Struct Predictor

Q: Why use Struct Predictor on TypingMind?

Because you install it once and use it with any model. Struct Predictor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

Q: How do I install Struct Predictor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ClawBio/ClawBio/tree/main/skills/struct-predictor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Q: Which AI models can use Struct Predictor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

Q: How many AI models can I use with Struct Predictor?

As many as you like. As long as a model supports skills, you can use Struct Predictor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Q: Is the Struct Predictor AI skill free?

Yes. It is published on GitHub by ClawBio under the MIT license. You only pay your own AI provider for the tokens you use.

Q: What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

Q: How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

OrganizationPopular

ClawBio

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.

Overview

Publisher	ClawBio
Repository	`ClawBio`
Skill name	`struct-predictor`
Stars	1.2K
Forks	280
Bundled files	10
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
10 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ClawBio on GitHub. Read the source before you install it.

Installation

Install the Struct Predictor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ClawBio/ClawBio/tree/main/skills/struct-predictor
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/ClawBio/ClawBio.git /tmp/ClawBio
mkdir -p .claude/skills
cp -r /tmp/ClawBio/skills/struct-predictor .claude/skills/struct-predictor

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Struct Predictor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Struct Predictor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Struct Predictor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Struct Predictor

You are the Struct Predictor, a specialised agent for protein structure prediction using Boltz-2.

Core Capabilities

Structure Prediction: Run Boltz-2 locally on a YAML input
Confidence Extraction: Per-residue pLDDT (from CIF B-factors) and PAE matrix (from confidence JSON)
Report Generation: Markdown with pLDDT line plot, PAE heatmap, band breakdown, and reproducibility bundle
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:


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 snapshot

YAML Complex Format

yaml
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: empty

MSA Options

`msa` value	Behaviour
`msa: empty`	No MSA — fast, fully offline, suitable for short/designed sequences
`msa: /path/to/file.a3m`	Pre-computed MSA — 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	≥ 90	Backbone accurate to ~0.5 Å
High	70–90	Generally reliable
Low	50–70	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

bash
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Struct Predictor AI skill do?

Why use Struct Predictor on TypingMind?

Because you install it once and use it with any model. Struct Predictor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Struct Predictor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ClawBio/ClawBio/tree/main/skills/struct-predictor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Struct Predictor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Struct Predictor?

As many as you like. As long as a model supports skills, you can use Struct Predictor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Struct Predictor AI skill free?

Yes. It is published on GitHub by ClawBio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Struct Predictor

Core Capabilities

CLI Reference

Plain Text Examples

Output Structure

YAML Complex Format

MSA Options

pLDDT Confidence Bands

Demo Data

Dependencies

Citations

Bundled files

Frequently asked questions

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