synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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/synthetic-sciences/openscience/gtarsnpx skills add synthetic-sciences/openscience --skill gtarsgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/gtars)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/gtars"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/gtars.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 | $0.00050 | $0.01772 |
| Opus 5 | $0.00025 | $0.00886 |
| Sonnet 5 | $0.00010 | $0.00354 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
gtars 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 yesterday.
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.
Copies of this mod
8 near-identical copies found in the catalogue:
- gtars — 100% identical, 5 lines differ
- gtars — 100% identical, 5 lines differ
- gtars — 100% identical, 5 lines differ
- gtars — 100% identical, 5 lines differ
- gtars — 98% identical, 6 lines differ
- gtars — 98% identical, 6 lines differ
- gtars — 98% identical, 6 lines differ
- gtars — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gtars: Genomic Tools and Algorithms in Rust
Overview
Gtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management.
Use this skill when working with:
- Genomic interval files (BED format)
- Overlap detection between genomic regions
- Coverage track generation (WIG, BigWig)
- Genomic ML preprocessing and tokenization
- Fragment analysis in single-cell genomics
- Reference sequence retrieval and validation
Installation
Python Installation
Install gtars Python bindings:
uv uv pip install gtars
CLI Installation
Install command-line tools (requires Rust/Cargo):
# Install with all features
cargo install gtars-cli --features "uniwig overlaprs igd bbcache scoring fragsplit"
# Or install specific features only
cargo install gtars-cli --features "uniwig overlaprs"
Rust Library
Add to Cargo.toml for Rust projects:
[dependencies]
gtars = { version = "0.1", features = ["tokenizers", "overlaprs"] }
Core Capabilities
Gtars is organized into specialized modules, each focused on specific genomic analysis tasks:
1. Overlap Detection and IGD Indexing
Efficiently detect overlaps between genomic intervals using the Integrated Genome Database (IGD) data structure.
When to use:
- Finding overlapping regulatory elements
- Variant annotation
- Comparing ChIP-seq peaks
- Identifying shared genomic features
Quick example:
import gtars
# Build IGD index and query overlaps
igd = gtars.igd.build_index("regions.bed")
overlaps = igd.query("chr1", 1000, 2000)
See references/overlap.md for comprehensive overlap detection documentation.
2. Coverage Track Generation
Generate coverage tracks from sequencing data with the uniwig module.
When to use:
- ATAC-seq accessibility profiles
- ChIP-seq coverage visualization
- RNA-seq read coverage
- Differential coverage analysis
What ships with it
6 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.
- yesterday First seen · 285 lines · 50 tokens per session scan A b432211c0f26
gtars is a skill published in the GitHub repository synthetic-sciences/openscience (3,432 stars, last pushed yesterday), licensed Apache-2.0. It adds 50 tokens to every session and 1,772 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons. Invoke for any question about IDC collections, cancer imaging datasets, DICOM data access, radiology (CT, MR, PET) or pathology AI training sets, metadata queries, visualization, or license checks — even when the user doesn't explicitly…
lab-hardware-cad
Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…
analytical-method-validation
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP / / , ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays…
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
diffdock
DiffDock and DiffDock-L molecular docking. Use for protein-small-molecule pose prediction from PDB or sequence plus SMILES/SDF/MOL2, batch docking, virtual screening, and pose-confidence interpretation. Not for binding affinity prediction.
anndata
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.