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 bulkrna-deconvolutiongit 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/bulkrna-deconvolution)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/bulkrna-deconvolution"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/bulkrna-deconvolution/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/bulkrna-deconvolution"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/bulkrna-deconvolution.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 | $0.00054 | $0.00714 |
| Opus 5.5 | $0.00022 | $0.00286 |
| Sonnet 5.5 | $0.00011 | $0.00143 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
bulkrna-deconvolution 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 15d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bulkrna-deconvolution
When to use
Estimate bulk cell-type proportions with an explicit signature matrix. See the description for adjacent skills.
Use from a step
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("bulkrna-deconvolution")
# Supply DataFrames read with read_input(..., reader=...) for your CSV layout.
result = library.deconvolve(counts, signature=signature)
write_output(result, "tables/result.csv")
The synthetic worked step is in examples/example_step.py.
API
deconvolve(counts, *, signature)
Estimate sample proportions without changing either input.
:param counts: Nonnegative gene-by-sample expression DataFrame. :param signature: Required gene-by-cell-type reference, matching the CLI --reference. :returns: Sample-by-cell-type DataFrame; diagnostics contain residuals and shared genes. :raises ValueError: Matrices have invalid values, duplicate labels or no shared genes.
run_info(result, *, keep=True)
Read reference overlap and reconstruction diagnostics.
:param result: DataFrame returned by deconvolve. :param keep: Default True; False removes diagnostics from attrs. :returns: Diagnostic dictionary, empty after removal.
proportions_figure(result)
Plot estimated cell-type proportions per sample.
:param result: Sample-by-cell-type DataFrame from deconvolve. :returns: A matplotlib Figure, without writing files. :raises ValueError: The table cannot be plotted as numeric proportions.
Methods and parameters
See parameters and methodology.
Gotchas
deconvolverequires shared gene identifiers and nonnegative matrices.run_info()['residuals']measures fit to the supplied reference, not confidence intervals.- NNLS is the only backend;
deconvolvedoes not call CIBERSORTx or MuSiC.
Inputs and outputs
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 1.7 KB runs code
- bulkrna_deconvolution.py 11 KB runs code
- examples/example_step.py 498 B runs code
- references/methodology.md 2.5 KB
- references/output_contract.md 1.3 KB
- references/parameters.md 222 B
- tests/__init__.py 0 B runs code
- tests/test_api.py 1.1 KB runs code
- tests/test_bulkrna_deconvolution.py 1.8 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.
- 15d ago Changed · +24 lines 84caac25b4fb
- 1mo ago First seen · 80 lines · 54 tokens per session scan A c3062fce2ed0
bulkrna-deconvolution is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 15d ago), licensed Apache-2.0. It adds 54 tokens to every session and 714 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 2026-08-30.
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