'sequence' 검색 결과

API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate .excalidraw diagrams from natural language. Use when creating flowcharts, mind maps, system architecture diagrams, sequence diagrams, ER diagrams, network diagrams, or any visual process visualization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Task sequences, progressive disclosure, and the pacing of an experience over time. Use when designing multi-step flows, evaluating onboarding, or diagnosing why users get lost.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates sourced, BDR-ready account research briefs for named target accounts in B2B SaaS ABM and outbound motions. Use after a target account list is built and before outreach sequences are written. Best for Tier 1, Tier 2, and intent-qualified accounts where BDRs need a clear "why this account, why now, why this persona" hypothesis. Designed for B2B SaaS teams selling compliance, security, GRC, risk, or other technical platforms into mid-market and enterprise buyers. Not intended for broad list building or mass enrichment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Always use when user asks to create, generate, draw, or design a diagram, flowchart, architecture diagram, ER diagram, sequence diagram, class diagram, network diagram, or mentions draw.io, drawio, .drawio files. Use for complex diagrams where Mermaid is insufficient.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build 2D games and animations using SpriteKit. Use when creating game scenes with SKScene and SKView, adding sprites with SKSpriteNode, animating with SKAction sequences, simulating physics with SKPhysicsBody and contact detection, creating particle effects with SKEmitterNode, building tile maps, using SKCameraNode, or integrating SpriteKit scenes in SwiftUI with SpriteView.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement, review, or improve SwiftUI gesture handling. Use when adding tap, long press, drag, magnify, or rotate gestures, composing gestures with simultaneously/sequenced/exclusively, managing transient state with @GestureState, resolving parent/child gesture conflicts with highPriorityGesture or simultaneousGesture, building custom Gesture protocol conformances, or migrating from deprecated MagnificationGesture to MagnifyGesture or using the newer RotateGesture.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pixel-based motion and UI change analysis from frame sequences or screenshots using computer vision and visual comparison. Use when `.transition` or visual effects are not observable via MotionEyes logs, when you only have screenshots/video, when you need regression diffs across builds, or when you need to summarize on-screen changes without instrumentation. Do not use when MotionEyes traces are available and you need precise timing/values; use `motioneyes-animation-debug` instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide for using renderers — the bridge between chat-style messages and token sequences. Covers renderer setup, TrainOnWhat, vision inputs, model family matching, and custom renderers. Use when the user asks about renderers, tokenization, message formatting, or vision inputs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Master orchestrator for the 5-step productivity loop. Runs audit, architect, analyst, refinery, and compounder in sequence or individually. Use when you want to run the full loop, start the system, check system status, resume from where you left off, or run a specific step. Manages state, sequencing, and skill chaining. Supports full loop, single step, resume, and status-only modes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Biopython is a comprehensive set of freely available Python tools for biological computation. It provides functionality for sequence manipulation, file I/O, database access, structural bioinformatics, phylogenetics, and many other bioinformatics tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Speed up long-sequence transformer training and inference.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Uses Microsoft RESTler to perform stateful REST API fuzzing by automatically generating and executing test sequences that exercise API endpoints, discover producer-consumer dependencies between requests, and Bul: security and reliability bugs. The tester compiles an OpenAPI specification into a RESTler fuzzing grammar, configures authentication, runs test/fuzz-lean/fuzz modes, and analyzes results for 500 errors, authentication bypasses, resource leaks, and payload injection vulnerabilities. ...

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Architecture Decision Records — context, decision, consequences, supersession trail.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

当使用者说"把这几张照片拼成一页"、"拼成一张长图"、"这组照片排成一页 PDF"时使用。Arrange 3–9 finished photographs into a cohesive one-page series, contact sheet, long image, or PDF. Use when a user wants to sequence and present a photo set with consistent frames, gutters, and canvas treatment. Preserve the photographer's chosen order unless they explicitly ask for sequencing suggestions; do not infer artistic intent from pixels alone. For a publication or exhibition-board layout with bands, a lead photo per band and a credit per band, or more than 9 photos, use photo-spread-composer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn a loose idea into a sequenced map of investigation tickets, then drive them to resolution one at a time.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Query physics databases — NIST CODATA constants, NIST Chemistry WebBook, Materials Project, Particle Data Group (PDG), OEIS sequences. Always use these instead of hardcoding physical constants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.