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alphafold2

Predict or audit protein structures with AlphaFold2-style workflows.

Skill by ParamJani21

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/paramjani21-alphafold2

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About

Predict or audit protein structures with AlphaFold2-style workflows. Use when a research task needs monomer/multimer structure prediction, MSA/template handling, confidence metrics, or comparison against PDB/AlphaFold references.

Use cases: security, research, productivity

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/alphafold2

What's inside

Skill name
alphafold2
Description
Predict or audit protein structures with AlphaFold2-style workflows. Use when a research task needs monomer/multimer structure prediction, MSA/template handling, confidence metrics, or comparison against PDB/AlphaFold references.
License
MIT
Instructions
15 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# AlphaFold2

Use this skill for protein-structure prediction or parity checks around AlphaFold2-style outputs.

Workflow:

1. Capture the biological question, sequence identifiers, FASTA input, oligomer state, organism, and expected cofactors or partners.
2. Verify the execution route before running: local install, managed endpoint, Modal/SSH job, or a documented public source. Do not assume weights, databases, or GPUs exist.
3. Save inputs, command or endpoint payload, model settings, stdout/stderr, and raw outputs under the active Feynman artifact folder.
4. Report pLDDT, PAE, ranking confidence, chain coverage, truncation, templates/MSA provenance, and any residues or interfaces that should not be trusted.
5. Compare against known structures or AlphaFold DB records when the claim depends on novelty, domain movement, interface geometry, or mutation impact.

Outputs should include the FASTA, predicted PDB/mmCIF, confidence files when available, a short method note, and a `.provenance.md` sidecar.

From ParamJani21/feynman, MIT.

More from ParamJani21/feynman

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Last commit (this folder)
Jul 5, 2026 (3 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

alphafold2 skill. Agent Mart. https://agentmart.dev/skills/paramjani21-alphafold2 (last updated 2026-10-24)