synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add synthetic-sciences/openscience --skill tamarindgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/tamarind)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/tamarind"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/tamarind/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/tamarind"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/tamarind.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00172 | $0.06485 |
| Opus 5.5 | $0.00069 | $0.02594 |
| Sonnet 5.5 | $0.00034 | $0.01297 |
| Haiku 4.5 | $0.00017 | $0.00649 |
Grade A, and why
tamarind scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl https://app.tamarind.bio/api/tools \ This is a copy
91% identical to tamarind — 402 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tamarind Bio
Tamarind Bio is a cloud platform that runs computational biology tools — structure prediction, protein and antibody design, docking, binding-affinity, MSA generation, and molecular dynamics — on managed GPUs. Users submit sequences or structures and get back predicted structures, designs, and biophysical scores, without provisioning their own hardware. It exposes hundreds of tools (AlphaFold, Boltz-2, Chai-1, RFdiffusion, ProteinMPNN, BoltzGen, ESMFold2, DiffDock, Autodock Vina, and many more) through one uniform job API.
Official docs: app.tamarind.bio/api-docs · platform UI at app.tamarind.bio
Canonical sources — fetch these, don't rely on a stale copy
Tamarind publishes live, machine-readable sources. Prefer fetching them at runtime over trusting any hardcoded list — tool names, schemas, and endpoints change frequently:
https://app.tamarind.bio/llms.txt— LLM index: links to the spec, API docs, and MCP guide.https://app.tamarind.bio/openapi.yaml— OpenAPI 3.0 spec for the 8 core job endpoints (submit-job/-batch, jobs, result, upload, files, delete-job/-file; authApiKeyAuth). Fetch it for those exact shapes. Discovery/management endpoints (/tools,/usage-statistics, pipelines, …) aren't in it — use the MCP/REST discovery tools for those.https://docs.tamarind.bio/llms.txt— documentation index; every page has a.mdform (e.g.docs.tamarind.bio/tamarind/batch.md,/tamarind/api.md,/tamarind/pipelines.md).- Live tool discovery —
GET /tools(REST) or MCPgetAvailableTools+getJobSchema(jobType)are the source of truth for what tools exist and their parameters.
This skill teaches the surface + the non-obvious behaviors those sources don't spell out (see the reference files). When in doubt about a shape, fetch openapi.yaml.
When to use this skill
Use Tamarind when the user wants to:
- Predict structure of a protein, complex, or protein-ligand system (AlphaFold, Boltz-2, Chai-1, ESMFold2, Chai/Boltz cofolding)
- Design proteins or binders (RFdiffusion, BoltzGen, BindCraft, ProteinMPNN/LigandMPNN inverse folding)
- Design or characterize antibodies/nanobodies (sequence generation, humanization, developability, immunogenicity)
- Dock small molecules to a protein (DiffDock, Autodock Vina) or predict binding affinity
- Generate MSAs for downstream folding
- Run molecular dynamics or other biophysical workflows on managed GPUs
- Batch-screen many sequences or designs through the same tool
- Chain tools into pipelines (e.g. design → fold → score) using the output of one job as the input of the next
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +5 lines a76f81fa6d04
- 1mo ago First seen · 288 lines · 172 tokens per session scan A 5c8a6ab792c9
tamarind is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 172 tokens to every session and 6,485 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to tamarind, differing in 402 lines, and is treated as a copy.
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Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.