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 spatial-raw-processinggit 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/spatial-raw-processing)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-raw-processing"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-raw-processing/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/spatial-raw-processing"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-raw-processing.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 | $0.00070 | $0.01850 |
| Opus 5.5 | $0.00028 | $0.00740 |
| Sonnet 5.5 | $0.00014 | $0.00370 |
| Haiku 4.5 | $0.00007 | $0.00185 |
Grade A, and why
spatial-raw-processing 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-raw-processing
Use from a step
This is CLI_ONLY: ST-Pipeline consumes FASTQ files, barcode coordinates and reference indexes. It is not an in-memory analysis function.
from skills._sdk.notebook import run_cli
output = run_cli("spatial-raw-processing", "--input", "data/run_bundle.json",
inputs=["data/run_bundle.json", "data/R1.fastq.gz",
"data/R2.fastq.gz", "data/barcodes.tsv"])
Record the reference index version in the module README. The executable example uses synthetic upstream outputs and does not run ST-Pipeline.
When to use
The user has paired-end spatial-transcriptomics FASTQ files (read1 =
spatial barcode + UMI, read2 = cDNA) plus a STAR genome index, and
wants the standard ST-Pipeline run that produces a raw_counts.h5ad
with one row per spatial spot. Single backend: st_pipeline (calls
run_stpipeline from skills/spatial/_lib/stpipeline_adapter.py).
After this skill, chain to spatial-preprocess for QC + normalisation.
For non-spatial scRNA FASTQ use sc-fastq-qc. For bulk RNA-seq read
QC use bulkrna-read-qc.
Inputs & Outputs
Inputs
- Input kinds:
file,directory - Modalities: visium, slideseq
- File types:
.fastq,.fq,.json,.yaml,.yml - FASTQ structure: valid first record;
pairedlayout - Directory layouts (any):
paired-fastq
Outputs
tables/gene_qc.csvtables/raw_gene_qc.csvtables/raw_processing_run_summary.csvtables/raw_processing_spatial_points.csvtables/raw_spot_qc.csvtables/raw_top_genes.csvtables/run_summary.csvtables/saturation_curve.csvtables/spatial_coordinates.csvtables/spot_qc.csvtables/stage_summary.csvtables/top_genes.csvfigures/raw_detected_genes_spatial.pngfigures/raw_spot_qc_histograms.pngfigures/raw_top_genes_barplot.pngfigures/raw_total_counts_spatial.pngfigures/st_pipeline_saturation_curve.pngfigures/st_pipeline_stage_attrition.pngomicsclaw_stpipeline_run.jsonraw_counts.h5adst_pipeline.stderr.txtst_pipeline.stdout.txtreport.mdresult.json- Processed AnnData (
saves_h5ad) — addsobs:barcode,x_array,y_array;obsm:spatial
What ships with it
8 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.
- examples/example_step.py 717 B runs code
- r_visualization/raw_processing_publication_template.R 3.5 KB
- r_visualization/README.md 1.4 KB
- references/methodology.md 7.8 KB
- references/output_contract.md 2.0 KB
- references/parameters.md 2.0 KB
- spatial_raw_processing.py 12 KB runs code
- tests/test_spatial_raw_processing.py 3.4 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 · +7 lines d488961b1d27
- 1mo ago First seen · 135 lines · 70 tokens per session scan A 09724602929f
spatial-raw-processing is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 14d ago), licensed Apache-2.0. It adds 70 tokens to every session and 1,850 once invoked, about $0.0003 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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