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 PKU-YuanGroup/OpenAI4S --skill bio-database-access-uniprot-accessgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-database-access-uniprot-access)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-uniprot-access"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-uniprot-access/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/pku-yuangroup/openai4s/bio-database-access-uniprot-access"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-uniprot-access.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00137 | $0.05119 |
| Opus 5 | $0.00068 | $0.02559 |
| Sonnet 5 | $0.00027 | $0.01024 |
| Haiku 4.5 | $0.00014 | $0.00512 |
Grade B, and why
bio-uniprot-access scanned grade B 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
submit = requests.post('https://rest.uniprot.org/idmapping/run', Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Python: `requests.get('https://rest.uniprot.org/uniprotkb/...')` (REST API) This is a copy
95% identical to bio-uniprot-access — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+; UniProt REST API as of 2024_06 release
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show requests pandas - API surface: confirm endpoint URLs match https://www.uniprot.org/help/api
The REST API JSON schema is stable within a release; major schema changes are documented at https://www.uniprot.org/release-notes. The 2022 migration broke the legacy https://www.uniprot.org/uniprot/... endpoints.
UniProt Access
"Get protein information from UniProt" -> Two facts dominate every UniProt workflow in 2026: (1) the API endpoint migrated in 2022 from https://www.uniprot.org/uniprot/... to https://rest.uniprot.org/uniprotkb/... with a substantially different JSON schema; pre-2022 code does not work as-is. (2) ?fields= is essential — default JSON returns the full entry (~20-30 KB each); for bulk pulls, request only the fields actually needed.
The major databases under the UniProt umbrella have different scopes:
-
UniProtKB: the curated knowledgebase — Swiss-Prot (manually reviewed, ~570K entries as of 2024) + TrEMBL (auto-annotated, ~250M). Always specify
reviewed:truefor high-quality reference work. -
UniRef: clustered sequences at 100%, 90%, 50% identity. UniRef50 is the standard for redundancy reduction.
-
UniParc: archival "every unique sequence ever seen" — for provenance and historical lookup.
-
Proteomes: organism-level groupings; reference proteomes (one per species) are the canonical subset.
-
Python:
requests.get('https://rest.uniprot.org/uniprotkb/...')(REST API) -
Python:
Bio.ExPASy.get_sprot_raw()(BioPython; legacy SwissProt format) -
CLI:
curl https://rest.uniprot.org/uniprotkb/P04637.json
Required Setup
import requests
import pandas as pd
import time
No API key required. Rate limit is generous (~200 req/sec tolerated empirically); ID-mapping has its own job queue.
What ships with it
3 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 · 449 lines · 137 tokens per session scan B f37bfdd5b000
bio-uniprot-access is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 137 tokens to every session and 5,119 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). It is 95% identical to bio-uniprot-access, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.
scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…
cellxgene-census
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
glycobiology
Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.