With Snakemake, data analysis workflows are defined via an easy to read, adaptable, yet powerful specification language on top of Python. Steps are defined by "rules", which denote how to generate a set of output files from a set of input files (e.g. using a shell command). Wildcards (in curly braces) provide generalization. Dependencies between rules are determined automatically.
Build Reproducible Analytical Pipelines with Nix
Streamlines the creation of reproducible analytical pipelines using default.nix expressions generated via the rix package for reproducibility. Define derivations in R, Python or Julia, chain them into a composition of pure functions and build the resulting pipeline using Nix as the underlying end-to-end build tool. Functions to plot the pipeline as a directed acyclic graph are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the targets package.
Dagu: The workflow engine that doesn't turn into an SRE project.
Dagu is a lightweight alternative to Airflow or Cron with a Web UI. Define DAGs in a simple declarative YAML format. It supports shell commands, docker containers, k8s jobs, remote commands via SSH, and more. It was designed to be easy to use, self-contained, and require no coding, making it ideal for small teams.
What Is DSPy? How It Works, Use Cases, and Resources
DSPy is an open-source Python framework that allows developers to build language model applications using modular and declarative programming instead of relying on one-off prompting techniques.
Python Tutorial | Learn Python Programming Language - GeeksforGeeks
Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.
Track your Data Pipelines
Track and document dplyr data pipelines. As you filter, mutate, and join your way through a data set, dtrackr seamlessly keeps track of your data flow and makes publication ready documentation of a data pipeline simple.
Automatisch - Open Source Zapier Alternative
Build workflow automation without spending time and money. No code is required.
Vercel Workflow
Build durable, reliable, and observable applications and AI agents with the Workflow Development Kit (WDK).
marimo | a next-generation Python notebook
Explore data and build apps seamlessly with marimo, a next-generation Python notebook.
Cookiecutter Data Science
A project template and directory structure for Python data science projects.
Documentation — Terminal Graph
Download Terminal Graph beta, learn the shortcuts, nodes, and dataflow model.
The fish language — fish-shell 4.7.1 documentation
This document is a comprehensive overview of fish’s scripting language.
FlowBench: separating planning, fault recovery and interpretation in agentic bioinformatics
Agentic large language model (LLM) systems are being deployed in bioinformatics faster than they are understood, and single-metric evaluations conflate capabilities that fail independently. We introduce FlowBench, a benchmark that decomposes agentic bioinformatics performance into planning, fault recovery, biological interpretation, and end-to-end output-fidelity. Existing systems achieve high plan completeness, but their closed, single-provider designs prevent attribution of performance to scaffolding versus the underlying model. We therefore built FlowAgent, a modular, provider-agnostic framework whose components can be selectively disabled and whose backbone model can be swapped across providers on a shared harness, and used it to evaluate 23 models from three main providers. Three findings emerge. First, generating a valid workflow plan from a named toolchain is largely solved, whereas inferring an appropriate toolchain from biological intent alone is uniformly difficult regardless of model tier, compressing all models into a narrow 44–57% pass-rate band. Second, ablation shows that the dependency-structured plan and a completeness-reflection step drive performance, while adding a same-context validator-driven retry makes structural quality worse. Third, fault recovery and data-grounded interpretation remain unsolved. Models frequently propose fixes that force a clean exit while leaving the underlying data invalid, and data-grounded interpretation lags internal-knowledge recall by a consistent margin. Safety does not emerge from capability, and reasoning-tier models were among the least reliable at recognising unrecoverable faults. Once planning saturates, agent architecture and refusal calibration, not model scale, are the productive frontier. Availability and implementation FlowAgent and FlowBench are available under a GPLv3 licence at <https://github.com/EnteloBio/flowagent> Contact adam{at}entelo.bio ### Competing Interest Statement The authors are current employees of Entelo Bio, with APC holding equity.
Svelte Agentation
Minimal source-inspection tooling for Svelte apps, inspired by Agentation and designed for dev-only workflows.
Tool use with Claude
Connect Claude to external tools and APIs. Learn where tools execute and how the agentic loop works.