msa-structure-prediction-pipeline SKILL.md skillA
msa-structure-prediction-pipeline from nvidia/skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add nvidia/skills/msa-structure-prediction-pipeline@git:20260923.d232a17Pins git:20260923.d232a17 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- nvidia/skills
- Path
skills/bionemo-msa-structure-prediction-pipeline/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260923.d232a17, committed Sep 23, 2026
- Size
- 6,570 bytes
- Repository stars
- 3,871
- License
- Apache-2.0
NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# MSA Structure Prediction Pipeline Predict protein structures with high accuracy by chaining two BioNeMo NIMs: ``` Step 1: MSA-Search → Step 2: OpenFold3 (Search homologs) (Predict structure with MSA) ``` --- ## Overview Why chain these NIMs? - **MSA-Search** finds evolutionary homologs in UniRef30 and ColabFold databases using GPU-accelerated MMSeqs2. The resulting alignment provides crucial evolutionary information. - **OpenFold3** uses the MSA to improve structure prediction accuracy — especially for sequences where no close homolog exists in PDB. - Running MSA-Search first means OpenFold3 gets the full evolutionary context rather than a single-sequence prediction. --- ## Before you start Confirm with the user: 1. **Query sequence**: amino acid sequence to predict 2. **MSA depth**: how many sequences to retrieve (default 500; more = slower but more context) 3. **API mode**: hosted or local Docker? Note: local MSA-Search requires 1.4 TB of database storage — strongly recommend hosted unless the user has that infrastructure. For local Docker, do not assume MSA-Search and OpenFold3 are both on …
Read the whole file at its exact version.
How to install
mdr add nvidia/skills/msa-structure-prediction-pipeline@sha256:ffd2ae551e7d912eThe line at the top of this page pins the label git:20260923.d232a17, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_t65d2mvwykkvsdnh)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Sep 23, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (6570 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
nvidia/skills · 3,871 stars · license Apache-2.0 · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_t65d2mvwykkvsdnh GET https://markdownregistry.com/api/v1/resolve?ref=nvidia/skills/msa-structure-prediction-pipeline GET https://markdownregistry.com/api/v1/blob/ffd2ae551e7d912e059e3a8f6a975a82f08eaafbb16cf2dd20f4cf462aeb9d02
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.