alphafold2
Predict or audit protein structures with AlphaFold2-style workflows.
Skill by ParamJani21
Install
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
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: security, research, productivity
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
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.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jul 5, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/ParamJani21/feynman
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Run or prepare Chai-1 structure predictions for biomolecular complexes.
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Cite this page
alphafold2 skill. Agent Mart. https://agentmart.dev/skills/paramjani21-alphafold2 (last updated 2026-10-24)