Mosaic

What it does

When your agent uses it

• Composing several structure
• Sequence models into one design objective
• Optimizing binders against a custom loss rather than a fixed pipeline
• Wanting gradient descent over sequence space in the style of ColabDesign

Example prompts

• “/mosaic”

Requirements

• Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 59dd633. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • uv
  • git

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com

  Also links to:

  • blog.escalante.bio

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mosaic loads about 1.7k tokens when it runs. Until then it costs about 154 tokens; SKILL.md has 616 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~154

When it runs · the whole SKILL.md, loaded when a task matches

~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 616 words, ~1,660 tokens.

name

mosaic

description

Multi-objective, gradient-based protein binder design with Mosaic. Use this skill when: (1) Composing several structure or sequence models into one design objective, (2) Optimizing binders against a custom loss rather than a fixed pipeline, (3) Wanting gradient descent over sequence space in the style of ColabDesign, RSO, or BindCraft but with interchangeable predictors, (4) Letting the optimizer choose the epitope instead of fixing hotspots. For an end-to-end binder pipeline with default filters, use bindcraft. For all-atom diffusion design, use boltzgen. For backbone-only generation, use rfdiffusion.

license

MIT

category

design-tools

tags

design, gradient-optimization, multi-objective, jax, binder

git clone https://github.com/escalante-bio/mosaic && cd mosaic
uv sync --group jax-cuda      # or --group jax-tpu / --group jax-cpu
uv add jax[cuda12]            # may be needed for a GPU build
uv run marimo edit examples/example_notebook.py

import mosaic.losses.structure_prediction as sp

# Compose a loss from interface, confidence, and inverse-folding terms
design_loss = (
    sp.BinderTargetContact()
    + sp.WithinBinderContact()
    + 0.05 * sp.TargetBinderPAE()
    + 0.05 * sp.BinderTargetPAE()
    + 0.025 * sp.IPTMLoss()
    + 0.1 * sp.PLDDTLoss()
)

from mosaic.models.boltz2 import Boltz2

boltz2 = Boltz2()
ranking_loss = boltz2.build_multisample_loss(
    loss=1.00 * sp.IPTMLoss()
    + 0.5 * sp.TargetBinderIPSAE()
    + 0.5 * sp.BinderTargetIPSAE(),
    features=design_features,
    num_samples=6,
    recycling_steps=3,
)

Should I use Mosaic?
│
├─ Need a custom objective across multiple models? → Mosaic
├─ Want one-click binders with default filters?    → bindcraft
├─ Want all-atom diffusion design?                  → boltzgen
└─ Want backbone-only diversity?                    → rfdiffusion + proteinmpnn

© adaptyvbio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/mosaic of adaptyvbio/protein-design-skills.

Open the folder on GitHubat commit 59dd633

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.

• NeverSight/learn-skills.devdata/skills-md/adaptyvbio/protein-design-skills/mosaic