'sequences' 검색 결과

Operate desktop applications through DeepChat's built-in Computer Use tools. Inspect windows, click controls, enter text, use menus, and arrange windows, checking the visible result after each action.

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• Entering text and using controls in desktop apps
• Opening files or URLs in an application
• Arranging application windows

Turn content into AntV Infographic syntax, a text format for describing infographics. The skill organizes titles, items, relationships, and metrics, then selects a suitable template for sequences, lists, comparisons, hierarchies, or charts.

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• Turn procedural text into sequence syntax
• Structure a two-sided comparison
• Describe hierarchical information

Review Apache Pinot changes for concurrency and state safety, including locks, shared callbacks, lifecycle changes, and distributed metadata. Use it to examine how changes affect shared state when multiple operations run at the same time.

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• Reviewing changes to locks and atomic operations
• Checking shared callback registration changes
• Reviewing Helix state transitions and ZK node changes

Write and repair custom MSBuild targets, the steps that organize build tasks. Preserve existing build sequences, choose appropriate insertion points, distinguish returned results from rebuild checks, and register generated files for cleanup.

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• Add custom steps to an existing build sequence
• Repair dependency settings that replace SDK build steps
• Correct stale results returned by query targets

Review existing interface animations and tune their timing, movement, scale, blur, and easing—the way speed changes. It matches values to their purpose and checks opening, closing, hover behavior, and staggered sequences against shared motion tokens.

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• Reviewing existing interface animations
• Adjusting modal opening and closing speeds
• Fixing delays in staggered list animations

Document a settled architecture choice as an ADR, a record of a design decision. Capture its context, rejected alternatives, benefits, costs, and links to earlier decisions it replaces so future readers can understand the reasoning.

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• Documenting why one design approach was chosen
• Preserving the costs and obligations of a decision
• Replacing an earlier decision while keeping its history

Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.

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• Splitting large command files into focused modules
• Adding interactive setup prompts
• Recording command results through execution hooks

Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.

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• Build agent memory that reuses past experiences
• Record and assess sequences of agent actions
• Consolidate similar experiences into reusable patterns

Walk Chinese-speaking users through code or a pull request while checking correctness. Follow the actual execution flow, explain key implementation choices, and report concrete defects with trigger conditions, consequences, and precise file locations.

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• Understanding how an implementation works
• Reviewing a pull request for defects
• Checking edge cases and missing tests

Organize topology problems about compactness, a property defined through finite selections from open covers. Choose tests suited to the space, examine products, and apply compactness to continuous images and maximum or minimum values.

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• Check compactness of a subset of Euclidean space
• Examine compactness through sequences or open covers
• Study compactness of product spaces

Plan solutions to Banach space problems in functional analysis. Work through completeness, meaning Cauchy sequences converge, and select major theorems to guide arguments about extending linear functionals, open mappings, and bounded operators.

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• Planning a proof that a normed space is complete
• Choosing a theorem for extending a linear functional
• Studying open mappings and bounded inverses

Plan product images and e-commerce detail pages, then turn the visual direction into image-generation prompts. Coordinate colors, layouts, and image sequences across a set, with optional image generation through a configured API.

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• Planning a complete set of product images
• Designing graphics for a product detail page
• Keeping colors and layouts consistent across images

Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.

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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

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Validate and use packed sequences and long-context training in Megatron-Bridge, including offline LLM packing, collate-time VLM packing, Energon online packing, and CP constraints.

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Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIM_MODEL_NAME (the recommended default fast path, ~14 min vs over 80 min for the built-in downloader); a plain docker run uses the slow built-in downloader.

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Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.

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NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

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Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Sends user-provided PDB files and design parameters to NVIDIA's hosted API, authenticated with an environment API key, or to a user-selected local NIM. Use for sequence design, backbone redesign, fixed chains and residues, omit_AAs, sampling temperature, soluble model, local Docker, and multi-FASTA output.

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Agent guide for operating Sequenzy. Use when Codex needs to authenticate, inspect identity, manage subscribers, create or edit campaigns/sequences/templates, generate draft email content, send a transactional email, read delivery stats, or decide whether a requested Sequenzy workflow is currently supported. Prefer the CLI when it is implemented, and fall back to the dashboard or direct API use when the current CLI surface is only partial.

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Access protein metadata, function, taxonomy, and sequences across UniProtKB, UniParc, and UniRef. Use when searching for proteins, mapping identifiers, or retrieving functional annotations and publications. Don't use for sequence alignment, protein folding, or sequence similarity search (use specialized skills for those tasks).

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Searches for homologous protein sequences using MMseqs2 (fast, default) or BLAST (comprehensive, fallback). Trigger this whenever the user provides a protein sequence or FASTA file and asks to find homologues, sequence matches, or wants to infer protein function based on sequence similarity, but not when the user wants to infer protein function based on structural similarity.

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Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.

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Dataset annotation management — COCO labels, sequences, export, and Kaggle upload

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