Scientific Tooling is an umbrella home for scientific tools, protocols, workflows, and articles designed for the AI Agent era.
We treat AI agents as first-class components in scientific systems, with explicit tool contracts, protocol boundaries, reproducible execution, and verifiable outputs.
Build practical, open, and reproducible infrastructure where humans and AI agents collaborate across the scientific lifecycle:
- framing questions
- designing protocols
- executing workflows
- validating results
- documenting rationale
- publishing reproducible outputs
- agent-ready tools with typed inputs and structured outputs
- protocol definitions for multi-step research execution
- workflow orchestration with human approval checkpoints
- provenance, lineage, and auditability by default
- evaluation harnesses for correctness, reliability, and reproducibility
- clear and stable interfaces for humans and agents
- structured metadata and machine-readable contracts
- deterministic execution where possible
- robust error handling and observability
Typical targets include CLIs, APIs, workflow runners, adapters, validation utilities, and experiment execution surfaces that agents can invoke safely.
- playbooks for human-agent collaboration
- review checkpoints and approval gates
- escalation paths for uncertainty and failure
- versioned environments and dependencies
- provenance and audit trails
- data/model lineage and validation pipelines
The goal is not just automation, but replayability, inspection, and controlled handoff across humans, agents, and software systems.
- implementation guides and design patterns
- anti-patterns and operational lessons
- domain case studies and benchmarking practices
- Reproducibility over novelty
- Transparency over opacity
- Modularity over monoliths
- Safety over speed
- Human accountability for scientific claims
We consider agent-enabled research infrastructure credible when it includes:
- versioned environments, prompts, and protocols
- machine-readable tool and data contracts
- structured execution traces and artifact capture
- validation against benchmarks or scientific invariants
- explicit approval paths for high-impact actions
- structured-intelligence: reusable agents, skills, prompts, workflows, and validation assets for AI-assisted coding, research, and writing.
- research-knowledge-substrate: an agent-first local research graph for ingesting papers, extracting claims, linking evidence, and querying a traceable research workspace.
We welcome contributions from researchers, engineers, and technical writers.
- Pick an issue labeled good first issue or help wanted.
- Open a draft pull request early for feedback.
- Include tests and update docs for behavior changes.