/struct-predictor
Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs
$ npx -y skills add ClawBio/ClawBio --skill struct-predictor --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition โ
- You can call itInvoke it directly when you want it.
- Slash command
/struct-predictor
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The summary Claude sees to decide when to auto-load this skill.
Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs
SKILL.md
struct-predictor.SKILL.mdname: struct-predictor
description: 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.
license: MIT
metadata:
version: 0.2.0
openclaw:
requires:
bins:
- python3
anyBins:
- boltz
always: false
emoji: ๐งฑ
homepage: https://github.com/ClawBio/ClawBio
os:
- darwin
- linux
install:
- kind: uv
package: boltz
bins:
- boltz
comment: 'GPU: uv pip install ''boltz[cuda]'' -U'
- kind: uv
package: numpy
- kind: uv
package: matplotlib
- kind: uv
package: pyyamlStruct 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) โ runs immediately, no input required
CLI Reference
# 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 โ 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:
python skills/struct-predictor/struct_predictor.py --input complex_ab.yaml --output /tmp/complex_out
Output Structure
output_dir/
boltz_results_[name]/ # Boltz native output
lightning_logs/ # training/eval logs
predictions/
[name]/
[name]_model_0.cif # predicted structure (pLDDT in B-factors)
confidence_[name]_model_0.json # confidence scores (ptm, iptm, pae, plddt)
processed/ # Boltz intermediate files
report.md # primary markdown report
viewer.html # self-contained 3Dmol.js 3D viewer (open in browser)
result.json # machine-readable summary
figures/
plddt.png # per-residue pLDDT confidence plot
pae.png # PAE inter-residue error heatmap
reproducibility/
commands.sh # exact boltz predict command used
environment.txt # boltz version snapshotYAML Complex Format
version: 1
sequences:
- protein:
id: A
sequence: ACDEFGHIKLMNPQRSTVWY
msa: empty # runs offline; replace with a path to a .a3m file for MSA-guided prediction
- protein:
id: B
sequence: NPQRSTVWYLSDEDFKAVFG
msa: emptyMSA Options
| `msa` value | Behaviour | |---|---| | `msa: empty` | No MSA โ fast, fully offline, suitable for short/designed sequences | | `msa: /path/to/file.a3m` | Pre-computed MSA โ best accuracy for natural proteins | | *(omit field)* | Boltz errors unless `--use_msa_server` is passed at predict time |
pLDDT Confidence Bands
| Band | pLDDT Range | Interpretation | |------|------------|----------------| | Very high | โฅ 90 | Backbone accurate to ~0.5 ร
| | High | 70โ90 | Generally reliable | | Low | 50โ70 | Disordered or uncertain | | Very low | < 50 | Likely intrinsically disordered |
Demo Data
| Item | Value | |------|-------| | File | `skills/struct-predictor/demo_data/trpcage.yaml` | | Sequence | `NLYIQWLKDGGPSSGRPPPS` | | Name | Trp-cage miniprotein | | Length | 20 residues | | PDB reference | 1L2Y |
Dependencies
uv pip install boltz -U # CPU
uv pip install "boltz[cuda]" -U # GPU (recommended)
uv pip install numpy matplotlib pyyaml
Citations
- Passaro S et al. (2025) *Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction*. bioRxiv. doi:10.1101/2025.06.14.659707. PMID: 40667369; PMCID: PMC12262699.
- Wohlwend J et al. (2024) *Boltz-1: Democratizing Biomolecular Interaction Modeling*. bioRxiv. doi:10.1101/2024.11.19.624167
- Jumper J et al. (2021) *AlphaFold2 pLDDT definition*. Nature. doi:10.1038/s41586-021-03819-2
Read more
name: struct-predictor
description: 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.
license: MIT
metadata:
version: 0.2.0
openclaw:
requires:
bins:
- python3
anyBins:
- boltz
always: false
emoji: ๐งฑ
homepage: https://github.com/ClawBio/ClawBio
os:
- darwin
- linux
install:
- kind: uv
package: boltz
bins:
- boltz
comment: 'GPU: uv pip install ''boltz[cuda]'' -U'
- kind: uv
package: numpy
- kind: uv
package: matplotlib
- kind: uv
package: pyyamlStruct 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) โ runs immediately, no input required
CLI Reference
# 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 โ 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:
python skills/struct-predictor/struct_predictor.py --input complex_ab.yaml --output /tmp/complex_out
Output Structure
output_dir/
boltz_results_[name]/ # Boltz native output
lightning_logs/ # training/eval logs
predictions/
[name]/
[name]_model_0.cif # predicted structure (pLDDT in B-factors)
confidence_[name]_model_0.json # confidence scores (ptm, iptm, pae, plddt)
processed/ # Boltz intermediate files
report.md # primary markdown report
viewer.html # self-contained 3Dmol.js 3D viewer (open in browser)
result.json # machine-readable summary
figures/
plddt.png # per-residue pLDDT confidence plot
pae.png # PAE inter-residue error heatmap
reproducibility/
commands.sh # exact boltz predict command used
environment.txt # boltz version snapshotYAML Complex Format
version: 1
sequences:
- protein:
id: A
sequence: ACDEFGHIKLMNPQRSTVWY
msa: empty # runs offline; replace with a path to a .a3m file for MSA-guided prediction
- protein:
id: B
sequence: NPQRSTVWYLSDEDFKAVFG
msa: emptyMSA Options
| `msa` value | Behaviour | |---|---| | `msa: empty` | No MSA โ fast, fully offline, suitable for short/designed sequences | | `msa: /path/to/file.a3m` | Pre-computed MSA โ best accuracy for natural proteins | | *(omit field)* | Boltz errors unless `--use_msa_server` is passed at predict time |
pLDDT Confidence Bands
| Band | pLDDT Range | Interpretation | |------|------------|----------------| | Very high | โฅ 90 | Backbone accurate to ~0.5 ร | | High | 70โ90 | Generally reliable | | Low | 50โ70 | Disordered or uncertain | | Very low | < 50 | Likely intrinsically disordered |
Demo Data
| Item | Value | |------|-------| | File | `skills/struct-predictor/demo_data/trpcage.yaml` | | Sequence | `NLYIQWLKDGGPSSGRPPPS` | | Name | Trp-cage miniprotein | | Length | 20 residues | | PDB reference | 1L2Y |
Dependencies
uv pip install boltz -U # CPU uv pip install "boltz[cuda]" -U # GPU (recommended) uv pip install numpy matplotlib pyyaml
Citations
- Passaro S et al. (2025) *Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction*. bioRxiv. doi:10.1101/2025.06.14.659707. PMID: 40667369; PMCID: PMC12262699.
- Wohlwend J et al. (2024) *Boltz-1: Democratizing Biomolecular Interaction Modeling*. bioRxiv. doi:10.1101/2024.11.19.624167
- Jumper J et al. (2021) *AlphaFold2 pLDDT definition*. Nature. doi:10.1038/s41586-021-03819-2
๐ฆ ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free.
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