Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/adaptyvbio/protein-design-skillsnpx agentmods add skills/adaptyvbio/protein-design-skills/chaiWrote 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/adaptyvbio/protein-design-skills/chai)<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/chai"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/chai/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/adaptyvbio/protein-design-skills/chai"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/chai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00107 | $0.01821 |
| Opus 5 | $0.00053 | $0.00911 |
| Sonnet 5 | $0.00021 | $0.00364 |
| Haiku 4.5 | $0.00011 | $0.00182 |
Grade A, and why
chai 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- chai1-structure-prediction — 91% identical, 44 lines differ
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chai-1 Structure Prediction
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.0+ | 12.1+ |
| GPU VRAM | 24GB | 40GB (A100) |
| RAM | 32GB | 64GB |
How to run
First time? See Getting started to set up Modal and biomodals.
Option 1: Modal
cd biomodals
modal run modal_chai1.py \
--input-faa complex.fasta \
--out-dir predictions/
GPU: A100 (40GB) | Timeout: 30min default
Option 2: Chai API (recommended)
pip install chai_lab
python -c "
import chai_lab
from chai_lab.chai1 import run_inference
# Run prediction
run_inference(
fasta_file='complex.fasta',
output_dir='predictions/',
num_trunk_recycles=3
)
"
Option 3: Local installation
git clone https://github.com/chaidiscovery/chai-lab.git
cd chai-lab
pip install -e .
chai-lab predict \
--fasta complex.fasta \
--output predictions/
FASTA Format
Protein complex
>binder
MKTAYIAKQRQISFVKSHFSRQLE...
>target
MVLSPADKTNVKAAWGKVGAHAGE...
Protein + ligand
>protein
MKTAYIAKQRQISFVKSHFSRQLE...
>ligand|smiles
CCO
Protein + DNA/RNA
>protein
MKTAYIAKQRQISFVKSHFSRQLE...
>dna
ATCGATCGATCG
Key parameters
| Parameter | Default | Range | Description |
|---|---|---|---|
num_trunk_recycles |
3 | 1-10 | Recycles (more = better) |
num_diffn_timesteps |
200 | 50-500 | Diffusion steps |
seed |
0 | int | Random seed |
Output format
predictions/
├── pred.model_idx_0.cif # Best model (CIF format)
├── pred.model_idx_1.cif # Second model
├── scores.json # Confidence scores
├── pae.npy # PAE matrix
└── plddt.npy # pLDDT values
Note: Chai-1 outputs CIF format. Convert to PDB if needed:
from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("pred", "pred.model_idx_0.cif")
io = PDBIO()
io.set_structure(structure)
io.save("pred.model_idx_0.pdb")
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.
- 11d ago First seen · 273 lines · 107 tokens per session scan A 910f20817a2e
chai is a skill published in the GitHub repository adaptyvbio/protein-design-skills (158 stars, last pushed 3mo ago), licensed MIT. It adds 107 tokens to every session and 1,821 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
chai
Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2.…
chai1-structure-prediction
Chai-1 structure prediction for protein complexes and design validation. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC…
chai1-structure-prediction
Chai-1 structure prediction for protein complexes and design validation. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC…
chai
Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2.…
alphafold
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction…
alphafold2-multimer
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5)…