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Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.
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Query UniProt REST API: search by gene/protein name, fetch FASTA, map IDs (Ensembl, PDB, RefSeq), access Swiss-Prot annotations. Use bioservices for multi-DB access; alphafold-database-access for structures.
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Structures risk mitigation planning and residual risk tracking for Data Protection Impact Assessments under GDPR Article 35(7)(d). Covers mitigation measure identification, implementation tracking, residual risk acceptance, and Art. 36 prior consultation triggers. Keywords: DPIA mitigation, risk treatment, residual risk, Art. 35(7)(d), safeguards, mitigation tracking, prior consultation.
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TRIGGER THIS when researching market salaries, building compensation strategies, creating salary bands, analyzing pay equity, conducting salary reviews, preparing compensation proposals, or ensuring competitive compensation. Analyzes market data, builds equity-focused salary structures, identifies pay gaps, and creates data-driven compensation recommendations.
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Recommend basic data structures for a task. Use when a junior developer needs help choosing lists, maps, or sets.
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Analyze and design control flows and data structures. Produces compact ASCII tree diagrams showing triggers, call chains, payload shapes, state mutations, and re-render effects. Use when user asks to diagram, trace, visualize, or design a flow or data structure.
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CatColab Schemas - database schema modeling distinguishing entities (tables) from attributes (columns). Foundation for ACSets (Attributed C-Sets) and AlgebraicJulia data structures.
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Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
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Reference for advanced data structure patterns used in competitive programming and technical interviews. Covers heaps, monotonic stacks, tries, segment trees, Fenwick trees, stack-based parsing, and ordered sets with Python templates, recognition signals, and edge case guidance.
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Use this skill when personal data goes, or will go, to a recipient outside the EEA, or someone asks "do we need a TIA", "transfer impact assessment", "SCCs for this vendor", "can we use this US vendor under GDPR", "international data transfer assessment", "Schrems II assessment" or "our customer asked for our transfer assessments". Confirms whether there is a transfer at all, maps the full chain including remote access and sub-processors, identifies the transfer mechanism, structures the destination law assessment from sourced material, proposes supplementary measures and drafts a documented assessment with a review trigger.
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Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per X.
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"Apply Hierarchical Linear Modeling (HLM) to analyze nested data structures with random intercepts and slopes, accounting for intra-class correlation and cross-level interactions. Use this skill when the user has students nested in schools, employees in firms, or repeated measures in individuals, needs to partition variance across levels, or when they ask 'how do I handle nested data', 'what is ICC', or 'do group-level factors moderate individual-level relationships'.".
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Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
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Redis best practices for caching, data structures, and in-memory data architecture
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Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
中文简介正在准备中。当前显示原文说明。
Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.
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Organize, categorize, rename, and manage files systematically using automated rules, naming conventions, and folder structures for efficient file management. Use when organizing uploaded files, implementing file naming conventions, categorizing files by type or metadata, creating folder structures, cleaning up messy directories, automating file movements, implementing media libraries, or building file management systems.
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根据规格说明和处理流程设计系统组件及其连接方式，将技术选择、数据与接口设计、故障应对和扩展计划整理为架构图及设计文档。
Screen compound libraries for drug discovery using drug-likeness guidelines, structural alerts that flag patterns for review, and molecular complexity measures. Combine criteria to shortlist candidates and retain filtering results with reasons for further assessment.
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Create Markdown research notes, reports, and guides with Mermaid diagrams, which describe relationships as editable text. Choose suitable diagrams for workflows, timelines, or schemas, add accessible descriptions, and check rendering in the intended destination.
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Automate Airtable record searches, creation, updates, and deletion through Rube MCP. Inspect table structures before making changes, manage fields and table metadata, and handle larger operations using pagination and batches within the documented limits.
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Explore the context-matic MCP server through an interactive tour. Discover available APIs, choose one, and review authentication, data structures, and example calls in your project's programming language, with guidance matched to the SDK version.
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Detect five-point price patterns using Fibonacci ratios and generate buy, sell, or stand-aside signals. The skill identifies Gartley, Bat, Butterfly, and Crab patterns and locates potential reversal zones, where a price trend may change direction.
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Analyze Go malware binaries in Ghidra by extracting build metadata, function names, dependencies, and relevant strings. Use GoResolver to help recover obscured names and organize functions so analysts can focus their reverse engineering work.
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