and track design decisions and open questions across review rounds.

• Explaining system structure and data flows
• Reviewing a new architecture with a team
• Tracking open questions across design reviews

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.

• Calculating properties for a collection of molecules
• Searching for molecules with similar structures
• Filtering molecules by specific structural patterns

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.

• Looking up gene details and sequences
• Exploring protein structures and residue annotations
• Comparing tissue expression and disease associations

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.

• Predicting solubility from molecular structures
• Comparing graph models on MoleculeNet datasets
• Checking data splits for information leakage

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.

• Designing caches with expiration times
• Building leaderboards and request rate limits
• Processing messages with Redis Streams

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.

• Writing queries for totals and rankings
• Investigating slow queries with execution plans
• Reviewing table structures and constraints

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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Create clear educational diagrams for scientific concepts, physical structures, workflows, and data. Output SVG, a scalable vector image format, inside one standalone HTML file with consistent styling and automatic light and dark modes.

• Illustrating scientific concepts for teaching materials
• Explaining object structures with cross-sections and layers
• Visualizing workflows and data movement

Turn specifications into pseudocode, a language-independent outline of program logic. Define data structures, handle edge cases, analyze time and memory requirements, and document design patterns and optimization opportunities before implementation.

• Turning specifications into clear processing steps
• Comparing data structures and computational costs
• Planning error handling and edge cases before coding

Implement distributed data synchronization with CRDTs, data structures designed to merge concurrent updates. Covers counters, sets, shared values, and sequences, with incremental updates and tracking of dependencies between operations.

• Implementing shared counters across multiple nodes
• Merging concurrent additions and removals in shared sets
• Sending only changes during data synchronization

Build, analyze, and visualize relationship data in Python with NetworkX. Find shortest routes, identify important nodes and groups, generate networks for simulations, and exchange graph data through tables and common file formats.

• Finding important people and groups in social networks
• Calculating shortest routes in transportation networks
• Visualizing relationships between research papers

Use Dask to process tables, numerical arrays, and file collections in pieces across CPU cores or machines. Helps choose data structures, size memory-friendly chunks, coordinate dependent tasks, and diagnose slow or memory-heavy workflows.

• Aggregate CSV or Parquet files that exceed available memory
• Process large scientific arrays in chunks
• Clean JSON records and logs before analysis

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Analyze compiled programs without running them by recognizing branches, loops, data structures, and common instruction patterns. Use disassembly and decompilation, which translate machine code into readable representations, to reconstruct program logic and document findings.

• Understand an unfamiliar executable
• Reconstruct branches and loops from assembly
• Infer data structures and function parameters

Creates a new domain-specific skin for reskinnable-demo, combining branding, layouts, pages, data, tools, and an agent. Structures live sales demonstrations around visible AI interactions, including generated interfaces, shared state, confirmations, and memory features.

• Creating a live sales demo for a different industry
• Changing branding, layouts, and data together
• Demonstrating shared state and human confirmations