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-markersgit 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-markers)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-markers"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-markers/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-markers"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-markers.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 | $0.00061 | $0.02182 |
| Opus 5.5 | $0.00024 | $0.00873 |
| Sonnet 5.5 | $0.00012 | $0.00436 |
| Haiku 4.5 | $0.00006 | $0.00218 |
Grade A, and why
sc-markers 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 14d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-markers
When to use
The user already has clustering / cell-type labels in obs (typically
leiden, louvain, or cell_type) and wants ranked marker genes per
group as evidence for downstream annotation or interpretation. Four
methods: wilcoxon (default rank-sum), t-test (Welch), logreg
(multinomial logistic regression — discriminative ranking), cosg
(fast cosine-specificity scoring without p-values). This is for
cluster markers, not condition contrasts — for treatment-vs-control
DE with replicates use sc-de.
Use from a step
markers = load_skill("sc-markers")
adata = read_input("results/02_cluster/intermediate/adata_clustered.h5ad")
table = markers.find_markers(adata, groupby="leiden")
write_output(table, "tables/markers_all.csv")
write_output(markers.top_markers(table), "tables/markers_top.csv")
write_output(markers.run_info(table), "tables/marker_run.json")
A complete step on log-normalized PBMC expression is in examples/example_step.py.
API
find_markers(adata, *, groupby: str, method: str='wilcoxon', n_genes: int | None=None, min_in_group_fraction: float=0.25, min_fold_change: float=0.25, max_out_group_fraction: float=0.5, mu: float=1.0) -> pd.DataFrame
Rank marker genes for each group against the rest using adata.X.
X should contain normalized expression. Computation uses a copy, so the input's matrices, obs and uns are unchanged. Scanpy methods apply the expression-fraction and fold-change filters; when filtering fails or removes every row, the unfiltered ranking is returned and run_info records filter_fallback and its reason. COSG scores specificity without p-values.
:param groupby: The obs column defining clusters or cell types. :param method: wilcoxon (default), t-test, logreg or cosg. :param n_genes: Genes ranked per group. None means all genes for Scanpy methods and 50 for COSG, matching the CLI defaults. :param min_in_group_fraction: Minimum expressing fraction within a group. Default 0.25; unused by COSG. :param min_fold_change: Fold-change filter passed to Scanpy. Default 0.25; unused by COSG. :param max_out_group_fraction: Maximum expressing fraction outside a group. Default 0.5; unused by COSG. :param mu: COSG specificity penalty. Default 1.0; unused by Scanpy methods. :returns: A DataFrame with group, names, scores and method-dependent effect, p-value and fraction columns. COSG pvals and pvals_adj are NaN. The table's attrs hold the run record; use run_info before CSV export. :raises ValueError: Unknown method or missing grouping column.
What ships with it
9 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.
- _api.py 7.3 KB runs code
- examples/example_step.py 904 B runs code
- references/methodology.md 1.4 KB
- references/output_contract.md 1.4 KB
- references/parameters.md 2.3 KB
- references/r_visualization.md 681 B
- sc_markers.py 17 KB runs code
- tests/test_markers_api.py 3.2 KB runs code
- tests/test_sc_markers.py 1.4 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.
- 14d ago Changed · +76 lines scan C → A b4e08fe60b26
- 1mo ago First seen · 111 lines · 61 tokens per session scan C 42658b2004b6
sc-markers is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 14d ago), licensed Apache-2.0. It adds 61 tokens to every session and 2,182 once invoked, about $0.0002 per session on Opus 5.5. 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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