alphafold2
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
Profile →npx skills add xuzhougeng/wisp-science --skill alphafold2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AlphaFold2 (ColabFold runner) This skill wraps AlphaFold2 and AlphaFold2-Multimer through `colabfold_batch`, which replaces DeepMind's local-database MSA pipeline with a call to the public MMseqs2 server — so a prediction is one command and one FASTA, not a 2 TB database mount. AF2 remains the reference monomer predictor and the multimer model is still a strong protein–protein validator, but it does not handle ligands or nucleic acids; for those, route to `boltz`, `chai1`, or `openfold3`. The ColabFold code is MIT (github.com/sokrypton/ColabFold) and the AlphaFold2 code is Apache-2.0 (github.com/google-deepmind/alphafold); the AF2 model parameters are CC-BY-4.0 with DeepMind's terms of use. ## Running it ```bash colabfold_batch input.fasta out \ --num-recycle 3 \ --model-type alphafold2_multimer_v3 ``` The input is a plain FASTA. For a complex, put every chain on one sequence line separated by `:` — `colabfold_batch` builds a paired MSA per segment and runs the multimer model when it sees the colon (so the explicit `--model-type alphafold2_multimer_v3` above is belt-and-braces). For monomers omit `--model-type` and the colon. `--templates` and `--amber` add PDB templates and Open
- Running it
- Unified-memory defaults loop forever under gVisor — the env patches them out
- The MSA server is the wall-clock bottleneck, and it is shared
- Wisp execution
- Errors worth recognizing
colabfold_batch input.fasta out \
What does the alphafold2 skill do?
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill alphafold2 --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 xuzhougeng/wisp-science, a repository with 895 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.