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 agentmods add skills/azealoo/miniagent/differential_expression_helpernpx skills add Azealoo/miniAgent --skill differential_expression_helpergit clone --depth 1 https://github.com/Azealoo/miniAgentWrote 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/azealoo/miniagent/differential_expression_helper)<a href="https://agentmods.dev/skills/azealoo/miniagent/differential_expression_helper"><img src="https://agentmods.dev/badge/skills/azealoo/miniagent/differential_expression_helper.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00623 |
| Opus 5 | $0.00014 | $0.00311 |
| Sonnet 5 | $0.00005 | $0.00125 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
differential_expression_helper 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 60 lines · 27 tokens per session scan A 331c37a191de
differential_expression_helper is a skill published in the GitHub repository Azealoo/miniAgent (2 stars, last pushed 2mo ago), with no licence file. It adds 27 tokens to every session and 623 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
sc-markers
Load when ranking cluster-level marker genes from a clustered single-cell AnnData via Scanpy Wilcoxon / t-test / logreg or COSG specificity. Skip when comparing condition-vs-control with replicates (use sc-de); assigning cell-type labels (use sc-cell-annotation).
sc-cell-communication
Load when computing cell-cell ligand-receptor communication on an annotated scRNA AnnData via builtin scorer, LIANA, CellPhoneDB, CellChat (R), or NicheNet (R). Skip when assigning cell-type labels (use sc-cell-annotation); transcription factor → target regulatory networks (use sc-grn).
sc-enrichment
Load when running bulk-style pathway enrichment (ORA / GSEA / GSEA-R / GSVA-R) on a per-group ranked DE / marker list against a gene-set library. Skip when computing per-cell pathway scores in-place (use sc-pathway-scoring); de-novo gene-program discovery (use sc-gene-programs).
sc-batch-integration
Load when integrating multi-sample scRNA-seq with Harmony, scVI, scANVI, BBKNN, Scanorama, SIMBA, or supported R-backed methods to remove batch effects. Skip when the data is one sample (no batch effect to integrate); upstream merging only (use sc-multi-count).
sc-cell-annotation
Load when assigning cell-type labels to a clustered scRNA AnnData via marker dictionaries, CellTypist, PopV, KNNPredict, SingleR, scmap, SCSA, or a manual cluster-to-label map. Skip when ranking marker genes per cluster (use sc-markers); condition-vs-control DE (use sc-de).
sc-consensus-integration
Load when you want a multi-sample single-cell (scRNA) clustering robust to the choice of integration method — fanning out Harmony/Scanorama/scVI + an unintegrated baseline, scoring each by a batch-mixing intrinsic panel, and voting a consensus. Skip when single-batch (use sc-consensus-clustering); one integration…