A filtering step for single-cell RNA sequencing data that removes cells and genes with too little or too much measured activity. It can use explicit quality thresholds or tissue-specific presets.

Use it to filter cells by detected genes, counts, or mitochondrial RNA and to remove genes detected in too few cells.

It removes low-quality observations that can add noise and make later analyses less reliable.

sc-filter — Skill for Claude Code and Codex by TianGzlab

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.

Any agent

npx skills add TianGzlab/OmicsClaw --skill sc-filter

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-filter.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-filter)

Your own site

<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-filter"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-filter.svg" alt="Measured on agentmods" height="20"></a>

Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,238 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

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.

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00063	$0.01238
Opus 5	$0.00032	$0.00619
Sonnet 5	$0.00013	$0.00248
Haiku 4.5	$0.00006	$0.00124

Measured 5d ago against content hash c93c9b5eb57c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

sc-filter 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 5d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (sc_filter.py, tests/test_sc_filter.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-filter/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sc-filter

When to use

The user has reviewed sc-qc output and now wants to actually drop low-quality cells and lowly-detected genes — by per-cell thresholds (--min-genes, --max-genes, --max-mt-percent, --min-counts, --max-counts, --min-cells) or tissue-specific presets (--tissue brain / pbmc / etc.). This skill removes cells; it does not normalise, cluster, or annotate.

Inputs & Outputs

Inputs

Modalities: scrna
File types: .h5ad

Outputs

tables/cell_metadata.csv
tables/filter_reasons.csv
tables/filter_state.csv
tables/filter_stats.csv
tables/filter_summary.csv
tables/gene_expression.csv
tables/retention_summary.csv
figures/filter_comparison.png
figures/filter_reason_summary.png
figures/filter_state_scatter.png
figures/filter_summary.png
figures/filter_thresholds.png
figures/r_feature_violin.png
analysis_summary.txt
processed.h5ad
report.md
result.json
Processed AnnData (saves_h5ad)

Flow

Load AnnData via shared loader; persist expression_source in result.json.
If --tissue is set, apply preset thresholds (overrides any matching CLI flag silently).
Compute per-cell metrics; mark cells / genes failing each rule.
Drop cells failing any active rule; drop genes detected in fewer than --min-cells cells.
Emit before/after retention tables and figures.
Save processed.h5ad + report.md + result.json.

Gotchas

--tissue presets silently override matching CLI flags. Passing --tissue pbmc plus --max-mt-percent 30 resolves to whatever the PBMC preset declares for max_mt_percent, not 30. When mixing, omit the explicit flag or override the preset by editing it in references/methodology.md. Result tables record the effective thresholds, not the user-passed ones.
QC metrics are computed on demand if missing. sc_filter.py:617-622 calls ensure_qc_metrics(...) when the AnnData lacks n_genes_by_counts / pct_counts_mt, so this skill works without a prior sc-qc run. Running sc-qc first is still recommended for diagnostic figures, but it's not a hard prerequisite — the routing description used to overstate this.
Input file missing → hard fail. sc_filter.py:573 raises FileNotFoundError on a non-existent --input. Common in batch pipelines when an upstream output dir was renamed.
expression_source is recorded but does not gate the filter. result.json["summary"]["expression_source"] carries which matrix the metrics came from (layers.counts / adata.raw / adata.X). Filtering still runs even if the source is log-normalised — but total_counts / mt% interpretations become meaningless. Check the source before relying on the thresholds.
processed.h5ad is contract-preserving, not contract-canonical. The skill keeps whatever layers / raw / uns the input had; if upstream skipped sc-standardize-input, downstream skills may still mis-classify the count source. Run sc-standardize-input before sc-filter when input came from outside OmicsClaw.

Read the full file on GitHub · 108 lines

Files

What ships with it

7 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.

5d ago First seen · 108 lines · 63 tokens per session scan A c93c9b5eb57c

Subscribe to this mod's changes

sc-filter is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 1,238 once invoked, about $0.0003 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.

Scientific Single-Cell bioinformatics filter knowledge-graph llm-agent mitochondrial multi-agents multi-omics qc scrna singlecell

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cellxgene-census-query

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…

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Complete single-cell RNA-seq analysis workflow built on Scanpy and AnnData. Use this skill when: (1) Loading diverse single-cell data formats (10X, h5ad, CSV), (2) Performing quality control and filtering, (3) Normalization, dimensionality reduction, and clustering, (4) Marker gene identification and cell type…

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sc-filter

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

sc-filter

When to use

Inputs & Outputs

Flow

Gotchas

What ships with it

What this file has done since we first saw it

Other skills, from other repositories

Single-Cell Analysis Skills Index

scRNA_qc_checklist

scanpy

Virtual Embryo — atlas data + knowledge graph

cellxgene-census-query

single-cell-scrna-seq-analysis-scanpy