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Agent skill

Fastqc Report Interpreter

by aipoch in aipoch/medical-research-skills

A skill your agent uses when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems.

MITAuto-check passedResearch & Science

Install Fastqc Report Interpreter

skills CLI
$ npx skills add aipoch/medical-research-skills --skill fastqc-report-interpreter -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills fastqc-report-interpreter --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Data Analysis/fastqc-report-interpreter' .claude/skills/fastqc-report-interpreter && 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
fastqc-report-interpreter
GitHub stars
1.9k
Token cost
~2.1k tokens
SKILL.md length
842 words
Files
3 (incl. scripts)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems.

  • Works in 4 steps: Quality Metrics Analysis → Issue Diagnosis → Batch Analysis → …
  • Analyzing FASTQC quality reports from sequencing data
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 13 more sections
  • Runs Python scripts from its folder; calls python

What it does

Fastqc Report Interpreter is an agent skill from aipoch/medical-research-skills. Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `fastqc-report-interpreter_audit_result_v2.json` and `scripts/main.py`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Analyzing FASTQC quality reports from sequencing data
  • Identifying quality issues in NGS datasets
  • Troubleshooting sequencing problems

Example prompts

  • “/fastqc-report-interpreter”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Quality Metrics Analysis
  2. Issue Diagnosis
  3. Batch Analysis
  4. Recommendation Generation

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

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

  • Network

    No URLs in SKILL.md.

    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

Fastqc Report Interpreter loads about 2.1k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 842 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 842 words, ~2,117 tokens.

Download SKILL.mdSave it as .claude/skills/fastqc-report-interpreter/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
fastqc-report-interpreter
description
Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

FASTQC Report Interpreter

Analyze FASTQC quality control reports for Next-Generation Sequencing (NGS) data to assess data quality and identify issues.

When to Use

  • Use this skill when the task needs Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
  • Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
  • Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

  • Scope-focused workflow aligned to: Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
  • Packaged executable path(s): scripts/main.py.
  • Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

  • Python: 3.10+. Repository baseline for current packaged skills.
  • Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

bash
cd "20260318/scientific-skills/Data Analytics/fastqc-report-interpreter"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/main.py with the validated inputs.
  4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

  • Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
  • Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
  • Primary implementation surface: scripts/main.py.
  • Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
  • Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

bash
python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

bash
python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

  1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
  2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
  3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
  4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
  5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Quick Start

python
from scripts.fastqc_interpreter import FASTQCInterpreter

interpreter = FASTQCInterpreter()

# Analyze report
analysis = interpreter.analyze("sample_fastqc.html")
print(f"Overall Quality: {analysis.quality_status}")
print(f"Issues Found: {analysis.issues}")

Core Capabilities

1. Quality Metrics Analysis
python
metrics = interpreter.parse_metrics("fastqc_data.txt")

Key Metrics:

MetricGoodWarningFail
Per base sequence qualityQ > 28Q 20-28Q < 20
Per sequence quality scoresPeak at Q30Peak Q20-30Peak < Q20
Per base N content< 5%5-20%> 20%
Sequence duplication< 20%20-50%> 50%
Adapter content< 5%5-10%> 10%
Show full SKILL.md (363 more words)Show less
2. Issue Diagnosis
python
issues = interpreter.diagnose_issues(metrics)
for issue in issues:
    print(f"{issue.severity}: {issue.description}")
    print(f"Recommendation: {issue.recommendation}")

Common Issues:

Low Quality at Read Ends

  • Cause: Phasing effects, reagent depletion
  • Solution: Trim last 10-20 bases

Adapter Contamination

  • Cause: Incomplete adapter removal
  • Solution: Re-run cutadapt/Trimmomatic with stricter parameters

High Duplication

  • Cause: PCR over-amplification, low input
  • Solution: Use deduplication; consider library prep optimization

Per Base Sequence Content Bias

  • Cause: Adapter dimers, non-random priming
  • Solution: Check for adapter contamination; randomize primers
3. Batch Analysis
python
batch_results = interpreter.analyze_batch(
    fastqc_files=["sample1_fastqc.html", "sample2_fastqc.html", ...],
    output_summary="batch_summary.csv"
)
4. Recommendation Generation
python
recommendations = interpreter.get_recommendations(
    analysis,
    application="rna_seq",  # or "dna_seq", "chip_seq"
    quality_threshold="high"
)

Application-Specific Thresholds:

  • RNA-seq: Acceptable duplication up to 40% (transcript abundance)
  • DNA-seq: Strict quality requirements (variant calling)
  • ChIP-seq: Moderate quality, focus on enrichment metrics

CLI Usage

text

# Analyze single report
python scripts/fastqc_interpreter.py --input sample_fastqc.html

# Batch analysis
python scripts/fastqc_interpreter.py --batch "*fastqc.html" --output report.pdf

# With custom thresholds
python scripts/fastqc_interpreter.py --input fastqc.html --application rna_seq

Output Interpretation

PASS (Green): Proceed with analysis WARNING (Yellow): Review but likely acceptable FAIL (Red): Requires action before downstream analysis

Troubleshooting Guide

See references/troubleshooting.md for:

  • Platform-specific issues (Illumina, PacBio, Oxford Nanopore)
  • Library prep problem diagnosis
  • Downstream analysis impact assessment

Skill ID: 205 | Version: 1.0 | License: MIT

Output Requirements

Every final response should make these items explicit when they are relevant:

  • Objective or requested deliverable
  • Inputs used and assumptions introduced
  • Workflow or decision path
  • Core result, recommendation, or artifact
  • Constraints, risks, caveats, or validation needs
  • Unresolved items and next-step checks

Error Handling

  • If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
  • If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
  • If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
  • Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of fastqc-report-interpreter and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

fastqc-report-interpreter only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

© aipoch, 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 (scripts) in scientific-skills/Data Analysis/fastqc-report-interpreter of aipoch/medical-research-skills.

  • SKILL.md
  • fastqc-report-interpreter_audit_result_v2.json
  • scripts/main.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

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Fastqc Report Interpreter compared with similar skills
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Fastqc Report Interpreter this skillaipoch/medical-research-skills1.9k—~2.1kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

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Categories

Questions about Fastqc Report Interpreter

What does Fastqc Report Interpreter do?

A skill your agent uses when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Fastqc Report Interpreter is an agent skill from aipoch/medical-research-skills. Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems.

When should I use Fastqc Report Interpreter?

Fastqc Report Interpreter fits situations like: analyzing FASTQC quality reports from sequencing data; identifying quality issues in NGS datasets; troubleshooting sequencing problems.

How do I install Fastqc Report Interpreter in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill fastqc-report-interpreter -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/fastqc-report-interpreter in aipoch/medical-research-skills) into .claude/skills/fastqc-report-interpreter in your project. Claude Code loads it when a task matches its description.

How do I install Fastqc Report Interpreter in Codex?

Run `npx skills add aipoch/medical-research-skills --skill fastqc-report-interpreter -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/fastqc-report-interpreter in aipoch/medical-research-skills) into .agents/skills/fastqc-report-interpreter in your project. Codex loads it when a task matches its description.

Can I use Fastqc Report Interpreter 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 aipoch/medical-research-skills --skill fastqc-report-interpreter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fastqc-report-interpreter, .gemini/skills/fastqc-report-interpreter, .github/skills/fastqc-report-interpreter and .opencode/skills/fastqc-report-interpreter in your project.

What does Fastqc Report Interpreter need to run?

Going by SKILL.md and its folder, Fastqc Report Interpreter needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Fastqc Report Interpreter access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fastqc Report Interpreter 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Fastqc Report Interpreter use?

Fastqc Report Interpreter is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fastqc Report Interpreter use?

About 2.1k tokens (SKILL.md is roughly 8.5k 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 Fastqc Report Interpreter?

Skills that share tags, products or a category with Fastqc Report Interpreter: 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 Fastqc Report Interpreter?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.