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 tiledbvcfgit 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/tiledbvcf)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/tiledbvcf"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/tiledbvcf/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/tiledbvcf"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/tiledbvcf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 3 findings, 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 MCP Rug Pull · line 53 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 54 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- low Privilege Escalation · line 236 Skill requests more permissions than appear necessary for its stated functionality. Review if elevated access is justified.Fix: Request only the minimum permissions required. Document why each permission is needed. Remove broad permissions like '*' or 'all'.
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 | $0.00062 | $0.03554 |
| Opus 5.5 | $0.00025 | $0.01422 |
| Sonnet 5.5 | $0.00012 | $0.00711 |
| Haiku 4.5 | $0.00006 | $0.00355 |
Grade A, and why
tiledbvcf 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 24d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- tiledbvcf — 100% identical, 589 lines differ
- tiledbvcf — 100% identical, 591 lines differ
- tiledbvcf — 100% identical, 589 lines differ
- tiledbvcf — 100% identical, 591 lines differ
- tiledbvcf — 98% identical, 596 lines differ
- tiledbvcf — 98% identical, 590 lines differ
- tiledbvcf — 98% identical, 590 lines differ
- tiledbvcf — 94% identical, 594 lines differ
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TileDB-VCF
When to use
Use for cohort variant storage, incremental sample ingestion, interval queries, and exporting subsets for downstream genomics. TileDB-VCF stores records; it does not perform joint variant calling, association testing, normalization, or population structure adjustment. Dataset size alone does not determine whether cloud execution is appropriate: benchmark the intended sample, region, and attribute workload.
This skill targets released TileDB-VCF 0.40.3. Native Python/CLI checks used two synthetic single-sample VCFs on macOS ARM64. Cloud client 0.14.4 contracts were checked against its released wheel and mocked dispatch; hosted queries and jobs were not executed. See installation and verification notes.
Install and verify the native stack
The tiledbvcf Python distribution is not published on PyPI at this review. The
official tiledb Conda channel supplies tiledbvcf-py 0.40.3 for macOS ARM64,
macOS x86-64, and Linux x86-64, with Python 3.9-3.12 builds. tiledb on PyPI is
TileDB-Py and does not install TileDB-VCF. Native Apple Silicon no longer requires
forcing CONDA_SUBDIR=osx-64.
For the tested macOS ARM64 environment, pins below avoid versioned-library import failures in the otherwise successful current Conda solve:
conda create -n tiledb-vcf -c tiledb -c conda-forge \
python=3.12 tiledbvcf-py=0.40.3 \
azure-core-cpp=1.16.2 azure-storage-blobs-cpp=12.16.0 \
azure-storage-files-datalake-cpp=12.14.0 capnproto=1.4.0 c-blosc2=2.23.1
conda activate tiledb-vcf
python -c 'import tiledbvcf; print(tiledbvcf.version)'
tiledbvcf version
The equivalent Micromamba solve/install was executed. These extra ABI pins are a verified macOS ARM64 workaround, not a claim about all platforms. Preserve the resolved environment for production. Other-platform installation and official Docker images are alternatives documented upstream, not tested here.
Prepare and create a cohort
- Record reference assembly, contig naming/lengths, callers, normalization rules,
sample identity, and file checksums. Do not mix assemblies or assume
1andchr1are equivalent. Confirm compatible headers across inputs. - Each input must contain one sample, be coordinate sorted, and have an index.
Use BGZF-compressed VCF or BCF with a matching
.csi/.tbi. Inspectbcftools query -l sample1.vcf.gz; filenames are not sample identifiers. - Create the dataset explicitly, then ingest. Opening
mode="w"alone does not create its schema. Materialize frequently read fields at creation time. - Validate a small known interval and a round-trip export before scaling.
What ships with it
1 file 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.
- 24d ago Changed · -173 lines · +16 tokens per session 38e55a6962e1
- 25d ago Changed 594578a931bd
- 1mo ago First seen · 456 lines · 46 tokens per session scan A 7f051f513865
tiledbvcf is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,587 stars, last pushed 7d ago), licensed MIT. It adds 62 tokens to every session and 3,554 once invoked, about $0.0002 per session on Opus 5.5. 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
bio-fasta-database-curator
Curate and validate FASTA or FAA databases. Use when standardizing headers, merging or deduplicating references, converting GenBank, or preparing BLAST, MMseqs2, or HMM inputs.
bio-foundation-housekeeping
Add LinkML/Pydantic metadata validation, Parquet tables, and a DuckDB catalog to a bioinformatics project. Use when records need schema checks or a queryable catalog.
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…
alterlab-tiledbvcf
Store and query genomic variant data at scale with TileDB-VCF — ingest VCF/BCF into compressed TileDB arrays, add samples incrementally, run fast parallel region/sample queries, and export back to VCF. Use when managing population-genomics variant datasets that are too large for flat VCF, building joint variant…
laravel-eloquent
Use when working with database models — Eloquent ORM, PHP attributes, relationships, queries, observers, or factories in Laravel 13.