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 yanjumlinnb-boop/scientific-agent-skills --skill bioservicesgit clone --depth 1 https://github.com/yanjumlinnb-boop/scientific-agent-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/yanjumlinnb-boop/scientific-agent-skills/bioservices)<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/bioservices"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/bioservices/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/yanjumlinnb-boop/scientific-agent-skills/bioservices"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/bioservices.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 | $0.00073 | $0.02869 |
| Opus 5 | $0.00036 | $0.01435 |
| Sonnet 5 | $0.00015 | $0.00574 |
| Haiku 4.5 | $0.00007 | $0.00287 |
Grade A, and why
bioservices 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 12d 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.
This is a copy
81% identical to bioservices — 36 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 — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BioServices
Overview
BioServices is a Python package providing programmatic access to approximately 40 bioinformatics web services and databases. Retrieve biological data, perform cross-database queries, map identifiers, analyze sequences, and integrate multiple biological resources in Python workflows. The package handles both REST and SOAP/WSDL protocols transparently.
Version note: Examples target bioservices 1.16.0 (PyPI, Mar 2026). Requires Python 3.9–3.12. UniProt REST changes in mid-2022 (bioservices ≥1.10) mainly affect tabular columns names — see upstream _legacy_names if parsing breaks. ChEMBL wrappers changed at 1.6.0 (2018 API); use get_similarity, get_substructure, get_molecule instead of pre-1.6 method names.
When to Use This Skill
This skill should be used when:
- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
- Analyzing metabolic pathways and gene functions via KEGG or Reactome
- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
- Running sequence similarity searches (BLAST, MUSCLE alignment)
- Querying gene ontology terms (QuickGO, GO annotations)
- Accessing protein-protein interaction data (PSICQUIC, IntactComplex)
- Mining genomic data (BioMart, ArrayExpress, ENA)
- Integrating data from multiple bioinformatics resources in a single workflow
Core Capabilities
1. Protein Analysis
Retrieve protein information, sequences, and functional annotations:
from bioservices import UniProt
u = UniProt(verbose=False)
# Search for protein by name
results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
# Retrieve FASTA sequence
sequence = u.retrieve("P43403", "fasta")
# Map identifiers between databases
kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
Key methods:
search(): Query UniProt with flexible search termsretrieve(): Get protein entries in various formats (FASTA, XML, tab)mapping(): Convert identifiers between databases
What ships with it
7 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.
- references/identifier_mapping.md 17 KB
- references/services_reference.md 13 KB
- references/workflow_patterns.md 20 KB
- scripts/batch_id_converter.py 11 KB runs code
- scripts/compound_cross_reference.py 11 KB runs code
- scripts/pathway_analysis.py 9.3 KB runs code
- scripts/protein_analysis_workflow.py 13 KB runs code
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.
- 12d ago First seen · 386 lines · 73 tokens per session scan A 1f18effc3958
bioservices is a skill published in the GitHub repository yanjumlinnb-boop/scientific-agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 73 tokens to every session and 2,869 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 81% identical to bioservices, differing in 36 lines, and is treated as a copy.
Other skills, from other repositories
jupyter-notebook
Iterative Python via live Jupyter kernel (hamelnb).
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
bioservices
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use…
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…