“Data pipelines”

Sling CLI MCP server for querying any database, running data pipelines and managing replications

JARVIS-CD MCP with a compact user pipeline contract and explicit admin compatibility profiles

Fetch and analyze BP5 data files using ADIOS2.

Search arXiv by topic, author, title or date, read paper details, and export BibTeX or PDFs.

Record and retrieve agent interactions in a ChronoLog log store.

Compress and decompress files with gzip and report the compression ratio.

Open HDF5 files, browse groups, inspect datasets and attributes, and read or summarize data in...

List, search and inspect Lmod environment modules, and save or restore module collections.

National Data Platform (NDP) MCP server for searching and discovering datasets across multiple...

Report CPU, memory, disk, network, GPU and process information for local or remote nodes.

Submit, list, inspect and cancel Slurm jobs, and describe the cluster.

Structured Spack discovery and installation tools for scientific agents

Provider-fixed web search plus transparent URL, DOI, and document fetching

Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation

Polymarket data pipeline (markets + Goldsky orderFilled events + processed trades).

Scans AI training data pipelines and datasets to detect bias, data poisoning, label corruption, and statistical anomalies that could compromise autonomous AI…

Use when reviewing data pipeline architecture, batch or streaming stages, data contracts, idempotency, retries, backfills, quality checks, and observability.

Pipeline 一键启动。清理旧进程 → GitHub搜索baseline → 用户确认 → Bootstrap → 启动 Dashboard。

Creates, configures, and updates Databricks Lakeflow Spark Declarative Pipelines (SDP/LDP) using serverless compute.

MCP server exposing Buildkite API data (pipelines, builds, jobs, tests) to AI tooling and editors.

Build a real-time change-data-capture pipeline from a transactional database (Aurora DSQL, Aurora Postgres, RDS) to Redshift Serverless, S3, or any other…

Team conventions for Python development — credentials, API clients, LLM response parsing, testing patterns, and data pipeline structure.

Pipeline diagnostics, status checks, and CI troubleshooting for GitLab.

Pipeline orchestrator that classifies incoming coding tasks and routes them through the correct combination of skills at the right depth.