Open Science is a local-first, model-agnostic workbench for reproducible scientific research. Scientists use its AI agents, Python and R execution, data connectors, and traceable outputs for tasks such as literature review, analysis, simulation, and visualization across macOS, Windows, and Linux.
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 aipoch/open-science --skill boltzgit clone --depth 1 https://github.com/aipoch/open-scienceWrote 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/aipoch/open-science/boltz)<a href="https://agentmods.dev/skills/aipoch/open-science/boltz"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/boltz/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/aipoch/open-science/boltz"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/boltz.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Prompt Injection · line 112 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00088 | $0.01482 |
| Opus 5.5 | $0.00035 | $0.00593 |
| Sonnet 5.5 | $0.00018 | $0.00296 |
| Haiku 4.5 | $0.00009 | $0.00148 |
Grade A, and why
boltz scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Boltz-2
Boltz-2 is the open-weights diffusion co-folder closest in surface to
AlphaFold3: a YAML describing protein, DNA, RNA, and ligand chains in, mmCIF
plus pTM/ipTM/pLDDT confidences out, with an optional small-molecule affinity
head. Among our four co-fold skills it is the default for binder-validation
campaigns — fully open MIT weights and the fastest sampler; pick chai1 when
you want a second independent model for consensus, openfold3 when AF3-faithful
settings matter, and esmfold2 when you can live without an MSA. Code and
weights are MIT (PyPI boltz, github.com/jwohlwend/boltz).
Running it
# complex.yaml
version: 1
sequences:
- protein:
id: A
sequence: MVTPEGNVSLVDESLLVGVTDEDRAVRS... # target
- protein:
id: B
sequence: AIQRTPKIQVYSRHPAENG... # binder
- ligand:
id: L
smiles: 'N[C@@H](Cc1ccc(O)cc1)C(=O)O' # or ccd: SAH
boltz predict complex.yaml \
--use_msa_server --out_dir out/ --recycling_steps 3 --diffusion_samples 5
Each protein chain needs an MSA; without one the run exits before the model
loads. --use_msa_server queries api.colabfold.com (expect a 30–90 s pause
per chain) and is the right default unless you already have an .a3m to name
under msa: in the YAML. Setting msa: empty forces single-sequence mode —
that is an accuracy sacrifice, not a speed or memory optimization, because the
MSA search runs on CPU before the GPU stage starts.
Per input the output lands at out/boltz_results_complex/predictions/complex/.
Read confidence_complex_model_0.json first: iptm > 0.5 is the community
pass line for an interface, complex_plddt > 0.7 for the fold itself, and
confidence_score is the weighted aggregate the structures are ranked by.
Structures themselves are complex_model_{0..N-1}.cif (or .pdb with
--output_format pdb).
Affinity head
Add a properties: block naming one ligand chain as the binder and Boltz-2
predicts protein–small-molecule binding affinity alongside the structure:
What ships with it
1 file 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 First seen · 123 lines · 88 tokens per session scan A 3971ac101972
boltz is a skill published in the GitHub repository aipoch/open-science (5,354 stars, last pushed today), licensed Apache-2.0. It adds 88 tokens to every session and 1,482 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
figure-style
Correctness and legibility checklist for publication figures, plus a matplotlib sidecar. Load before plotting anything and call applyfigurestyle() (role-mapped font ladder, outward ticks, frameless legends, 300-dpi saves, CJK-safe fonts). Covers data fidelity, label budgets, axis/colour/type rules, chart choice by…
journal-club-ppt
Instructions for turning a scientific paper PDF into a journal-club presentation or literature report. They require reconstructing the paper's scientific logic before creating the slides.
research-integrity-audit
A research-integrity review of a scientific manuscript's figures and reported numbers. It looks for possible image reuse or alteration and numerical patterns that may indicate data inconsistencies.
analysis-workflow
Organize multi-step scientific analyses into reproducible, self-contained modules. Use for workflows such as QC→PCA→DEG→GSEA that produce scripts, inputs, figures, tables, and methods. Creates a stable module layout, records exact inputs/parameters/package and database versions in each module README, keeps large data…
audit-biomedical-paper-evidence
A reviewer for life-science and biomedical papers, PDFs, figures, captions, and screenshots. It examines the scientific question, experiments, evidence, causality, and limitations.
literature-review
Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links.