“data structures”的搜索结果

将科学原理、物体结构和工作流程整理为简洁的SVG图，统一配色与留白，自动适应明暗模式，并输出可在浏览器直接打开的单个HTML文件。

• 为教材绘制科学原理图
• 用剖面图或分层图解释结构
• 展示工作流程和数据流向

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

帮助实现多个设备或服务器之间的数据同步，使用CRDT合并并发更新，涵盖计数器、集合等结构，以及仅发送变更的增量同步和操作因果关系追踪。

• 汇总多个节点的计数
• 合并共享集合的并发更新
• 实现仅传输变更的增量同步

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

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

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

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

Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.

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• Extracting errors from large logs
• Summarizing test failures and build warnings
• Inspecting JSON and CSV data

Create mathematical and technical explainer videos with Manim Community Edition. The skill guides scene planning, animation code, rendering, and clip assembly to show equations, algorithms, system structures, and data through coordinated visuals.

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• Explaining mathematical concepts through animation
• Showing equation derivations step by step
• Visualizing algorithm execution

Organize Notion workspaces with clear page structures, connected databases, and reusable templates. Build team wikis and task views, reduce duplicated content, and use the Notion API to automate page creation, database queries, and content updates.

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• Organizing a team wiki so information is easier to find
• Creating templates for meeting notes and project briefs
• Connecting project databases with filtered views

Build C# applications with .NET 8+, including ASP.NET Core APIs and Blazor interfaces. Get models, endpoints, services, and configuration, with guidance on database access, asynchronous operations, cancellation, authentication, performance tuning, and tests.

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• Build a REST API with ASP.NET Core
• Implement database access with Entity Framework Core
• Create Blazor components and manage application state

Plan a personal monitoring dashboard using a reference for panels, data sources, APIs, refresh intervals, and development priorities. It covers schedules, messages, news, stocks, finances, and combined views such as a daily briefing.

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• Choosing which dashboard panel to build next
• Checking panel APIs and data structures
• Planning a combined view of schedules, messages, and stocks

Assess GraphQL APIs during penetration tests, from finding endpoints and mapping data structures to checking access controls, query limits, subscriptions, and uploads. Includes example queries, guidance for testing tools, and defensive controls for interpreting findings.

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• Map GraphQL endpoints and data structures
• Check access controls across users and roles
• Review query limits and subscription security

Look up compound–target binding data in BindingDB using PDB or UniProt identifiers, or search for similar chemical structures. Receive concise summaries of matching records, with full API responses saved to a file when requested.

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• Find ligands linked to a PDB identifier
• Look up binding data for a UniProt entry
• Search for compounds with similar structures

Retrieve focused information from CIViC for cancer variant interpretation. It uses GraphQL, a way to request specific data fields, to inspect available data structures and turn targeted evidence results into concise summaries.

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• Retrieve CIViC evidence for a specific cancer variant
• Inspect available CIViC data fields
• Get concise Markdown summaries of query results

Write, review, and optimize Postgres queries, database structures, and configuration using guidance maintained by Supabase. Prioritized rules and SQL examples help assess query speed, connections, indexes, and row-level security, which controls access to individual rows.

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• Review slow SQL queries
• Assess table structures and indexes
• Review connection pooling and scaling settings

Guide Claude Code to check files, documentation, data, and execution results before presenting answers. The skill sets verification steps for coding, analysis, and writing, including checking citations and numbers, diagnosing errors, and clearly stating what cannot be verified.

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• Checking function names and arguments before coding
• Inspecting data columns and file paths before analysis
• Verifying row counts and missing values after joins

Work with crystal structures and materials calculations in Python. Convert structure files, examine symmetry and stability, analyze electronic properties, prepare calculation inputs, and retrieve computed materials data from Materials Project using pymatgen and its supporting tools.

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• Convert crystal structure files between CIF and POSCAR
• Analyze crystal symmetry and neighboring atoms
• Create phase diagrams and assess material stability

Search and summarize public FDA data through openFDA using Python. Retrieve adverse event reports, recalls, drug labels, device approvals, and substance identifiers to support safety research, regulatory analysis, and comparisons across products or time periods.

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• Summarizing drug adverse event reports
• Researching medical device approvals and recalls
• Tracking food recalls involving allergens

Turn recorded browser traffic into an OpenAPI 3.1 specification, a standard format for describing APIs. Infer endpoints and data structures from observed requests, and generate client code plus reports showing examples, confidence, and coverage gaps.

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• Documenting observed endpoints of an undocumented website API
• Preparing client integration from captured browser traffic
• Inferring response data structures from captured body samples

Generate molecular predictions from a CSV of SMILES strings, a text notation for molecular structures, using a finetuned KERMT model. Validate inputs, clean data, compute molecular features, and return predictions with execution logs and a replay command.

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• Predict targets for molecules listed in a SMILES CSV
• Check whether a saved model is suitable for inference
• Reuse prepared data for another prediction run

Continue training a KERMT molecular model on a custom SMILES corpus, where text strings describe molecular structures. The skill validates data and checkpoints, prepares training inputs, and launches containerized training with saved logs and checkpoints.

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• Continue molecular model pretraining on a custom SMILES corpus
• Start additional training with a new learning-rate schedule
• Resume an interrupted training run from a saved checkpoint

Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.

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