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 LeonChaoX/qinyan-academic-skills --skill interpro-databasegit clone --depth 1 https://github.com/LeonChaoX/qinyan-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/leonchaox/qinyan-academic-skills/interpro-database)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/interpro-database"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/interpro-database/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/leonchaox/qinyan-academic-skills/interpro-database"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/interpro-database.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 | $0.00062 | $0.02742 |
| Opus 5 | $0.00031 | $0.01371 |
| Sonnet 5 | $0.00012 | $0.00548 |
| Haiku 4.5 | $0.00006 | $0.00274 |
Grade A, and why
interpro-database scanned grade A with 1 finding 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 5d 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.
response = requests.get(url, params=params, headers=headers) Copies of this mod
1 near-identical copy found in the catalogue:
- interpro-database — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
InterPro Database
Overview
InterPro (https://www.ebi.ac.uk/interpro/) is a comprehensive resource for protein family and domain classification maintained by EMBL-EBI. It integrates signatures from 13 member databases including Pfam, PANTHER, PRINTS, ProSite, SMART, TIGRFAM, SUPERFAMILY, CDD, and others, providing a unified view of protein functional annotations for over 100 million protein sequences.
InterPro classifies proteins into:
- Families: Groups of proteins sharing common ancestry and function
- Domains: Independently folding structural/functional units
- Homologous superfamilies: Structurally similar protein regions
- Repeats: Short tandem sequences
- Sites: Functional sites (active, binding, PTM)
Key resources:
- InterPro website: https://www.ebi.ac.uk/interpro/
- REST API: https://www.ebi.ac.uk/interpro/api/
- API documentation: https://github.com/ProteinsWebTeam/interpro7-api/blob/master/docs/
- Python client: via
requests
When to Use This Skill
Use InterPro when:
- Protein function prediction: What function(s) does an uncharacterized protein likely have?
- Domain architecture: What domains make up a protein, and in what order?
- Protein family classification: Which family/superfamily does a protein belong to?
- GO term annotation: Map protein sequences to Gene Ontology terms via InterPro
- Evolutionary analysis: Are two proteins in the same homologous superfamily?
- Structure prediction context: What domains should a new protein structure be compared against?
- Pipeline annotation: Batch-annotate proteomes or novel sequences
Core Capabilities
1. InterPro REST API
Base URL: https://www.ebi.ac.uk/interpro/api/
import requests
BASE_URL = "https://www.ebi.ac.uk/interpro/api"
def interpro_get(endpoint, params=None):
url = f"{BASE_URL}/{endpoint}"
headers = {"Accept": "application/json"}
response = requests.get(url, params=params, headers=headers)
response.raise_for_status()
return response.json()
What ships with it
1 file 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.
- 5d ago First seen · 306 lines · 62 tokens per session scan A d2e59e100ed0
interpro-database is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (876 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 2,742 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
academic-research
Nested swiss-knife reference for academic literature work — find papers, fetch full-text PDFs, trace citations, write LaTeX manuscripts. First action for any "get me this paper" request: python3 /scripts/fetchpaper.py — walks arXiv → Unpaywall → Europe PMC → CORE → in-house publisher-page extraction…
clean-data
Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher…
model-scaffold
Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, image-to-image synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone (transfer learning) — the missing middle link between choosing an architecture and validating a…
model-sourcing
Vet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family. Records a model dossier (source and version pin, licence and the file it was read from, intended use, pretrained-weight provenance, model task vs study task, reported validation…
preprocess-imaging
Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling and intensity normalisation, and the augmentation plan — so the pipeline is leakage-safe before model-scaffold builds the training repo. Emits a declarative preprocessing manifest and a deterministic data-stage…
radiomics-ml
Produce or audit a radiomics / tabular clinical-ML study — imaging or clinical features → any classical learner (penalised logistic [LASSO / ridge / elastic-net], SVM, k-NN, naive Bayes, LDA/QDA, decision tree, random forest, gradient boosting [XGBoost / LightGBM / CatBoost], shallow MLP, stacked ensembles) → a…