'data structures' 검색 결과

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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Claude의 파일별 첫 편집과 생성을 잠시 거부하는 훅을 설정합니다. 호출 위치, 데이터 구조, 사용자 요청을 확인하도록 하여 변경 전 조사를 작업에 포함합니다.

• 편집 전 영향을 받는 모듈 조사하기
• 변경 전 데이터 구조 확인하기
• 테스트와 생성 파일 제외 설정하기

시스템 구성과 데이터 흐름을 클릭해 살펴보는 입체 HTML 지도로 정리합니다. 같은 데이터로 설명 문서도 생성하고 설계 결정과 미해결 질문을 관리합니다.

• 시스템 구성과 데이터 흐름 설명
• 팀과 새로운 설계 검토
• 검토 중 미해결 질문 관리

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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• 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.

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• 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.

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• 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.

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• 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.

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• 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 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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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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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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과학 원리, 사물의 구조, 작업 흐름을 색상과 여백이 일관된 SVG 도표로 만듭니다. 밝은 화면과 어두운 화면을 지원하며 단일 HTML 파일로 출력합니다.

• 교재용 과학 원리 도해 제작
• 단면도와 층별 도표로 구조 설명
• 작업 공정과 데이터 흐름 시각화

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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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