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 sc-grngit 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/sc-grn)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-grn"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-grn/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/sc-grn"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-grn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Rogue Agent · line 3 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00100 | $0.02017 |
| Opus 5 | $0.00050 | $0.01009 |
| Sonnet 5 | $0.00020 | $0.00403 |
| Haiku 4.5 | $0.00010 | $0.00202 |
Grade A, and why
sc-grn 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 5d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-grn
When to use
The user has a normalised scRNA AnnData (cluster labels in
obs[--cluster-key], default leiden) and wants gene regulatory
network inference: TFs → target genes plus per-cell regulon activity
scores. Two paths:
- Full SCENIC pipeline (when
--tf-list+--db+--motifare all provided): GRNBoost2 co-expression → cisTarget motif enrichment- pruning → AUCell scoring per cell. Produces motif-validated
regulons; AUCell activity is exposed as per-TF
obs["regulon_<TF>"]columns (one float column per regulon) plustables/grn_auc_matrix.csv.
- pruning → AUCell scoring per cell. Produces motif-validated
regulons; AUCell activity is exposed as per-TF
- Correlation fallback (when external resources are missing AND
--allow-simplified-grnis set, or in--demo): adjacency-only output, no motif validation, no AUCell. Useful for sanity checks but NOT a substitute for the full pipeline.
For ligand-receptor / cell-cell communication use
sc-cell-communication. For in-silico KO predictions use
sc-in-silico-perturbation (which builds a simpler GRN internally).
Inputs & Outputs
Inputs
- Modalities: scrna
- File types:
.h5ad,.txt - Requires a preprocessed AnnData (
Xnormalised, PCA/neighbours present)
Outputs
tables/auc_matrix.csvtables/cell_metadata.csvtables/gene_expression.csvtables/regulon_summary.csvtables/top_adjacencies.csvfigures/r_regulon_cor.pngfigures/r_regulon_violin.pnganalysis_summary.txtprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad) — addsobs:regulon_<TF>
Flow
- Load AnnData (
--input) or build a synthetic demo (GRNBoost2-only). - Preflight: when running the full pipeline, verify
--tf-list/--db/--motifexist; demo /--allow-simplified-grnskip the resource check. - Try GRNBoost2 (arboreto) for co-expression adjacencies; if
arboretois not installed OR returns empty, silently fall back to correlation-based adjacencies and recordresult.json["used_fallback"]=True+fallback_reason. - With full resources: run cisTarget motif enrichment + pruning → AUCell scoring per cell.
- Detect degenerate output (zero regulons / TFs) → write troubleshooting block; do NOT raise.
- Save tables, figures,
processed.h5ad,report.md,result.json.
What ships with it
6 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.
- 5d ago First seen · 135 lines · 100 tokens per session scan A 88a9b68bd070
sc-grn is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 100 tokens to every session and 2,017 once invoked, about $0.0005 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 2026-09-03.
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