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 agentmods add skills/synthetic-sciences/openscience/structure-predictionnpx skills add synthetic-sciences/openscience --skill structure-predictiongit 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/structure-prediction)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/structure-prediction"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/structure-prediction.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.01906 |
| Opus 5 | $0.00021 | $0.00953 |
| Sonnet 5 | $0.00008 | $0.00381 |
| Haiku 4.5 | $0.00004 | $0.00191 |
Grade A, and why
structure-prediction 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 yesterday.
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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structure Prediction (ESMFold)
Overview
This skill provides protein 3D structure prediction from amino acid sequences using ESMFold (Evolutionary Scale Modeling Fold). ESMFold is a single-sequence protein structure prediction model developed by Meta AI that produces accurate 3D coordinates directly from an amino acid sequence without requiring multiple sequence alignments (MSA) or template search.
Key advantages of ESMFold:
- No MSA required: Predictions run on a single sequence, making inference dramatically faster than AlphaFold2 (seconds vs. minutes/hours).
- Single GPU execution: The entire model fits on one GPU (requires ~16 GB VRAM for sequences up to ~400 residues, more for longer sequences).
- End-to-end: Takes a raw amino acid string and outputs a full PDB structure with per-residue confidence scores (pLDDT).
- Drug discovery ready: Suitable for rapid screening of target structures, variant modeling, and initial structural hypotheses.
When to Use This Skill
Use the structure-prediction skill when you need to:
- Predict a protein structure from an amino acid sequence
- Fold a protein when no experimental structure is available
- Screen multiple sequences for structural viability in batch mode
- Evaluate prediction confidence to assess reliability of modeled regions
- Compare a predicted structure against an experimental reference (e.g., from PDB)
- Identify disordered regions in a protein based on low-confidence scores
- Generate initial models for downstream molecular docking or dynamics simulations
Trigger phrases: "predict structure", "fold protein", "run ESMFold", "structure from sequence", "batch fold", "evaluate pLDDT", "compare structures"
Related Skills
- alphafold-database: If the protein has a UniProt ID, check AlphaFold DB first — pre-computed structures are instant, no GPU needed.
- esm: For generative protein design, embeddings, or inverse folding (broader than just structure prediction).
- protein-diagram: For rendering 2D diagrams of protein structures (domain maps, Ramachandran plots) — not prediction.
What ships with it
5 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.
- yesterday First seen · 175 lines · 42 tokens per session scan A 3cfc53166a77
structure-prediction is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 42 tokens to every session and 1,906 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
Other skills, from other repositories
meta-paper-write
Use this meta-skill instead of answering directly when the current user asks to draft or produce a new academic/research paper or LaTeX manuscript. It uses multi-skill orchestration for manuscript workflows that need source search, citation planning, experiment or figure/table placeholders, drafting, length checks…
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-section-author
Write one publication-style research-paper section as a bounded, citation-grounded LaTeX fragment from a writing plan, outline, citation plan, and optional figure/table context.
meta-arxiv-daily-digest-deck
Fetch the day's top arXiv submissions in a chosen category, write a structured per-paper digest, render the digest as a PPTX deck (one slide per paper), and persist the digest to long-term memory. Use for a daily 'arxiv morning briefing' — manual fire or cron-scheduled.
paper-quality-gate
Deterministic pre-compile gate for meta-paper-write. Enforces length/citation verdicts and rejects unsupported empirical-result claims when no user evidence was supplied.
paper-latex-sanitizer
Deterministically normalize safe LaTeX punctuation and replace unsupported forecast magnitudes with explicit placeholders before meta-paper-write publication gates run.