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 TianGzlab/OmicsClaw --skill literature-preclinicalgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/literature-preclinical)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/literature-preclinical"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/literature-preclinical/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/tiangzlab/omicsclaw/literature-preclinical"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/literature-preclinical.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00004 | $0.02988 |
| Opus 5 | $0.00002 | $0.01494 |
| Sonnet 5 | $0.00001 | $0.00598 |
| Haiku 4.5 | $0.00000 | $0.00299 |
Grade A, and why
Preclinical Literature Extraction 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 11d 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preclinical Literature Extraction
Search Consensus (consensus.app) for preclinical studies on a molecular target in a disease, then extract structured in vitro and in vivo experiment details from each paper.
When to Use This Skill
Use this skill when you need to:
- Survey preclinical evidence for a drug target in a disease indication
- Extract in vitro experiments — cell lines, assays (viability, migration, apoptosis, etc.), key findings
- Extract in vivo experiments — animal models (xenograft, PDX, syngeneic, transgenic), endpoints, key findings
- Identify common model systems — which cell lines and animal models are most used for your target
- Compare in vitro vs in vivo concordance — papers reporting both experiment types
- Support IND-enabling decisions — compile preclinical evidence landscape
Do NOT use this skill for:
- ❌ Clinical trial literature (use
literature-reviewinstead) - ❌ Automated full-text parsing (agent reads full text for top papers after abstract extraction)
- ❌ Meta-analysis or statistical pooling of preclinical results
- ❌ Citation management / formatting only
Installation
Python (Search + Extraction)
pip install requests pandas
PDF Report Generation (Optional)
pip install reportlab
R (Visualization)
install.packages(c("ggplot2", "ggprism", "dplyr", "tidyr", "patchwork"))
# Optional for high-quality SVG:
install.packages("svglite")
Package Licenses
| Software | Version | License | Commercial Use | Installation |
|---|---|---|---|---|
| requests | ≥2.25 | Apache 2.0 | ✅ Permitted | pip install requests |
| pandas | ≥1.3 | BSD | ✅ Permitted | pip install pandas |
| reportlab | ≥3.6 | BSD | ✅ Permitted | pip install reportlab |
| ggplot2 | ≥3.4 | MIT | ✅ Permitted | install.packages("ggplot2") |
| ggprism | ≥1.0.3 | GPL (≥3) | ✅ Permitted | install.packages("ggprism") |
| dplyr | ≥1.1 | MIT | ✅ Permitted | install.packages("dplyr") |
| tidyr | ≥1.3 | MIT | ✅ Permitted | install.packages("tidyr") |
| patchwork | ≥1.1 | MIT | ✅ Permitted | install.packages("patchwork") |
What ships with it
8 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/preclinical_synthesis_report.md 4.9 KB
- scripts/extract_experiments.py 13 KB runs code
- scripts/generate_plots.R 9.4 KB
- scripts/generate_report.py 22 KB runs code
- scripts/narrative_synthesis.py 24 KB runs code
- scripts/preclinical_search.py 9.7 KB runs code
- scripts/preclinical_synthesis.py 8.7 KB runs code
- scripts/report_generation.py 12 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.
- 11d ago First seen · 279 lines · 4 tokens per session scan A 6b8b5c5a1f6c
Preclinical Literature Extraction is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 4 tokens to every session and 2,988 once invoked, about $0.0000 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
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
single-cell-scrna-seq-analysis-scanpy
Complete single-cell RNA-seq analysis workflow built on Scanpy and AnnData. Use this skill when: (1) Loading diverse single-cell data formats (10X, h5ad, CSV), (2) Performing quality control and filtering, (3) Normalization, dimensionality reduction, and clustering, (4) Marker gene identification and cell type…
single-cell-multi-omics-analysis-scvi
Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics…
single-cell
Single-cell analysis pipeline covering scRNA-seq, snRNA-seq, and CyTOF (mass cytometry) — QC, normalization, integration, clustering, annotation, differential expression, trajectory, cell communication, and TF activity inference.
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.