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 GPTomics/bioSkills --skill effector-gene-prioritizationgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/effector-gene-prioritization)<a href="https://agentmods.dev/skills/gptomics/bioskills/effector-gene-prioritization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/effector-gene-prioritization/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/gptomics/bioskills/effector-gene-prioritization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/effector-gene-prioritization.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00228 | $0.10866 |
| Opus 5 | $0.00114 | $0.05433 |
| Sonnet 5 | $0.00046 | $0.02173 |
| Haiku 4.5 | $0.00023 | $0.01087 |
Grade B, and why
bio-causal-genomics-effector-gene-prioritization scanned grade B with 2 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post('https://api.platform.opentargets.org/api/v4/graphql', Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post('https://api.platform.opentargets.org/api/v4/graphql', Copies of this mod
1 near-identical copy found in the catalogue:
- bio-causal-genomics-effector-gene-prioritization — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: MAGMA 1.10+ (cncr.nl/research/magma), FUMA web platform v1.6+ (fuma.ctglab.nl), Open Targets Genetics API (REST + GraphQL, June 2024 release), PoPS (head of FinucaneLab/pops, 2024), cS2G pre-computed scores (Zenodo record 7754032, Gazal 2022), ABC-Enhancer-Gene-Prediction 0.2.2+, ENCODE-rE2G v1.0+ (Gschwind 2023 preprint), DEPICT v1 rel194, INQUISIT (Fachal 2020 supplementary), Python 3.9-3.11, R 4.3+, PLINK 1.9 + PLINK 2.0.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
magma --helpto confirm gene-window, gene-annot, and gene-set flag names - Python:
pip show gentropy; introspect endpoints atapi.platform.opentargets.org/api/v4/graphql - R:
packageVersion('coloc')etc. for upstream evidence integration
If a script throws an error about an argument that has moved (e.g. an Open Targets endpoint renamed during a release) or a model file schema change, introspect the installed tool and adapt rather than retrying. Open Targets Genetics deprecated the standalone Genetics Portal in 2024 in favour of the integrated platform; verify endpoint URLs at the time of use.
Effector Gene Prioritization
"Which gene at this GWAS locus is actually the causal mediator?" -> Integrate fine-mapping, colocalization, chromatin-based enhancer-gene predictions, distance, and gene-similarity priors into a per-locus per-gene confidence score, then require concordance across multiple orthogonal evidence streams before nominating a causal effector. Effector gene prioritization is the bridge between statistical fine-mapping (variant level) and biological hypothesis (gene level); it is the most failure-prone step in GWAS-to-target pipelines because the nearest-gene assumption is wrong roughly 30-50% of the time at well-studied loci.
- CLI (gene-level association):
magma --bfile ref --gene-loc geneloc.txt --pval gwas.tsv ncol=N --out out->magma --gene-results out.genes.raw --set-annot annot.txt --out out - Web (integrative): FUMA SNP2GENE at fuma.ctglab.nl (positional + eQTL + Hi-C + chromatin in one workflow)
- API (pre-computed L2G): Open Targets Genetics GraphQL
studyLocus2GeneTablequery (note: Open Targets Genetics was consolidated into the Open Targets Platform in 2024; verify the live endpoint atapi.platform.opentargets.org/api/v4/graphql) - Python (similarity prior):
python pops.py --gene_annot_path gene_annot.txt --feature_mat_prefix features --control_features_path control.features --magma_prefix magma_out --out_prefix out - Lookup (combined SNP-to-gene): cS2G pre-computed gene scores at zenodo.org/records/7754032
- CLI (enhancer-gene): ABC pipeline or ENCODE-rE2G (cross-reference atac-seq/enhancer-gene-linking)
What ships with it
3 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.
- 9d ago First seen · 422 lines · 228 tokens per session scan B a93a77106d92
bio-causal-genomics-effector-gene-prioritization is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 24d ago), licensed MIT. It adds 228 tokens to every session and 10,866 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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