Alphafold Database Fetch And Analyze
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
ToolUniverse workflow — Protein Structure Retrieval. An agent skill from lamm-mit/scienceclaw.
$ npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lamm-mit/scienceclaw protein-structure-retrieval --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/protein-structure-retrieval .claude/skills/protein-structure-retrieval && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "protein-structure-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/protein-structure-retrieval into .claude/skills/protein-structure-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-structure-retrieval", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/lamm-mit/scienceclaw/tree/main/skills/protein-structure-retrievalType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lamm-mit/scienceclaw protein-structure-retrieval --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/protein-structure-retrieval .agents/skills/protein-structure-retrieval && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "protein-structure-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/protein-structure-retrieval into .agents/skills/protein-structure-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-structure-retrieval", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lamm-mit/scienceclaw protein-structure-retrieval --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/protein-structure-retrieval .cursor/skills/protein-structure-retrieval && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "protein-structure-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/protein-structure-retrieval into .cursor/skills/protein-structure-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-structure-retrieval", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/lamm-mit/scienceclaw.git --path skills/protein-structure-retrieval--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lamm-mit/scienceclaw protein-structure-retrieval --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/protein-structure-retrieval .gemini/skills/protein-structure-retrieval && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "protein-structure-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/protein-structure-retrieval into .gemini/skills/protein-structure-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-structure-retrieval", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install lamm-mit/scienceclaw protein-structure-retrievalInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/protein-structure-retrieval .github/skills/protein-structure-retrieval && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "protein-structure-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/protein-structure-retrieval into .github/skills/protein-structure-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-structure-retrieval", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lamm-mit/scienceclaw protein-structure-retrieval --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/protein-structure-retrieval .opencode/skills/protein-structure-retrieval && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "protein-structure-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/protein-structure-retrieval into .opencode/skills/protein-structure-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-structure-retrieval", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
protein-structure-retrievalToolUniverse workflow — Protein Structure Retrieval. An agent skill from lamm-mit/scienceclaw.
Protein Structure Retrieval is an agent skill from lamm-mit/scienceclaw. ToolUniverse workflow — Protein Structure Retrieval
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/run.py`).
It sits in Research & Science, covering Protein structure and design. It works with AlphaFold and UniProt. The licence is Apache-2.0.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit ab9aba1. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 2 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
rcsb.orgebi.ac.ukalphafold.ebi.ac.ukFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Protein Structure Retrieval loads about 2.8k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 621 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 621 words, ~2,834 tokens.
.claude/skills/protein-structure-retrieval/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Retrieve protein structures with proper disambiguation, quality assessment, and comprehensive metadata.
IMPORTANT: Always use English terms in tool calls (protein names, organism names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
Phase 0: Clarify (if needed)
↓
Phase 1: Disambiguate Protein Identity
↓
Phase 2: Retrieve Structures (Internal)
↓
Phase 3: Report Structure ProfileAsk the user ONLY if:
Skip clarification for:
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
# Strategy depends on input type
if user_provided_pdb_id:
# Direct structure retrieval
pdb_id = user_provided_pdb_id.upper()
elif user_provided_uniprot:
# Get UniProt info, then search structures
uniprot_id = user_provided_uniprot
# Can also get AlphaFold structure
af_structure = tu.tools.alphafold_get_structure_by_uniprot(
uniprot_id=uniprot_id
)
elif user_provided_protein_name:
# Search by name
result = tu.tools.search_structures_by_protein_name(
protein_name=protein_name
)Common ambiguous terms:
| Term | Ambiguity | Resolution |
|---|---|---|
| "kinase" | Hundreds of kinases | Ask which kinase (EGFR, CDK2, etc.) |
| "receptor" | Many receptor types | Specify receptor family |
| "protease" | Multiple families | Ask serine/cysteine/metallo/etc. |
| "hemoglobin" | Clear | Proceed (α/β chain specified if needed) |
| "insulin" | Clear | Proceed |
Retrieve all data silently. Do NOT narrate the search process.
# Search by protein name
result = tu.tools.search_structures_by_protein_name(
protein_name=protein_name
)
# Filter results by quality
high_res = [
entry for entry in result["data"]
if entry.get("resolution") and entry["resolution"] < 2.5
]For each relevant structure:
pdb_id = "4INS"
# Basic metadata
metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id)
# Experimental details
exp_details = tu.tools.get_protein_experimental_details_by_pdb_id(
pdb_id=pdb_id
)
# Resolution (if X-ray)
resolution = tu.tools.get_protein_resolution_by_pdb_id(pdb_id=pdb_id)
# Bound ligands
ligands = tu.tools.get_protein_ligands_by_pdb_id(pdb_id=pdb_id)
# Similar structures
similar = tu.tools.get_similar_structures_by_pdb_id(
pdb_id=pdb_id,
cutoff=2.0
)# Entry summary
summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id)
# Molecular entities
molecules = tu.tools.pdbe_get_molecules(pdb_id=pdb_id)
# Binding sites
binding_sites = tu.tools.pdbe_get_binding_sites(pdb_id=pdb_id)# When no experimental structure exists, or for comparison
if uniprot_id:
af_structure = tu.tools.alphafold_get_structure_by_uniprot(
uniprot_id=uniprot_id
)| Primary | Fallback | Notes |
|---|---|---|
| RCSB search | PDBe search | Regional availability |
| get_protein_metadata | pdbe_get_entry_summary | Alternative source |
| Experimental structure | AlphaFold prediction | No experimental structure |
| get_protein_ligands | pdbe_get_binding_sites | Ligand info unavailable |
Present as a Structure Profile Report. Hide search process.
# Protein Structure Profile: [Protein Name]
**Search Summary**
- Query: [protein name/PDB ID]
- Organism: [species]
- Structures Found: [N] experimental, [M] AlphaFold
---
## Best Available Structure
### [PDB ID]: [Title]
| Attribute | Value |
|-----------|-------|
| **PDB ID** | [pdb_id] |
| **UniProt** | [uniprot_id] |
| **Organism** | [species] |
| **Method** | X-ray / Cryo-EM / NMR |
| **Resolution** | [X.XX] Å |
| **Release Date** | [date] |
**Quality Assessment**: ●●● High / ●●○ Medium / ●○○ Low
### Experimental Details
| Parameter | Value |
|-----------|-------|
| **Method** | [X-ray crystallography] |
| **Resolution** | [1.9 Å] |
| **R-factor** | [0.18] |
| **R-free** | [0.21] |
| **Space Group** | [P 21 21 21] |
### Structure Composition
| Component | Count | Details |
|-----------|-------|---------|
| **Chains** | [N] | [A (enzyme), B (inhibitor)] |
| **Residues** | [N] | [coverage %] |
| **Ligands** | [N] | [list ligand names] |
| **Waters** | [N] | |
| **Metals** | [N] | [Zn, Mg, etc.] |
### Bound Ligands
| Ligand ID | Name | Type | Binding Site |
|-----------|------|------|--------------|
| [ATP] | Adenosine triphosphate | Substrate | Active site |
| [MG] | Magnesium ion | Cofactor | Catalytic |
### Binding Site Details
For drug discovery applications:
**Site 1: Active Site**
- Location: Chain A, residues 45-89
- Key residues: Asp45, Glu67, His89
- Pocket volume: [X] ų
- Druggability: High/Medium/Low
---
## Alternative Structures
Ranked by quality and relevance:
| Rank | PDB ID | Resolution | Method | Ligands | Notes |
|------|--------|------------|--------|---------|-------|
| 1 | [4INS] | 1.9 Å | X-ray | Zn | Best resolution |
| 2 | [3I40] | 2.1 Å | X-ray | Zn, phenol | With inhibitor |
| 3 | [1TRZ] | 2.3 Å | X-ray | None | Porcine |
---
## AlphaFold Prediction
### AF-[UniProt]-F1
| Attribute | Value |
|-----------|-------|
| **UniProt** | [uniprot_id] |
| **Model Version** | [v4] |
| **Confidence (pLDDT)** | [average score] |
**Confidence Distribution**:
- Very High (>90): [X]% of residues
- High (70-90): [X]% of residues
- Low (50-70): [X]% of residues
- Very Low (<50): [X]% of residues
**Use Cases**:
- ✓ Overall fold reliable
- ✓ Core domain structure
- ⚠ Loop regions uncertain
- ✗ Not suitable for binding site analysis
---
## Structure Comparison
| Property | [PDB_1] | [PDB_2] | AlphaFold |
|----------|---------|---------|-----------|
| Resolution | 1.9 Å | 2.5 Å | N/A (predicted) |
| Completeness | 98% | 85% | 100% |
| Ligands | Yes | No | No |
| Confidence | Experimental | Experimental | High (85 avg) |
---
## Download Links
### Coordinate Files
| Format | PDB ID | Link |
|--------|--------|------|
| PDB | [4INS] | [link] |
| mmCIF | [4INS] | [link] |
| AlphaFold | [UniProt] | [link] |
### Database Links
- RCSB PDB: https://www.rcsb.org/structure/[pdb_id]
- PDBe: https://www.ebi.ac.uk/pdbe/entry/pdb/[pdb_id]
- AlphaFold: https://alphafold.ebi.ac.uk/entry/[uniprot_id]
Retrieved: [date]| Tier | Symbol | Criteria |
|---|---|---|
| Excellent | ●●●● | X-ray <1.5Å, complete, R-free <0.22 |
| High | ●●●○ | X-ray <2.0Å OR Cryo-EM <3.0Å |
| Good | ●●○○ | X-ray 2.0-3.0Å OR Cryo-EM 3.0-4.0Å |
| Moderate | ●○○○ | X-ray >3.0Å OR NMR ensemble |
| Low | ○○○○ | >4.0Å, incomplete, or problematic |
| Resolution | Use Case |
|---|---|
| <1.5 Å | Atomic detail, H-bond analysis |
| 1.5-2.0 Å | Drug design, mechanism studies |
| 2.0-2.5 Å | Structure-based design |
| 2.5-3.5 Å | Overall architecture, fold |
| >3.5 Å | Domain arrangement only |
| pLDDT Score | Interpretation |
|---|---|
| >90 | Very high confidence, experimental-like |
| 70-90 | Good backbone confidence |
| 50-70 | Uncertain, flexible regions |
| <50 | Low confidence, likely disordered |
Every structure report MUST include:
User: "Get structure for EGFR kinase with inhibitor" → Filter for ligand-bound structures, emphasize binding site
User: "Find best template for homology modeling of protein X" → High-resolution structures, note sequence coverage
User: "Compare available SARS-CoV-2 main protease structures" → All structures with systematic comparison table
User: "Structure of protein with UniProt P12345" → Check PDB first, then AlphaFold, note confidence
| Error | Response |
|---|---|
| "PDB ID not found" | Verify 4-character format, check if obsoleted |
| "No structures for protein" | Offer AlphaFold prediction, suggest similar proteins |
| "Download failed" | Retry once, provide alternative link |
| "Resolution unavailable" | Likely NMR/model, note in assessment |
RCSB PDB (Experimental Structures)
| Tool | Purpose |
|---|---|
search_structures_by_protein_name | Name-based search |
get_protein_metadata_by_pdb_id | Basic info |
get_protein_experimental_details_by_pdb_id | Method details |
get_protein_resolution_by_pdb_id | Quality metric |
get_protein_ligands_by_pdb_id | Bound molecules |
download_pdb_structure_file | Coordinate files |
get_similar_structures_by_pdb_id | Homologs |
PDBe (European PDB)
| Tool | Purpose |
|---|---|
pdbe_get_entry_summary | Overview |
pdbe_get_molecules | Molecular entities |
pdbe_get_experiment_info | Experimental data |
pdbe_get_binding_sites | Ligand pockets |
AlphaFold (Predictions)
| Tool | Purpose |
|---|---|
alphafold_get_structure_by_uniprot | Get prediction |
alphafold_search_structures | Search predictions |
© lamm-mit, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (scripts) in skills/protein-structure-retrieval of lamm-mit/scienceclaw.
Open the folder on GitHubat commit ab9aba1
Protein Structure Retrieval next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Protein Structure Retrieval this skilllamm-mit/scienceclaw | 244 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills | 3.2k | 2 repos | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Bio DB ToolsDrugClaw/DrugClaw | 125 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Ggetdavila7/claude-code-templates | 32k | 10 repos | ~6.3k | Automated safety check: Pass | MIT | |
| Alphafold Databasedavila7/claude-code-templates | 32k | 10 repos | ~4k | Automated safety check: Pass | MIT | |
| Foldseek Structural Searchgoogle-deepmind/science-skills | 3.2k | 1 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 |
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
DrugClaw/DrugClaw
Query public biology databases and APIs including UniProt, RCSB PDB, AlphaFold DB, ClinVar, dbSNP, gnomAD, Ensembl, GEO, InterPro, KEGG, OpenTargets, Reactome, and STRING.
davila7/claude-code-templates
CLI/Python toolkit for rapid bioinformatics queries. An agent skill from davila7/claude-code-templates.
davila7/claude-code-templates
Access AlphaFold's 200M+ AI-predicted protein structures. An agent skill from davila7/claude-code-templates.
google-deepmind/science-skills
Performs 3D structural searches of proteins against various databases (PDB, AlphaFold, CATH, MGnify, etc.) using the Foldseek API.
wu-yc/LabClaw
Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles.
lamm-mit/scienceclaw
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources.
lamm-mit/scienceclaw
Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections.
lamm-mit/scienceclaw
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index.
lamm-mit/scienceclaw
Cloud-based quantum chemistry platform with Python API. An agent skill from lamm-mit/scienceclaw.
lamm-mit/scienceclaw
Create professional infographics using Nano Banana Pro AI with smart iterative refinement.
lamm-mit/scienceclaw
Generate comprehensive disease research reports using 100+ ToolUniverse tools.
Categories
ToolUniverse workflow — Protein Structure Retrieval. An agent skill from lamm-mit/scienceclaw. Protein Structure Retrieval is an agent skill from lamm-mit/scienceclaw.
Protein Structure Retrieval fits situations like: tasks that involve Protein structure and design.
Run `npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a claude-code`. Or copy the skill folder (skills/protein-structure-retrieval in lamm-mit/scienceclaw) into .claude/skills/protein-structure-retrieval in your project. Claude Code loads it when a task matches its description.
Run `npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a codex`. Or copy the skill folder (skills/protein-structure-retrieval in lamm-mit/scienceclaw) into .agents/skills/protein-structure-retrieval in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lamm-mit/scienceclaw --skill protein-structure-retrieval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/protein-structure-retrieval, .gemini/skills/protein-structure-retrieval, .github/skills/protein-structure-retrieval and .opencode/skills/protein-structure-retrieval in your project.
Going by SKILL.md and its folder, Protein Structure Retrieval needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md names 3 domains. In commands or code: rcsb.org, ebi.ac.uk and alphafold.ebi.ac.uk; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Protein Structure Retrieval is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Protein Structure Retrieval: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Bio DB Tools (DrugClaw/DrugClaw, 125 stars), Gget (davila7/claude-code-templates, 32k stars) and Alphafold Database (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 86 skills in this directory. The repository was last updated on August 21, 2026.
Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.