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-gene-programsgit 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-gene-programs)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-gene-programs"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-gene-programs/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-gene-programs"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-gene-programs.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 | $0.00062 | $0.01618 |
| Opus 5 | $0.00031 | $0.00809 |
| Sonnet 5 | $0.00012 | $0.00324 |
| Haiku 4.5 | $0.00006 | $0.00162 |
Grade A, and why
sc-gene-programs 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-gene-programs
When to use
The user has a non-negative scRNA AnnData (raw counts or log-normalised expression) and wants to decompose it into K gene programs (latent factors) plus a per-cell usage matrix. Two methods:
cnmf(default) — consensus NMF (multiple runs + clustering of factors) for stable programs. Auto-falls back tonmfif thecnmfpackage isn't installed.nmf— sklearn NMF, single run.
Output: tables/program_usage.csv (cells × K), tables/program_weights.csv
(genes × K), tables/top_program_genes.csv (top-N genes per program).
For per-cluster marker discovery use sc-markers; for TF → target
regulons use sc-grn; for per-cell pathway scores against curated
gene sets use sc-pathway-scoring.
Inputs & Outputs
Inputs
- Modalities: scrna
- File types:
.h5ad - Requires a preprocessed AnnData (
Xnormalised, PCA/neighbours present)
Outputs
tables/cell_metadata.csvtables/gene_expression.csvtables/program_correlation.csvtables/program_tpm.csvtables/program_usage.csvtables/program_weights.csvtables/top_program_genes.csvfigures/mean_program_usage.pngfigures/program_correlation.pngfigures/r_feature_cor.pngfigures/r_feature_violin.pnganalysis_summary.txtprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad) — addsobsm:X_gene_programs
Flow
- Auto-fallback check: try
import cnmf; if it fails, silently switch--methodtonmf. - Load AnnData (
--input) or build a demo. - Preflight: pick source matrix per
--layer(auto-preferlayers["counts"]for cnmf when--layeris unset); reject negative values; warn ifn_genes < 50or running NMF on raw counts without--layer counts. - Run cNMF (consensus NMF with
--n-iterruns) or sklearn NMF (single run,--seed). - Build top-genes-per-program table; compute per-program correlation matrix.
- Detect degenerate output → record diagnostics; do NOT raise.
- Save tables, figures,
processed.h5ad,report.md,result.json.
What ships with it
7 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 · 120 lines · 62 tokens per session scan A e1055ed6c762
sc-gene-programs is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 62 tokens to every session and 1,618 once invoked, about $0.0003 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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