Science Skills is a collection of add-ons that give AI agents structured instructions, scripts, and references for scientific research, including genomics, structural biology, cheminformatics, and literature search. Researchers use it to guide agents through specialized scientific tasks with information from databases and tools such as AlphaGenome, AFDB, and UniProt. The catalogue entries are individual skills from this collection.
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 agentmods add skills/google-deepmind/science-skills/jaspar_databasenpx skills add google-deepmind/science-skills --skill jaspar_databasegit clone --depth 1 https://github.com/google-deepmind/science-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/google-deepmind/science-skills/jaspar_database)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/jaspar_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/jaspar_database.svg" alt="Measured on agentmods" 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 | $0.00081 | $0.01189 |
| Opus 5 | $0.00041 | $0.00594 |
| Sonnet 5 | $0.00016 | $0.00238 |
| Haiku 4.5 | $0.00008 | $0.00119 |
Grade A, and why
jaspar-database 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 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.
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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
JASPAR Skill
JASPAR is the definitive open-access database for Transcription Factor (TF) binding profiles, stored as Position Frequency Matrices (PFMs).
Use this skill to map abstract sequence motifs or genomic regions to specific biological regulators (e.g., "what TFs bind here?" or "what is the motif for CTCF?").
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/jaspar_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://jaspar.elixir.no/ and https://jaspar.elixir.no/api/, then (2) create the file recording the notification text and timestamp.
Core Rules
CRITICAL: You MUST respect the JASPAR API Terms of Use by adhering to the following:
- Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
- Maximum API Window Size: The genomic window for a single API query MUST
NOT exceed 100,000 bp (100kb). The
jaspar_api.pyscript automatically chunks larger requests for you to bypass this limitation when querying larger regions. - Valid Matrix IDs:
get_tf_motif,get_tf_metadata, andget_tf_pwmrequire a stable JASPAR Matrix ID (e.g.,MA0488.2). If a user provides a gene symbol (e.g.,JUN), you must resolve it first usingresolve_tf_id. - Taxonomy Required: Resolving IDs requires a
tax_idto ensure targeted searches. Common IDs: Human=9606, Mouse=10090. - Notification: If this skill is used, ensure this is mentioned in the output.
Utility Scripts
Run all commands using the bundled Python script:
1. Resolve TF to Matrix ID
Maps a transcription factor name to a stable Matrix ID. Required step before fetching motifs if only a gene name is provided.
What ships with it
2 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.
- 5d ago First seen · 126 lines · 81 tokens per session scan A 5cbe866bfd4c
jaspar-database is a skill published in the GitHub repository google-deepmind/science-skills (2,835 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,189 once invoked, about $0.0004 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 2026-08-30.
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