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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-alphafold-dbgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-alphafold-db)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-alphafold-db"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-alphafold-db/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/alterlab-ieu/alterlab-academic-skills/alterlab-alphafold-db"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-alphafold-db.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 | $0.00144 | $0.02492 |
| Opus 5 | $0.00072 | $0.01246 |
| Sonnet 5 | $0.00029 | $0.00498 |
| Haiku 4.5 | $0.00014 | $0.00249 |
Grade A, and why
alterlab-alphafold-db scanned grade A with 2 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Read WebFetch Bash(curl:*) Bash(python:*) Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
ID and uses list-form `subprocess.run` (never `shell=True`). See How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaFold Database
Overview
AlphaFold DB is a public repository of AI-predicted 3D protein structures for over 200 million proteins, maintained by DeepMind and EMBL-EBI. Access structure predictions with confidence metrics, download coordinate files, retrieve bulk datasets, and integrate predictions into computational workflows.
When to Use This Skill
This skill should be used when working with AI-predicted protein structures in scenarios such as:
- Retrieving protein structure predictions by UniProt ID or protein name
- Downloading PDB/mmCIF coordinate files for structural analysis
- Analyzing prediction confidence metrics (pLDDT, PAE) to assess reliability
- Accessing bulk proteome datasets via Google Cloud Platform
- Comparing predicted structures with experimental data
- Performing structure-based drug discovery or protein engineering
- Building structural models for proteins lacking experimental structures
- Integrating AlphaFold predictions into computational pipelines
Core Capabilities
Worked, copy-paste Python recipes for every capability below live in
references/code_examples.md. Load it when you need runnable code; the summaries
here give the routing and the key decisions.
1. Searching and Retrieving Predictions
Three entry points, in order of preference:
- Biopython (recommended):
Bio.PDB.alphafold_db.get_predictions(accession),download_cif_for(...),get_structural_models_for(...)— simplest path. - Direct REST:
GET https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}; the AlphaFold ID isresponse[0]['entryId']. - Find accessions first via UniProt when you only have a gene name or PDB ID —
use the UniProt ID-mapping job API (
get_uniprot_idshelper incode_examples.md§1; valid db names at https://rest.uniprot.org/configure/idmapping/fields).
2. Downloading Structure Files
The /prediction response carries version-stamped file URLs — use those, don't
hand-build a _v{N} suffix. The DB version advances (currently v6) and old
_v4 file URLs now 404:
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 234 lines · 144 tokens per session scan A 504dcc424b23
alterlab-alphafold-db is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 8d ago), licensed MIT. It adds 144 tokens to every session and 2,492 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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