Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add beita6969/ScienceClaw --skill protein-structuregit clone --depth 1 https://github.com/beita6969/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beita6969/scienceclaw/protein-structure)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/protein-structure"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/protein-structure/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/protein-structure"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/protein-structure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.00740 |
| Opus 5 | $0.00026 | $0.00370 |
| Sonnet 5 | $0.00011 | $0.00148 |
| Haiku 4.5 | $0.00005 | $0.00074 |
Grade A, and why
protein-structure scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions:
- Protein folding, AlphaFold, ESMFold, RoseTTAFold
- PDB files, structural analysis, Ramachandran plots
- Molecular docking, binding affinity, binding pockets
- Homology modeling, threading, ab initio structure prediction
- Protein-protein interactions (PPI), interface analysis
- Structural alignment, RMSD, TM-score
- Cryo-EM, X-ray crystallography data interpretation
Step-by-Step Methodology
- Retrieve or predict structure - Search PDB for experimental structures (by UniProt ID or gene name). If unavailable, use AlphaFold DB or run ESMFold. Check pLDDT confidence scores for predicted structures.
- Quality assessment - For experimental structures: check resolution, R-free, and completeness. For predictions: evaluate pLDDT per-residue and PAE (predicted aligned error) matrices.
- Structural analysis - Identify secondary structure elements (helices, sheets, loops). Compute solvent-accessible surface area. Map conserved residues and functional domains.
- Binding site identification - Use fpocket, SiteMap, or DoGSiteScorer for pocket detection. Cross-reference with known ligand binding from PDBe or BindingDB.
- Molecular docking - Recommend AutoDock Vina, GNINA, or Glide. Define grid box around binding site. Report binding energy (kcal/mol) and key interactions (H-bonds, hydrophobic, pi-stacking).
- Structural comparison - Align structures using TM-align or FATCAT. Report RMSD and TM-score. Identify conformational changes between states.
- Visualization guidance - Recommend PyMOL, ChimeraX, or Mol* for rendering. Specify coloring schemes (by chain, B-factor, electrostatics, or conservation).
Key Databases and Tools
- PDB / PDBe - Experimental protein structures
- AlphaFold DB - AI-predicted structures for UniProt entries
- UniProt - Protein sequences, domains, and annotations
- InterPro / Pfam - Domain classification
- BindingDB / ChEMBL - Binding affinity data
- RCSB PDB REST API - Programmatic structure queries
- PDBe-KB - Aggregated structural annotations
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 54 lines · 53 tokens per session scan A 38806ec61d2e
protein-structure is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 740 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
biomcp
Search and retrieve biomedical data - genes, variants, clinical trials, diagnostic tests, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. Use for gene function, variant pathogenicity, trials, diagnostics, drug safety, pathway context, disease workups…
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.