agentmods.dev

sc-cell-annotation

sc-cell-annotation is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 73 tokens per session (1,736 once invoked), scanned A, original, Apache-2.0.

A single-cell analysis that assigns biological cell-type labels to clusters using marker genes, reference data, or a supplied label map. Marker genes are genes whose activity helps identify a cell type.

In plain words
What is it for?
Use it after clustering to label cells or clusters in preprocessed single-cell data. It supports marker-based, manual, and reference-based labelling approaches.
Why use it?
Clusters only show groups of similar cells and do not explain what those groups are. Annotation turns those groups into interpretable labels such as T cell or macrophage.

Skill for Claude CodeCodex

not rated 160 +2 1mo ago A scan 73 tokens original Apache-2.0
View the source file TianGzlab/OmicsClaw
Install

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.

agentmods
npx agentmods add skills/tiangzlab/omicsclaw/sc-cell-annotation
Any agent
npx skills add TianGzlab/OmicsClaw --skill sc-cell-annotation
Clone the repo
git clone --depth 1 https://github.com/TianGzlab/OmicsClaw

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for sc-cell-annotation

README.md
[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-cell-annotation.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-cell-annotation)
Your own site
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-cell-annotation"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-cell-annotation.svg" alt="Measured on agentmods" height="20"></a>
Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,736 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00073 $0.01736
Opus 5 $0.00036 $0.00868
Sonnet 5 $0.00015 $0.00347
Haiku 4.5 $0.00007 $0.00174

Measured 2d ago against content hash 7b160e31c9ec, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

sc-cell-annotation 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.

The scan reads SKILL.md. This mod also ships 4 executable files (sc_annotate.py, tests/__init__.py, tests/test_sc_annotate_methods.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/singlecell/scrna/sc-cell-annotation/SKILL.md · 142 lines

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.

sc-cell-annotation

When to use

The user has a clustered AnnData (e.g. obs["leiden"]) and wants labelled cell types in obs["cell_type"]. Pick a method by data / reference availability:

  • markers (default) — built-in or custom marker-gene scoring.
  • manual — user-supplied cluster-to-label map (--manual-map or --manual-map-file).
  • celltypist — pretrained Immune_All_Low.pkl style classifier.
  • popv / knnpredict — reference AnnData mapping (PopV consensus or lightweight KNN).
  • singler / scmap — R-backed reference annotation.
  • scsa — Fisher-test DB scoring (--species, --tissue).

This skill labels — for ranking the genes that justify a label use sc-markers; for replicate-aware condition DE use sc-de.

Inputs & Outputs

Inputs

  • Modalities: scrna
  • File types: .h5ad
  • Requires a preprocessed AnnData (X normalised, PCA/neighbours present)

Outputs

  • tables/annotation_embedding_points.csv
  • tables/annotation_summary.csv
  • tables/cell_metadata.csv
  • tables/cell_type_counts.csv
  • tables/cellmarker2_markers.csv
  • tables/cluster_annotation_matrix.csv
  • tables/popv_predictions.csv
  • tables/scmap_results.csv
  • tables/singler_results.csv
  • figures/cell_type_counts.png
  • figures/cluster_to_cell_type_heatmap.png
  • figures/embedding_annotation_score.png
  • figures/embedding_cell_type.png
  • figures/embedding_cluster_vs_cell_type.png
  • figures/r_cell_barplot.png
  • figures/r_cell_proportion.png
  • figures/r_cell_sankey.png
  • figures/r_embedding_discrete.png
  • figures/r_embedding_feature.png
  • _demo_ref.h5ad
  • analysis_summary.txt
  • input.h5ad
  • processed.h5ad
  • report.md
  • result.json
  • Processed AnnData (saves_h5ad) — adds obs: cell_type, annotation_requested_method, annotation_score; obsm: cell_type_prob

Flow

  1. Load AnnData; preflight method-specific requirements (e.g., manual needs a map; popv / knnpredict need a reference).
  2. Resolve --cluster-key (auto-pick from leiden / louvain / cell_type if unset).
  3. Dispatch to the method-specific annotator (_METHOD_DISPATCH[method]).
  4. Record requested_method, actual_method, used_fallback, optional fallback_reason into obs + result.json.
  5. Build counts / cluster-matrix tables and standard figures.
  6. Save processed.h5ad, tables/, figures/, report.md, result.json.

Read the full file on GitHub · 142 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 142 lines · 73 tokens per session scan A 7b160e31c9ec

Subscribe to this mod's changes

sc-cell-annotation is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 1,736 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.

Scientificbioinformaticscell-annotationcelltypistknnpredictknowledge-graphmarkerspopvscmapscrnascsasinglecellsingler
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