Alphafold

Q: Why use Alphafold on TypingMind?

Because you install it once and use it with any model. Alphafold 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 Alphafold in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/alphafold. 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 Alphafold?

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 Alphafold?

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

Q: Is the Alphafold AI skill free?

Yes. It is published on GitHub by FreedomIntelligence 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

FreedomIntelligence

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 with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

Overview

Publisher	FreedomIntelligence
Repository	`OpenClaw-Medical-Skills`
Skill name	`alphafold`
Stars	3K
Forks	410
Bundled files	1
License	MIT
Links	Skill source Repository

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by FreedomIntelligence on GitHub. Read the source before you install it.

Installation

Install the Alphafold 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.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/alphafold

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/FreedomIntelligence/OpenClaw-Medical-Skills.git /tmp/OpenClaw-Medical-Skills
mkdir -p .claude/skills
cp -r /tmp/OpenClaw-Medical-Skills/skills/alphafold .claude/skills/alphafold

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

Use it in TypingMind

Enable Alphafold 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 Alphafold 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 Alphafold 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.

AlphaFold2 Structure Validation

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.10
CUDA	11.0+	12.0+
GPU VRAM	32GB	40GB (A100)
RAM	32GB	64GB
Disk	100GB	500GB (for databases)

How to run

First time? See Installation Guide to set up Modal and biomodals.

Option 1: ColabFold (recommended for multimer)

bash
cd biomodals
modal run modal_colabfold.py \
  --input-faa sequences.fasta \
  --out-dir output/

GPU: A100 (40GB) | Timeout: 3600s default

Option 2: Local installation

bash
git clone https://github.com/deepmind/alphafold.git
cd alphafold

python run_alphafold.py \
  --fasta_paths=query.fasta \
  --output_dir=output/ \
  --model_preset=monomer \
  --max_template_date=2026-01-01

Option 3: ESMFold (fast single-chain)

bash
modal run modal_esmfold.py \
  --sequence "MKTAYIAKQRQISFVK..."

Key parameters

Parameter	Default	Options	Description
`--model_preset`	monomer	monomer/multimer	Model type
`--num_recycle`	3	1-20	Recycling iterations
`--max_template_date`	-	YYYY-MM-DD	Template cutoff
`--use_templates`	True	True/False	Use template search

Output format


output/
├── ranked_0.pdb           # Best model
├── ranked_1.pdb           # Second best
├── ranking_debug.json     # Confidence scores
├── result_model_1.pkl     # Full results
├── msas/                  # MSA files
└── features.pkl           # Input features

Extracting metrics

python
import pickle

with open('result_model_1.pkl', 'rb') as f:
    result = pickle.load(f)

plddt = result['plddt']
ptm = result['ptm']
iptm = result.get('iptm', None)  # Multimer only
pae = result['predicted_aligned_error']

Sample output

Successful run


$ python run_alphafold.py --fasta_paths complex.fasta --model_preset multimer
[INFO] Running MSA search...
[INFO] Running model 1/5...
[INFO] Running model 5/5...
[INFO] Relaxing structures...

Results:
  ranked_0.pdb:
    pLDDT: 87.3 (mean)
    pTM: 0.78
    ipTM: 0.62
    PAE (interface): 8.5

Saved to output/

What good output looks like:

pLDDT: > 85 (mean, on 0-100 scale) or > 0.85 (normalized)
pTM: > 0.70
ipTM: > 0.50 for complexes
PAE_interface: < 10

Decision tree


Should I use AlphaFold?
│
├─ What are you predicting?
│  ├─ Single protein → ESMFold (faster)
│  ├─ Protein-protein complex → AlphaFold/ColabFold ✓
│  ├─ Protein + ligand → Chai or Boltz
│  └─ Batch of sequences → ColabFold ✓
│
├─ What do you need?
│  ├─ Highest accuracy → AlphaFold/ColabFold ✓
│  ├─ Fast screening → ESMFold
│  └─ MSA-free prediction → Chai or ESMFold
│
└─ Which AF2 option?
   ├─ Local installation → Full control, slow setup
   ├─ ColabFold → Easier, MSA server
   └─ Modal → Recommended for batch

Typical performance

Campaign Size	Time (A100)	Cost (Modal)	Notes
100 complexes	1-2h	~$8	With MSA server
500 complexes	5-10h	~$40	Standard campaign
1000 complexes	10-20h	~$80	Large campaign

Per-complex: ~30-60s with MSA server.

Verify

bash
find output -name "ranked_0.pdb" | wc -l  # Should match input count

Troubleshooting

Low pLDDT regions: May indicate disorder or poor design Low ipTM: Interface not confident, check hotspots High PAE off-diagonal: Chains may not interact OOM errors: Use ColabFold with MSA server instead

Error interpretation

Error	Cause	Fix
`RuntimeError: CUDA out of memory`	Sequence too long	Use A100 or split prediction
`KeyError: 'iptm'`	Running monomer on complex	Use multimer preset
`FileNotFoundError: database`	Missing MSA databases	Use ColabFold MSA server
`TimeoutError`	MSA search slow	Reduce num_recycles

Next: protein-qc for filtering and ranking.

Bundled files

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

references/multimer.md3 KB

Frequently asked questions

What does the Alphafold AI skill do?

Why use Alphafold on TypingMind?

Because you install it once and use it with any model. Alphafold 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 Alphafold in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/alphafold. 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 Alphafold?

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 Alphafold?

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

Is the Alphafold AI skill free?

Yes. It is published on GitHub by FreedomIntelligence 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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Finishing A Development Branch

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CommunityPopular

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

Receiving Code Review

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CommunityPopular

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…

Requesting Code Review

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CommunityPopular

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Systematic Debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

AlphaFold2 Structure Validation

Prerequisites

How to run

Option 1: ColabFold (recommended for multimer)

Option 2: Local installation

Option 3: ESMFold (fast single-chain)

Key parameters

Output format

Extracting metrics

Sample output

Successful run

Decision tree

Typical performance

Verify

Troubleshooting

Error interpretation

Bundled files

Frequently asked questions

Related AI Skills

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