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Load when checking raw single-cell FASTQ read quality (Phred / GC / adapter / length) before counting. Skip when reads are already counted (use sc-qc) or for bulk FASTQ (use bulkrna-read-qc).
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill sc-fastq-qcコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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SOC 職業分類に基づく
SKILL.md を表示中
| name | sc-fastq-qc |
| description | Load when checking raw single-cell FASTQ read quality (Phred / GC / adapter / length) before counting. Skip when reads are already counted (use sc-qc) or for bulk FASTQ (use bulkrna-read-qc). |
| version | 0.3.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["singlecell","scrna","fastq","qc","read-quality"] |
| requires | ["pandas","matplotlib"] |
The user has raw scRNA-seq FASTQ files (one or more, or a directory of
samples) and wants per-file / per-sample / per-base quality summaries
before running sc-count or cellranger. Uses FastQC + MultiQC when
those tools are installed; falls back to a stable Python-only summary
otherwise so the skill always returns something useful.
| Input | Format | Required |
|---|---|---|
| Raw scRNA reads | .fastq / .fastq.gz (single file or directory) | yes (unless --demo) |
| Output | Path | Notes |
|---|---|---|
| Per-file table | tables/fastq_per_file_summary.csv | one row per FASTQ |
| Per-sample table | tables/fastq_per_sample_summary.csv | aggregated by sample |
| Per-base table | tables/fastq_per_base_quality.csv | quality vs read position |
| Quality figures | figures/fastq_q30_summary.png, figures/per_base_quality.png, figures/fastq_file_quality.png, figures/fastq_read_structure.png | all four always rendered |
| Report | report.md + result.json | always written |
--input (single file or directory).fastqc is on $PATH, run it; if multiqc is on $PATH, run that too.--max-reads per FASTQ for Phred / GC / adapter / length.report.md + result.json.--max-reads 20000 (default) caps the Python-fallback path only. When FastQC is available the full FASTQ is processed; when not, only the first 20K reads per file are sampled. Sampling depth is recorded per file in tables/fastq_per_file_summary.csv; bump --max-reads if a FASTQ has high variance across the file.--r-enhanced is accepted but produces no R plots. This skill emits Python figures only. Pass freely, expect no R Enhanced output.status: "rendered" is local, not global. The result.json carries a status field per figure (e.g. figures.per_base_quality.status == "rendered"). All four panels are emitted unconditionally (sc_fastq_qc.py:430-433), so absence of an entry typically means upstream tool failure rather than a configuration choice — inspect summary.warnings before assuming a panel was suppressed.# Demo (built-in synthetic FASTQ)
python omicsclaw.py run sc-fastq-qc --demo --output /tmp/sc_fastq_qc_demo
# Single-file with paired-end
python omicsclaw.py run sc-fastq-qc \
--input sample_R1.fastq.gz --read2 sample_R2.fastq.gz --output results/
# Directory of samples, deeper sampling for the Python fallback
python omicsclaw.py run sc-fastq-qc \
--input fastq_dir/ --output results/ --max-reads 100000 --threads 8
references/parameters.md — every CLI flag and tuning hintreferences/methodology.md — FastQC integration + Python fallback rationalereferences/output_contract.md — table column schemas + figure rolessc-count (next step — FASTQ → AnnData), bulkrna-read-qc (bulk RNA-seq variant), sc-qc (downstream count-matrix QC)