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Agent skill

Bio Read Qc Quality Reports

by GPTomics in GPTomics/bioSkills

Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented…

MITAuto-check passedResearch & Science

Install Bio Read Qc Quality Reports

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-read-qc-quality-reports -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-read-qc-quality-reports --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/read-qc/quality-reports .claude/skills/bio-read-qc-quality-reports && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-read-qc-quality-reports
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.6k tokens
SKILL.md length
1,826 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented…

  • Works in 3 steps: FastQC pass/warn/fail are heuristics… → On 2-color chemistry (NextSeq, NovaSeq,… → The duplication percentage is read-level…
  • Performing initial QC on raw sequencing reads
  • SKILL.md covers Version Compatibility, The Single Most Important…, Tool Taxonomy and Decision Tree by Scenario, plus 7 more sections
  • Runs Shell scripts from its folder; calls pip

What it does

Bio Read Qc Quality Reports is an agent skill from GPTomics/bioSkills. Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented sequences, and adapter content. Use when performing initial QC on raw sequencing reads, validating preprocessing, or judging a multi-sample cohort for outliers and batch effects. For long reads use NanoPlot; for adapter/quality remediation route to adapter-trimming, quality-filtering, or fastp-workflow.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/run_qc_pipeline.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Performing initial QC on raw sequencing reads
  • Validating preprocessing
  • Judging a multi-sample cohort for outliers and batch effects

Example prompts

  • “Use the bio-read-qc-quality-reports skill to generate and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and…”
  • “/bio-read-qc-quality-reports”

Requirements

  • A Bash shell

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. FastQC pass/warn/fail are heuristics calibrated to random whole-genome DNA, so they FALSE-FAIL on every other assay. RNA-seq fails…
  2. On 2-color chemistry (NextSeq, NovaSeq, MiniSeq) G is the ABSENCE of signal, so poly-G tails are called at HIGH quality and the quality…
  3. The duplication percentage is read-level and complexity-blind: it cannot tell a PCR jackpot from genuine high abundance. Identical reads…

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Read Qc Quality Reports loads about 3.6k tokens when it runs. Until then it costs about 131 tokens; SKILL.md has 1,826 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~131
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,826 words, ~3,608 tokens.

Download SKILL.mdSave it as .claude/skills/bio-read-qc-quality-reports/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-read-qc-quality-reports
description
Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented sequences, and adapter content. Use when performing initial QC on raw sequencing reads, validating preprocessing, or judging a multi-sample cohort for outliers and batch effects. For long reads use NanoPlot; for adapter/quality remediation route to adapter-trimming, quality-filtering, or fastp-workflow.
tool_type
cli
primary_tool
fastqc

Version Compatibility

Reference examples tested with: FastQC 0.12+, MultiQC 1.21+, falco 1.2+, seqkit 2.5+

Before using code patterns, verify installed versions match. If versions differ:

  • CLI: <tool> --version then <tool> --help to confirm flags
  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Quality Reports -- the traffic light is a hypothesis about WGS DNA, not a verdict

Generate per-file QC with FastQC/falco and aggregate the cohort with MultiQC, then READ THE PLOTS against the assay rather than trusting pass/warn/fail.

"Run quality control on FASTQ files" -> Compute per-base quality, composition, GC, duplication, and adapter profiles per file, then aggregate across samples to find outliers.

  • CLI: fastqc -t 8 *.fastq.gz then multiqc .
  • Long reads: NanoPlot --fastq reads.fastq.gz (FastQC assumes fixed-length short reads)

Scope: this skill OWNS raw-FASTQ QC reporting and interpretation, and carries the cross-cutting quality-score / chemistry / duplication concepts the rest of read-qc depends on. Remediation lives elsewhere -> read-qc/adapter-trimming, read-qc/quality-filtering, read-qc/fastp-workflow. Contamination -> read-qc/contamination-screening. Transcriptome QC on the BAM -> read-qc/rnaseq-qc. OUT OF SCOPE: any modification of the reads.

The Single Most Important Modern Insight

  1. FastQC pass/warn/fail are heuristics calibrated to random whole-genome DNA, so they FALSE-FAIL on every other assay. RNA-seq fails per-base content (random-hexamer bias) and duplication (high-expression molecules); amplicon fails duplication and GC by design; bisulfite fails base content (C->T conversion); small-RNA fails length distribution; single-cell R1 fails everything (it is barcode+UMI, not biology). A red light is a HYPOTHESIS about a WGS library. For any other protocol, first ask "is this module expected to deviate for this chemistry?" before treating the red as a defect. Read the plot shape; the traffic light is calibration noise.

  2. On 2-color chemistry (NextSeq, NovaSeq, MiniSeq) G is the ABSENCE of signal, so poly-G tails are called at HIGH quality and the quality plot will NOT flag them. When a cluster runs out of template, dark cycles read as a run of Gs with high confidence. Quality trimming alone does not remove them. They surface as a 3'-end RISE in G content (per-base sequence content) and a spurious high-GC spike, and they mis-map or manufacture false somatic variants if left in. The fix is a chemistry-aware poly-G trim (fastp auto-enables it from the instrument ID; cutadapt --nextseq-trim), not a quality cutoff. Read the per-base CONTENT plot on any 2-color run, not just the quality plot.

  3. The duplication percentage is read-level and complexity-blind: it cannot tell a PCR jackpot from genuine high abundance. Identical reads from a highly expressed transcript, a targeted amplicon, or a ChIP/ATAC peak are counted as duplicates even though they are independent biological molecules. Duplication % is a function of BOTH library complexity AND sequencing depth (a good library sequenced deeply shows high duplication). It is a PROMPT to reason about library complexity (preseq), never an automatic "remove duplicates" -- and removing duplicates in non-UMI RNA-seq is actively wrong (read-qc/umi-processing, read-qc/rnaseq-qc).

Bonus trap: NovaSeq/NextSeq emit BINNED quality scores (RTA3 uses four values: 2, 12, 23, 37), so FastQC box plots look blocky/quantized. This is the instrument's quality table, NOT bad data and NOT something to fix. The bin edges are RTA-version-specific (NovaSeq X / RTA4 differs) -- never hard-code one bin set.

Tool Taxonomy

ToolRoleMechanism / when
FastQCPer-file short-read QC (HTML + zip)Java; the module set below; duplication/overrep from the first 100k distinct reads. The de-facto standard per-file report.
falcoDrop-in FastQC re-implementation (C++)~3x faster, lower memory, same module names and MultiQC-compatible output. Use when FastQC throughput bottlenecks a large cohort.
MultiQCCross-sample aggregator (SCRAPER, not a re-analyzer)Walks directories, regex-matches each tool's log/report, parses the numbers, builds one cohort report. The unit of review for multi-sample studies.
seqkit statsInstant tabular FASTA/FASTQ numbersseqkit stats -a: N50, Q20%, Q30%, GC%, length quartiles. For quick numbers and assembly/long-read contexts where FastQC is the wrong shape.
NanoPlot / NanoCompLong-read (ONT/PacBio) QCRead-length and quality distributions, yield, N50, length-vs-quality. The correct first pass for long reads; FastQC's fixed-length assumptions break there.

Decision Tree by Scenario

ScenarioUseWhy
Per-file Illumina short-read QCFastQC (or falco)Module-level diagnostics; read the plots by assay
Many samples / a study cohortFastQC/falco then MultiQCOutlier and batch detection is RELATIVE; only visible overlaid
Long reads (ONT/PacBio)NanoPlot / NanoCompFastQC is built for fixed-length short reads
Instant numbers, assembly inputseqkit stats -aN50/Q20/Q30/GC in one line; no HTML overhead
Large cohort, FastQC too slowfalco then MultiQCSame output, ~3x faster

Default when uncertain: FastQC on each file, then MultiQC over the run directory, and judge each sample against the cohort.

FastQC Modules -- thresholds and the expert read

Thresholds are FastQC's limits.txt defaults (calibrated to random WGS DNA). The expert read is what to conclude BEYOND the traffic light.

ModuleDefault warn / failExpert read
Per base sequence qualitywarn LQ<10 or median<25; fail LQ<5 or median<203' decay is normal; blocky boxes on NovaSeq are binning; this plot will NOT reveal poly-G on 2-color
Per tile sequence qualityspatial deviation (no numeric)A hot tile band across cycles = a localized flowcell problem (bubble, debris, edge); reason no MultiQC table replaces raw FastQC
Per sequence quality scoresdistribution of per-read mean QA low-Q hump = a junk subpopulation to FILTER (not trim)
Per base sequence contentwarn dev>10%; fail dev>20%First ~12 bp skew = random-hexamer priming (Hansen 2010), expected for RNA-seq, do NOT trim it. A 3'-end skew is poly-G / adapter -- act on that
Per sequence GC contentwarn dev>15%; fail dev>30%SHAPE matters: bimodal/secondary peak = contamination; sharp spike = adapter dimer / overrepresented; a shifted single peak = wrong-GC reference assumption
Per base N contentwarn N>5%; fail N>20%Ns at a fixed position = a failed cycle; rising 3' Ns = dying clusters
Sequence length distributionwarn if lengths differ; fail if any length 0WARNs trivially after trimming and on long reads -- ignore for those
Sequence duplication levelswarn if <70% would remain; fail if <50%Read-level, complexity-blind (see insight 3); high = think complexity, not dedup
Overrepresented sequenceswarn >0.1%; fail >1%Most diagnostic module: it prints the sequence -- BLAST it (adapter dimer, rRNA, primer, poly-G)
Adapter contentwarn k-mer>5%; fail >10%A curve climbing toward 3' = read-through from short inserts; this panel IS an insert-size readout (route to adapter-trimming)
K-mer content(deprecated, off by default)Only appears in old reports; do not build guidance on it

Algorithm note (why duplication/overrep are estimates): FastQC tracks only the first 100,000 DISTINCT sequences, keys on the first 50 bp for reads >75 bp (so 3' errors do not fragment a duplicate family), counts by exact identity, and extrapolates the "% remaining if deduplicated" headline. It is a sample-based estimate, not a full-library dedup.

Show full SKILL.md (711 more words)Show less

Duplication Taxonomy -- four causes, four actions

ClassMechanismDetected byAction
OpticalOne real cluster mis-segmented (non-patterned flowcell)Same tile, pixel distance (Picard default 100)Removable; spatially local artifact
ExAmp / patternedOne molecule seeds two nanowells (HiSeq X/4000, NovaSeq)Spatially clustered, larger radius (Picard 2500 for patterned)Removable; the reason patterned flowcells need the bigger pixel distance
PCRSame fragment amplified and sequenced twiceIdentical 5' coordinates post-alignment (+UMI if present)Mark/remove for variant calling; NEVER coordinate-dedup amplicon (use UMIs)
Natural / biologicalIndependent identical molecules (high coverage, expressed genes, amplicon start)Indistinguishable from PCR at read level without UMIsKEEP -- removing biases quantification (do not dedup non-UMI RNA-seq)

The read-level duplication % FastQC reports cannot separate these. Use preseq (Daley & Smith 2013) to model the complexity curve and ask "how many NEW molecules would more sequencing buy?" -- that curve, not a single %, judges whether a library is exhausted or just deeply sequenced.

Quality scores and encoding

Phred Q = -10*log10(P_error): Q20 = 1% error, Q30 = 0.1%, Q40 = 0.01%. Q30 is the routine Illumina target; bulk Q40+ is uncommon on legacy chemistry (phasing, signal decay) and is a tell for re-binned or synthetic data on old runs, though XLEAP-SBS (NovaSeq X, NextSeq 2000) genuinely reaches Q40+. Modern data is universally Phred+33; any Phred+64 file (Illumina 1.3-1.7) feeds 31-too-high scores to a +33-assuming tool and passes garbage silently -- convert it (seqtk seq -Q64 -V). A quality byte below ASCII 64 (digits/punctuation) proves +33; detection tools sample reads to break ties.

MultiQC -- the cohort is the unit of review

MultiQC does NOT re-analyze data; it scrapes tool logs/reports (search_patterns.yaml), parses the numbers, and tabulates them per sample. Consequences: it is only as good as the files left on disk and the sample-name parsing (name collisions merge samples -- check multiqc_data/multiqc_sources.txt), and it reports whatever the upstream tool wrote (a wrong reference or wrong strandedness shows as a coherent-but-wrong table, not an error). Read the General Statistics table FIRST -- outliers jump out as a column anomaly -- then overlay per-base-quality / GC / duplication and ask whether the low-quality set maps to one lane / prep batch / operator. The batch effect caught here at QC is the one not chased for a month in the DE results.

bash
# Per-file QC, then aggregate the run
fastqc -t 8 -o qc/raw/ raw_data/*.fastq.gz
multiqc qc/raw/ -o qc/multiqc/ -f

# Compare before vs after trimming in one report
fastqc -t 8 -o qc/trimmed/ trimmed/*.fastq.gz
multiqc qc/ -o qc/compare/ -f          # picks up both raw/ and trimmed/

# Long reads do not go through FastQC
NanoPlot --fastq ont_reads.fastq.gz -o qc/nanoplot/

Common Errors

SymptomCauseSolution
Every RNA-seq sample fails per-base contentRandom-hexamer 5' bias (Hansen 2010)Expected; do not trim the first bases
High-Q reads but a 3' G-content rise on NovaSeq2-color poly-G (dark cycles = G)Chemistry-aware poly-G trim (fastp / cutadapt --nextseq-trim), not -q
FastQC quality boxes look quantized/blockyNovaSeq/NextSeq binned qualities (RTA3)Expected; not a defect, do not "fix"
MultiQC merges two samples into one rowOver-aggressive name cleaning / collisionCheck multiqc_sources.txt; use --fn_as_s_name or fix names
Duplication 60%, urge to dedup RNA-seqRead-level dup is complexity-blindDo not dedup non-UMI RNA-seq; assess complexity (preseq)
FastQC crashes / huge plot on long readsFixed-length short-read assumptionsUse NanoPlot / seqkit stats instead
FastQC module missing in MultiQCThe fastqc_data.txt was not on disk / wrong dirPoint MultiQC at the directory holding the zip/data files

References

de Sena Brandine G, Smith AD. 2019. Falco: high-speed FastQC emulation for quality control of sequencing data. F1000Research 8:1874. Ewing B, Hillier L, Wendl MC, Green P. 1998. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Research 8(3):175-185. Ewing B, Green P. 1998. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Research 8(3):186-194. Hansen KD, Brenner SE, Dudoit S. 2010. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Research 38(12):e131. Daley T, Smith AD. 2013. Predicting the molecular complexity of sequencing libraries. Nature Methods 10(4):325-327. Ewels P, Magnusson M, Lundin S, Kaller M. 2016. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32(19):3047-3048. Shen W, Le S, Li Y, Hu F. 2016. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11(10):e0163962. De Coster W, D'Hert S, Schultz DT, Cruts M, Van Broeckhoven C. 2018. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34(15):2666-2669.

read-qc/adapter-trimming - Remove read-through adapter flagged by the adapter-content panel read-qc/quality-filtering - Drop low-quality reads and trim ends read-qc/fastp-workflow - All-in-one QC + trim, including 2-color poly-G read-qc/contamination-screening - Resolve a bimodal-GC or unexpected overrepresented-sequence signal read-qc/rnaseq-qc - Transcriptome QC (strandedness, gene-body coverage) on the aligned BAM sequence-io/sequence-statistics - Programmatic per-file sequence summaries

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in read-qc/quality-reports of GPTomics/bioSkills.

  • SKILL.md
  • examples/run_qc_pipeline.sh
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Bio Read Qc Quality Reports

What does Bio Read Qc Quality Reports do?

Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented…. Bio Read Qc Quality Reports is an agent skill from GPTomics/bioSkills. Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented sequences, and adapter content.

When should I use Bio Read Qc Quality Reports?

Bio Read Qc Quality Reports fits situations like: performing initial QC on raw sequencing reads; validating preprocessing; judging a multi-sample cohort for outliers and batch effects.

How do I install Bio Read Qc Quality Reports in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-read-qc-quality-reports -a claude-code`. Or copy the skill folder (read-qc/quality-reports in GPTomics/bioSkills) into .claude/skills/bio-read-qc-quality-reports in your project. Claude Code loads it when a task matches its description.

How do I install Bio Read Qc Quality Reports in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-read-qc-quality-reports -a codex`. Or copy the skill folder (read-qc/quality-reports in GPTomics/bioSkills) into .agents/skills/bio-read-qc-quality-reports in your project. Codex loads it when a task matches its description.

Can I use Bio Read Qc Quality Reports in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-read-qc-quality-reports -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-read-qc-quality-reports, .gemini/skills/bio-read-qc-quality-reports, .github/skills/bio-read-qc-quality-reports and .opencode/skills/bio-read-qc-quality-reports in your project.

What does Bio Read Qc Quality Reports need to run?

Going by SKILL.md and its folder, Bio Read Qc Quality Reports needs a shell for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: A Bash shell.

Does Bio Read Qc Quality Reports access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Read Qc Quality Reports safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Read Qc Quality Reports use?

Bio Read Qc Quality Reports is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Read Qc Quality Reports use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Read Qc Quality Reports?

Skills that share tags, products or a category with Bio Read Qc Quality Reports: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Read Qc Quality Reports?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.