搜索「Batch Processing」的结果

io.github.gigabrain-observer/google-docs-mcp-server

Google Docs MCP server with full tab support, markdown extraction, and batch updates.

io.github.marlinjai/email-mcp

Unified email MCP server for Gmail, Outlook, iCloud, and IMAP with batch operations

io.github.dpozimski/qrmint-mcp

Generate styled QR codes with colors, logos, frames, and batch support

io.github.standardbeagle/dart-query

Dart AI task management MCP with batch operations, DartQL selectors, CSV import, zero context rot

io.github.khglynn/spotify-bulk-actions-mcp

MCP server for bulk Spotify operations - batch playlist creation, exports, management

io.github.copyleftdev/fabric-atelier

AI-powered content processing with 226 Fabric patterns for writing, analysis, and code generation.

io.github.K-Dense-AI/scientific-agent-skills/gtars

Use Gtars for local genomic interval models and set algebra, overlaps and counts, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and the CLI.

io.github.K-Dense-AI/scientific-agent-skills/modal

Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.

io.github.K-Dense-AI/scientific-agent-skills/polars

High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

io.github.K-Dense-AI/scientific-agent-skills/optimize-for-gpu

GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.

io.github.K-Dense-AI/scientific-agent-skills/datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

io.github.K-Dense-AI/scientific-agent-skills/gget

Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

io.github.pytorch/pytorch/triaging-issues

Triages GitHub issues by routing to oncall teams, applying labels, and closing questions. Use when processing new PyTorch issues or when asked to triage an issue.

io.github.K-Dense-AI/scientific-agent-skills/rowan

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

io.github.code-yeongyu/oh-my-openagent/data-scientist

Expert data processing with a hybrid engine strategy: resident-kernel engines first - DuckDB plus a resident Python stack (Polars/numpy/matplotlib) in persistent js/py eval kernels where the harness has them, bun/uv one-shots elsewhere - and per-action placement judgment (in-memory vs streaming vs remote-in-place). Triggers: 'analyze the data', 'what is in this CSV/parquet/json', 'summarize this', 'group by', 'filter rows', 'sort by', 'join these files', 'merge datasets', 'time series trend', 'compare yesterday and today', 'distribution/histogram', 'correlation', 'clean duplicates', 'handle missing values', 'dataset larger than RAM', 'SQL query on files', 'DataFrame operations', 'chart/plot this data', DuckDB vs Polars selection, quick data exploration CLI. NOT for plain text/code inspection, configs, or tiny inline math.

io.github.nexu-io/open-design/threejs

Three.js skills for creating 3D elements and interactive experiences in the browser — scenes, materials, controls, and post-processing.

io.github.vinta/awesome-python/preview-verdicts

Generate the interactive keep/drop verdict preview (HTML page with per-row feedback controls) whenever a prune sweep, batch entry edit, or restructure needs maintainer review before touching README.md — and process the feedback JSON the maintainer pastes back.

io.github.affaan-m/ECC/quarkus-patterns

Quarkus 3.x LTS architecture patterns with Camel for messaging, RESTful API design, CDI services, data access with Panache, and async processing. Use for Java Quarkus backend work with event-driven architectures. Use when building or reviewing a Quarkus service, especially with Camel messaging or Panache data access.

io.github.affaan-m/ECC/springboot-patterns

Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work. Use when building or reviewing a Spring Boot backend — REST layer, services, data access, caching, or async work.

io.github.affaan-m/ECC/netmiko-ssh-automation

Safe Python Netmiko patterns for read-only collection, bounded batch SSH, TextFSM parsing, guarded config changes, timeouts, and network automation error handling. Use when automating network device access with Python Netmiko, whether collecting state or pushing guarded config changes.

io.github.langflow-ai/langflow/ibm-a11y-level1-audit

Perform a scoped IBM Equal Access Level 1 compliance audit of a chosen Langflow frontend surface (routes, components, or a PR) and produce a findings report mapped to WCAG/IBM Level 1 criteria. Default behavior is audit and report only — fixes are applied only when the user explicitly asks for remediation in the same request. Use when the user asks for an IBM Level 1 audit, a Level 1 compliance report, or to find/report Level 1 WCAG issues on a specific surface. For scanning a batch of routes without a report, see ibm-a11y-route-scan. For scanning and fixing an entire PR/branch end-to-end by default, see ibm-a11y-pr-remediation.

io.github.langflow-ai/langflow/ibm-a11y-pr-remediation

Scan every frontend surface touched by the current PR/branch for IBM Equal Access Level 1 accessibility issues and fix all in-scope violations by default. Discovers changed files, maps them to routes/components/states, runs both axe and IBM engines, remediates until both are green (or only documented baselines remain), and reports back. Use when the user asks to check, scan, or clean up accessibility for "this PR", "my branch", or "my changes" and wants fixes applied, not just a report. For a single scoped audit report without a default fix pass, use ibm-a11y-level1-audit; for a route batch scan only, use ibm-a11y-route-scan.

io.github.XiaomiMiMo/MiMo-Code/codex

Run, configure, and troubleshoot OpenAI Codex CLI in non-interactive headless environments. Use for Codex automation in Bash or PowerShell, native Windows or WSL2, shell scripts, CI/CD, Docker, Kubernetes, remote servers, agent harnesses, or batch jobs; for constructing `codex exec` commands; selecting sandbox and approval modes; consuming JSONL events or structured output; resuming sessions; passing prompts through stdin; and handling failures caused by unavailable interactive input such as `request_user_input`.

io.github.microsoft/vcpkg/review-vcpkg-prs-today

Review open non-draft microsoft/vcpkg pull requests updated in the last 30 days. Use when asked for batch triage, per-PR review reports, an index grouped by verdict, or grouping competing PRs that modify the same port.