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/tiangzlab/omicsclaw/sc-consensus-integrationnpx skills add TianGzlab/OmicsClaw --skill sc-consensus-integrationgit 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-consensus-integration)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-consensus-integration"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-consensus-integration.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 5.1 | $0.00079 | $0.01531 |
| Opus 5 | $0.00039 | $0.00766 |
| Sonnet 5 | $0.00016 | $0.00306 |
| Haiku 4.5 | $0.00008 | $0.00153 |
Grade A, and why
sc-consensus-integration 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 2d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-consensus-integration
When to use
Verified consensus over batch-correction representations. For multi-sample
single-cell data the dominant axis of variation is not clustering resolution but
how batch effect is removed: clustering uncorrected PCA of multi-sample data
clusters batches, not cell types, and different integration methods
(Harmony / Scanorama / scVI / …) yield different embeddings and so different
clusterings. Use this when you have a preprocessed multi-sample AnnData with a
batch key in obs (≥2 batches) and want a clustering that is not an artifact
of one integration method, with per-cell confidence and batch-artifact flags.
It mirrors consensus-domains: members fan out sc-integrate-cluster --method <m>
— each a self-contained integrate + cluster unit — at a fixed resolution
(so member cluster counts stay comparable for the operator), scored by the
integration intrinsic panel (ADR 0029) before voting a consensus.
Inputs & Outputs
Inputs
- Modalities: scrna
- File types:
.h5ad - Requires a preprocessed AnnData (
Xnormalised, PCA/neighbours present) - Expects
obsm:X_pca
Outputs
consensus_labels.tsvmember_scores.csvmember_intrinsic_panel.csvcross_method_nmi.csvplan.jsonreport.mdresult.json
Flow
- Plan members — the
--integration-methodsset (nonebaseline + harmony- scanorama by default;
scvivia--include-scvi).
- scanorama by default;
- Fan out — run
sc-integrate-cluster --method <m>per member at the fixed--resolution(member cluster counts stay comparable for the operator). - Score — the driver computes the batch-mixing intrinsic panel (ADR 0029,
recalibrated on panc8) on each member's embedding + batch key. The single
scored axis is
ilisi_norm(iLISI diversity,log(iLISI)/log(n_batches)) — the one metric validated to track ground-truth cell-type recovery.knn_preservation_norm(within-batchX_pcaretention),batch_asw_normandcluster_asw_normare reported as weight-0 diagnostics (knn_preservationanti-correlated with recovery, so it flags over-integration but does not score). - Consensus — vote
kmode/weighted/lcaover the voting members (the integration methods; thenonebaseline is excluded by default, B2). - Report — banner + per-cell support/entropy + a k-divergence section.
What ships with it
5 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.
- 2d ago First seen · 129 lines · 79 tokens per session scan A 0f4137006e6a
sc-consensus-integration is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 79 tokens to every session and 1,531 once invoked, about $0.0004 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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