alphafold-database-access SKILL.md skillA
alphafold-database-access from fridrichmethod/awesome-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add fridrichmethod/awesome-skills/alphafold-database-access@git:20260520.7a7a671Pins git:20260520.7a7a671 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- fridrichmethod/awesome-skills
- Path
skills/alphafold-database-access/SKILL.md- Versions recorded
- 2
- Latest version
- git:20260520.7a7a671, committed May 20, 2026
- First version recorded
- May 17, 2026
- Size
- 16,625 bytes
- Repository stars
- 15
- License
- a license file GitHub does not identify
Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.
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
# AlphaFold Database Access ## Overview AlphaFold DB is a public repository of AI-predicted 3D protein structures for over 200 million proteins, maintained by DeepMind and EMBL-EBI. Access predictions via BioPython or REST API, download coordinate files in multiple formats, analyze confidence metrics, and retrieve bulk proteome datasets via Google Cloud. ## When to Use - Retrieving AI-predicted protein structures by UniProt accession - Downloading PDB/mmCIF coordinate files for structural analysis or docking - Analyzing prediction confidence (pLDDT per-residue, PAE domain-level) - Bulk-downloading entire proteome predictions via Google Cloud - Comparing predicted structures with experimental PDB structures - Building structural models for proteins lacking experimental data - Identifying high-confidence binding sites for drug discovery - For experimental structures only → use PDB directly - For running AlphaFold predictions → use ColabFold or local AlphaFold ## Prerequisites ```bash # Core (BioPython for structure access) pip install biopython requests numpy matplotlib # Optional: Google Cloud for bulk access pip install google-cloud-bigquery google-cloud-storage ``` …
Read the whole file at its exact version.
How to install
mdr add fridrichmethod/awesome-skills/alphafold-database-access@sha256:6763788d70567945The line at the top of this page pins the label git:20260520.7a7a671, 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_anucbpp4mkfpkxx3)
1 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 2 versions of this file, between May 17, 2026 and May 20, 2026, about one every 4 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260520.7a7a671 latest | 2026-05-20 | 7a7a671 | 16,625 B | A | view · diff |
| git:20260517.81511a7 | 2026-05-17 | 81511a7 | 16,631 B | A | view |
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 (16625 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
fridrichmethod/awesome-skills · 15 stars · license a license file GitHub does not identify · pushed 2026-10-18 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_anucbpp4mkfpkxx3 GET https://markdownregistry.com/api/v1/resolve?ref=fridrichmethod/awesome-skills/alphafold-database-access GET https://markdownregistry.com/api/v1/blob/6763788d7056794573e26aeda9512b91a842e0b3d18c332109583d28ab3b9838
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.
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