Agent skill

struct-predictor

Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs boltz predict, extracts per-residue pLDDT and PAE confidence, and writes a markdown report with figures.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill struct-predictor --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 11
SKILL.md size: 5 KB
Bundled scripts: yes
Version: 0.2.0
Path: skills/clawbio/struct-predictor/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Struct Predictor You are the **Struct Predictor**, a specialised agent for protein structure prediction using Boltz-2. ## Core Capabilities 1. **Structure Prediction**: Run Boltz-2 locally on a YAML input 2. **Confidence Extraction**: Per-residue pLDDT (from CIF B-factors) and PAE matrix (from confidence JSON) 3. **Report Generation**: Markdown with pLDDT line plot, PAE heatmap, band breakdown, and reproducibility bundle 4. **Demo Mode**: Trp-cage miniprotein (20 residues, PDB 1L2Y) — runs immediately, no input required ## CLI Reference ```bash # Single protein or multi-chain complex (YAML) python skills/struct-predictor/struct_predictor.py \ --input complex.yaml --output /tmp/struct_out # Demo (Trp-cage miniprotein, PDB 1L2Y — no input needed) python skills/struct-predictor/struct_predictor.py \ --demo --output /tmp/struct_demo ``` ### Plain Text Examples Predict the structure of a single protein from a YAML file: python skills/struct-predictor/struct_predictor.py --input my_protein.yaml --output /tmp/struct_out Run the built-in Trp-cage demo (no input file needed): python skills/struct-predictor/struct_predictor.py --demo --output /tmp/struct_demo Predict a two-chain complex: p

What's inside
Steps it walks through
  1. Core Capabilities
  2. CLI Reference
  3. Plain Text Examples
  4. Output Structure
  5. YAML Complex Format
  6. MSA Options
  7. pLDDT Confidence Bands
  8. Demo Data
  9. Dependencies
  10. Citations
Ships with 10 files
  • demo_data/trpcage.yaml
  • struct_predictor.py
  • struct_predictor_core/__init__.py
  • struct_predictor_core/confidence.py
  • struct_predictor_core/io.py
  • struct_predictor_core/predict.py
  • struct_predictor_core/report.py
  • struct_predictor_core/viewer.py
  • tests/__init__.py
  • tests/test_struct_predictor.py
Commands it runs
Single protein or multi-chain complex (YAML)
python skills/struct-predictor/struct_predictor.py \
Demo (Trp-cage miniprotein, PDB 1L2Y — no input needed)
uv pip install boltz -U          # CPU
uv pip install "boltz[cuda]" -U  # GPU (recommended)
uv pip install numpy matplotlib pyyaml
More from awesome-bio-agent-skills
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About this skill
What does the struct-predictor skill do?

Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs boltz predict, extracts per-residue pLDDT and PAE confidence, and writes a markdown report with figures.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill struct-predictor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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