⚡ JEval helps you to evaluate your JMeter test plan and provides recommendation before you start your performance testing. All contributions welcome 🙏.
- jmeter
- python
- performance
- performance-testing
47,133 repositories indexed
169–185 of 185 · Most stars
⚡ JEval helps you to evaluate your JMeter test plan and provides recommendation before you start your performance testing. All contributions welcome 🙏.
Python Frontend to Algorithms of the Algorithm Engineering Group Heidelberg
Tool for suggesting lemma names in Coq verification projects
Declarative data quality engine. Define checks in YAML, run anywhere.
A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.
A Python library built on pandas and geopandas for linearly referenced data management, engineering, and analysis using the DataFrame accessor pattern (.lr).
Blazingly fast data comparison tool for Python, powered by Rust. Compare massive CSV/Parquet datasets instantly.
ETL scripts for EOS.
Per-repo memory, outcome telemetry, and a calibrated-confidence gate for Claude Code, with MCP and AGENTS.md projections so other AI coding tools can read its context. Notes survive sessions; success claims need test evidence; your reverts are remembered. Local-only, stdlib runtime.
A structured, AI-assisted toolkit for defining hardware products that have software inside — from concept through engineering spec to presentation-ready materials in eight phases.
Conflict-free merge for DataFrames, JSON, ML models & distributed agents — powered by CRDTs. The first merge library where every operation is mathematically guaranteed to converge.
Sigma Queries turned into KQL for Defender using pysigma - Automated
Open, audit-grade agentic data quality framework with portable industry packs
Data ingestion engine — Singer taps to Parquet in cloud buckets
Stream-first CAN & J1939 toolkit for security research and AI agents — typed JSONL events, CLI, TUI, fuzzing, MCP server.
A Single Library Parser to extract meta information,static analysis and detect macros within the files.
A column-oriented approach to feature engineering. Feature engineering and machine learning: together at last!