This repository contains the prompt, skill, agent, and MCP server assets for a Claude Science style scientific computing assistant. The contents are mostly runtime-facing configuration and documentation: skills teach the assistant how to perform domain workflows, agents define specialized profiles, and MCP servers expose scientific data and chemistry tools.

The base prompt and bundled skills in this repository are sourced from the Claude Science product. The public product page is available for context: https://claude.com/product/claude-science.

The skills/ directory includes workflows for several broad areas:

• Biomolecular modeling and design: alphafold2, boltz, chai1, openfold3, esmfold2, fair-esm2, diffdock, proteinmpnn, ligandmpnn, solublempnn, evo2, and borzoi.
• Scientific analysis and reporting: literature-review, pdf-explore, figure-style, figure-composer, paper-narrative, and indication-dossier.
• Single-cell and omics workflows: scgpt and scvi-tools.
• Runtime and environment operations: remote-compute-modal, remote-compute-ssh, compute-env-setup, managed-model-endpoints, and using-model-endpoint.
• Product and customization workflows: customize, skill-creator, product-self-knowledge, and self-awareness.

Most skills start with YAML front matter:

---
name: example-skill
description: When and why this skill should be loaded.
license: Apache-2.0
---

The body of SKILL.md should give the operational procedure, expected inputs, failure modes, validation steps, and any tool-specific constraints needed for reliable execution.

Agent profiles live under agents/<name>/metadata.yaml.

• operon is the general-purpose scientific computing agent.
• onboarding supports first-run user onboarding.
• reviewer audits another agent transcript for unsupported claims, fabrication, or plan deviation.
• bookmarker selects transcript spans worth preserving as navigation breadcrumbs.

These files are configuration assets, not standalone applications. Keep prompt changes tightly scoped and preserve any eval or benchmark notes near the prompt they justify.

The mcp-servers/ directory contains bundled MCP integrations:

• bio-tools vendors multiple Python packages for biological and biomedical data retrieval. run_server.py launches a named stdio server from mcp-servers/bio-tools/lib/.
• ketcher-chemistry contains a chemistry MCP server and widget assets for structure editing and chemistry workflows.

The MCP code is runtime infrastructure. When changing it, prefer narrow edits and verify both the server entry point and the shape of returned tool data.

Useful inspection commands:

# List all skills
find skills -maxdepth 2 -name SKILL.md | sort

# List agent profiles
find agents -maxdepth 2 -name metadata.yaml | sort

# Show launchable bio-tools MCP servers
find mcp-servers/bio-tools/lib -maxdepth 2 -name server.py \
  | sed 's#^mcp-servers/bio-tools/lib/##; s#/server.py$##' \
  | sort

Before editing a skill or prompt:

1. Read the relevant SKILL.md or metadata.yaml completely.
2. Preserve existing safety, artifact, provenance, and validation rules unless the change explicitly updates them.
3. Keep examples executable and aligned with the current runtime surface.
4. Add or update references next to the skill when a workflow depends on external documentation or non-obvious operational knowledge.

• Use ASCII in new files unless the surrounding file already requires a wider character set.
• Avoid broad rewrites of prompts or generated MCP bundles without a concrete regression target.
• Treat bundled third-party services, model weights, and APIs as dependencies: document licenses, terms, privacy implications, and data movement in the relevant skill metadata when applicable.
• If a skill ships helper code such as kernel.py, keep the markdown instructions and helper APIs in sync.