Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting 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 skills add K-Dense-AI/scientific-agent-skills --skill gtarsgit clone --depth 1 https://github.com/K-Dense-AI/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/k-dense-ai/scientific-agent-skills/gtars)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/gtars"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/gtars/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/k-dense-ai/scientific-agent-skills/gtars"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/gtars.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 6 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00046 | $0.03309 |
| Opus 5 | $0.00023 | $0.01655 |
| Sonnet 5 | $0.00009 | $0.00662 |
| Haiku 4.5 | $0.00005 | $0.00331 |
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 8d 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gtars
Gtars provides native Rust implementations, Python bindings, and a feature-gated
gtars binary for genomic interval and reference-sequence work. Start with the
bundled local inspectors; call upstream code only after the data contract,
provenance, resource bounds, and side effects are explicit.
Verified snapshot (2026-07-23)
- Python:
gtars==0.9.2, released 2026-06-17,Requires-Python >=3.10. - Rust meta-crate:
gtars=0.9.0, released 2026-06-15. Its default feature set is empty. - CLI crate/binary:
gtars-cli=0.9.0; the installed binary is namedgtars. - Direct refget crate:
gtars-refget=0.9.1, released 2026-06-17.gtars=0.9.0itself pins its component release set, which includes refget 0.9.0. - Upstream intentionally versions workspace crates, Python bindings, and CLI independently. Do not assume matching numbers mean matching artifacts.
- The published docs changelog stops at 0.5.1. API examples here were checked
against the 0.9.2 Python stubs/runtime and the
v0.9.0CLI/Rust source.
The license: MIT field covers this skill. Published gtars crates declare MIT,
while the GitHub repository currently displays BSD-2-Clause at the root; verify
the exact artifact's license before redistribution.
Native-code trust gate and exact pins
The Python wheel contains a PyO3 native extension. Cargo installation compiles a native binary and can run dependency build scripts. Treat either path as code execution:
- Confirm the official PyPI/crates.io/GitHub owner and immutable version.
- Review filenames, platform tags, release provenance, license, and SHA-256.
GitHub's v0.9.0 binary release includes per-archive
.sha256sidecars. - Never run an untrusted prebuilt binary, wheel, source tree, Cargo build script, or archive installer. Use isolation and CPU/RAM/disk/time limits.
- Keep a lockfile and artifact hashes with the analysis manifest.
What ships with it
14 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/cli.md 9.7 KB
- references/coverage.md 7.5 KB
- references/overlap.md 7.3 KB
- references/python-api.md 9.4 KB
- references/refget.md 10 KB
- references/tokenizers.md 8.0 KB
- scripts/__init__.py 57 B runs code
- scripts/_common.py 15 KB runs code
- scripts/artifact_inspector.py 11 KB runs code
- scripts/bed_validator.py 5.3 KB runs code
- scripts/coverage_preflight.py 8.1 KB runs code
- scripts/execution_plan.py 12 KB runs code
- scripts/refget_digest_plan.py 11 KB runs code
- scripts/tokenizer_manifest.py 8.4 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.
- 8d ago Changed · +17 lines 1afe0002e97d
- 12d ago First seen · 283 lines · 46 tokens per session scan A fc98bc1cf988
gtars is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 46 tokens to every session and 3,309 once invoked, about $0.0002 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.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.