Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/TianGzlab/OmicsClawnpx agentmods add skills/tiangzlab/omicsclaw/gwas-to-function-twasWrote 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/gwas-to-function-twas)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/gwas-to-function-twas"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/gwas-to-function-twas/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/gwas-to-function-twas"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/gwas-to-function-twas.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- low Excessive Agency · line 320 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00007 | $0.04765 |
| Opus 5 | $0.00003 | $0.02382 |
| Sonnet 5 | $0.00001 | $0.00953 |
| Haiku 4.5 | $0.00001 | $0.00477 |
Grade A, and why
GWAS to Function via TWAS 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 9d 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 — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GWAS to Function via TWAS
Identify genes whose genetically regulated expression is associated with disease risk, determine therapeutic directionality (inhibit vs. activate), and prioritize drug targets with causal genetic evidence using Transcriptome-Wide Association Study (TWAS) analysis.
Key capabilities:
- Dual TWAS tools: FUSION (comprehensive) and S-PrediXcan (fast, 10-100x faster)
- Therapeutic directionality: Determine inhibit vs. activate strategy for each gene
- Multi-tier analysis: Basic → Colocalization → Mendelian Randomization → Druggability
- Colocalization testing: Filters LD artifacts (up to 50% of TWAS hits), requires PP.H4 > 0.8
- Cross-tissue meta-analysis: S-MultiXcan across 54 GTEx v8 tissues
- Biopharma-ready reports: Excel workbooks with prioritized targets and confidence levels
When to Use This Skill
Use this skill when:
- ✅ You have genome-wide GWAS summary statistics (N > 5,000 samples)
- ✅ Need to identify effector genes for non-coding GWAS variants
- ✅ Want to determine therapeutic strategy (inhibit vs. activate gene expression)
- ✅ Prioritizing drug targets by genetic evidence strength
- ✅ Validating existing targets with causal genetic evidence
Don't use this skill when:
- ❌ Only have significant loci (p < 5×10⁻⁸) without genome-wide data
- ❌ Sample size < 5,000 (insufficient power for TWAS)
- ❌ Need functional enrichment analysis → use functional-enrichment-from-degs
- ❌ Working with individual-level genotypes → run GWAS first with gwas-genotype-qc
Data requirements: Genome-wide GWAS summary statistics with columns: SNP, CHR, BP, A1, A2, BETA/OR, SE, P, N
Installation
Required Software
| Software | Version | License | Commercial Use | Installation |
|---|---|---|---|---|
| FUSION | Latest | MIT | ✅ Permitted | https://github.com/gusevlab/fusion_twas |
| MetaXcan/S-PrediXcan | Latest | MIT | ✅ Permitted | pip install metaxcan |
| Python | ≥3.9 | PSF | ✅ Permitted | conda/pip |
| R | ≥4.0 | GPL | ✅ Permitted | CRAN (required for FUSION only) |
What ships with it
22 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/fusion_best_practices.md 2.8 KB
- references/installation-guide.md 9.8 KB
- references/ldsc_qc_guidelines.md 6.8 KB
- references/spredixxcan_best_practices.md 3.9 KB
- references/therapeutic_interpretation_guide.md 26 KB
- references/tissue_reference_guide.md 5.5 KB
- references/troubleshooting_guide.md 6.5 KB
- references/twas_hub_validation_guide.md 2.4 KB
- scripts/colocalization_analysis.py 5.3 KB runs code
- scripts/druggability_scoring.py 5.3 KB runs code
- scripts/export_results.py 6.7 KB runs code
- scripts/integrate_variant_annotation.py 5.4 KB runs code
- scripts/interpret_therapeutic_direction.py 16 KB runs code
- scripts/mendelian_randomization.py 23 KB runs code
- scripts/multilayer_direction_analysis.py 18 KB runs code
- scripts/plot_twas_results.py 7.4 KB runs code
- scripts/run_fusion.py 5.0 KB runs code
- scripts/run_smultixcan.py 3.0 KB runs code
- scripts/run_spredixxcan.py 4.0 KB runs code
- scripts/select_reference_panel.py 7.7 KB runs code
- scripts/validate_gwas_sumstats.py 7.1 KB runs code
- scripts/validate_with_twas_hub.py 16 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.
- 9d ago First seen · 400 lines · 7 tokens per session scan A d84f55fe5be2
GWAS to Function via TWAS is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 7 tokens to every session and 4,765 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.
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