co-scientist-alphafold-structures

name	co-scientist-alphafold-structures
description	AlphaFold structure prediction skill. Protein structure retrieval from AlphaFold DB, pLDDT confidence scoring, structural alignment, and downstream structural analysis. Use when working with protein structure retrieval from alphafold db, plddt confidence scoring, structural alignment.
tu_tools	[{"key":"alphafold","name":"AlphaFold DB"}]

AlphaFold structure prediction

AlphaFold structure prediction skill. Protein structure retrieval from AlphaFold DB, pLDDT confidence scoring, structural alignment, and downstream structural analysis.

Use This Skill When

• Protein structure retrieval from AlphaFold DB.
• PLDDT confidence scoring.
• Structural alignment.
• Downstream structural analysis.

Required Inputs

• Research objective, decision target, or hypothesis.
• Available data, source constraints, and domain assumptions.
• Required outputs, success metrics, and deadline or reproducibility constraints.

Workflow

1. Confirm scope, assumptions, and the exact artifact set to save.
2. Apply the narrowest domain method that answers the request with defensible evidence.
3. Save code, tables, figures, and intermediate outputs to files instead of chat-only output.
4. State limitations, uncertainty, and any validation or sensitivity checks performed.
5. Append skill selection, handoff I/O, and file writes to logs/process-log.jsonl.

Deliverables

• report.md: concise method, results, interpretation, and file inventory in the user's language.
• results/: structured outputs, metrics, model artifacts, or extracted findings.
• figures/: English-only charts, diagrams, or panels when visual output is needed.
• data/: processed or derived datasets when transformation occurs.

Available Tools (MCP)

External tools available via ToolUniverse MCP server. Falls back to Python requests + public REST APIs when MCP is unavailable.

Source	Tool	Description
AlphaFold DB	AlphaFold_get_prediction	AlphaFold DB API
AlphaFold DB	AlphaFold_search	AlphaFold DB API

Quality Gates

• The selected method matches the scientific question and stated assumptions.
• Outputs are reproducible, saved to files, and traceable from inputs to conclusions.
• Missing data, uncertainty, bias, and hard limits are made explicit.
• report.md and logs/process-log.jsonl reference the generated artifacts.
• No essential result remains chat-only.

If any gate fails: identify the specific failing check, fix the issue, and re-validate before proceeding.

Gotchas

• PDB structures may contain missing residues or alternate conformations. Check structure completeness before analysis
• Docking scores are relative, not absolute binding affinities. Use for ranking, not predicting Kd values
• Protein identifiers differ across databases (UniProt, PDB, RefSeq). Map to a canonical namespace before integration

Validation Loop

1. Execute analysis and generate outputs
2. Check:
   • Method selection matches the research question and stated assumptions
   • All outputs are saved to files (no chat-only results)
   • Limitations and uncertainty are explicitly stated
   • logs/process-log.jsonl is updated with execution trace
3. If any check fails:
   • Identify the failing gate
   • Fix the specific issue
   • Re-run validation
4. Proceed only after all gates pass