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Agent skill

Mageck

by K-Dense-AI in K-Dense-AI/scientific-agent-skills

Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect…

MITAuto-check passedResearch & Science

Install Mageck

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill mageck -a claude-code

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

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills mageck --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/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mageck .claude/skills/mageck && 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
mageck
GitHub stars
48k
Used in
1 other repo
Token cost
~2.2k tokens
SKILL.md length
929 words
Files
4 (incl. scripts, references)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect…

  • Works in 7 steps: Establish the library version,… → Validate a headerless TSV library… → For FASTQ, inspect read structure and… → …
  • CRISPRa screen analysis
  • SKILL.md covers When to use, Runtime, Workflow and Commands, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Mageck is an agent skill from K-Dense-AI/scientific-agent-skills. Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect sizes and FDR. Use for new knockout, CRISPRi, or CRISPRa screen analysis, enrichment or depletion contrasts, and MAGeCK count/test workflows; existing public dependency-score lookup belongs to DepMap.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/design.md`, `references/runtime.md` and `scripts/screen_analysis.py`). Compatibility notes: Requires Python 3.10+ for the helper and MAGeCK 0.5.9.5 with its compiled RRA executable for analysis. Tested runtime uses Python 3.11, NumPy 1.26.4 and SciPy…

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.

When your agent uses it

  • CRISPRa screen analysis
  • Depletion contrasts
  • MAGeCK count/test workflows
  • Existing public dependency-score lookup belongs to DepMap

Example prompts

  • “Use the mageck skill to analyz pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts…”
  • “/mageck”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires Python 3.10+ for the helper and MAGeCK 0.5.9.5 with its compiled RRA executable for analysis. Tested runtime uses Python 3.11, NumPy 1.26.4 and SciPy 1.13.1. Source installation needs a C++ compiler; network is needed only for installation. No credentials.

Workflow steps

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

  1. Establish the library version, perturbation modality, sample names, selection direction,
  2. Validate a headerless TSV library containing guide ID, DNA sequence, gene. IDs and
  3. For FASTQ, inspect read structure and known guide sequences to establish trimming and
  4. Run QC before statistical testing. Review library representation, median reads per guide,
  5. Choose normalization based on the screen. Median normalization assumes most guides are
  6. Use test for a two-group comparison. --paired requires both lists in corresponding
  7. Inspect guide concordance for leading genes, essential-gene recovery where appropriate,

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. 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

    Links to these hosts (documentation or services it may open):

    • sourceforge.net
    • github.com

    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.

  • Compatibility

    Requires Python 3.10+ for the helper and MAGeCK 0.5.9.5 with its compiled RRA executable for analysis. Tested runtime uses Python 3.11, NumPy 1.26.4 and SciPy 1.13.1. Source installation needs a C++ compiler; network is needed only for installation. No credentials.

    From compatibility in the SKILL.md frontmatter.

Context cost

Mageck loads about 2.2k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 929 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~102
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.8k

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 K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 929 words, ~2,153 tokens.

Download SKILL.mdSave it as .claude/skills/mageck/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
mageck
description
Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect sizes and FDR. Use for new knockout, CRISPRi, or CRISPRa screen analysis, enrichment or depletion contrasts, and MAGeCK count/test workflows; existing public dependency-score lookup belongs to DepMap.
compatibility
Requires Python 3.10+ for the helper and MAGeCK 0.5.9.5 with its compiled RRA executable for analysis. Tested runtime uses Python 3.11, NumPy 1.26.4 and SciPy 1.13.1. Source installation needs a C++ compiler; network is needed only for installation. No credentials.
license
MIT
metadata.version
1.1
metadata.skill-author
K-Dense Inc.
metadata.upstream-version
0.5.9.5
metadata.last-reviewed
2026-10-01

MAGeCK pooled-screen analysis

When to use

Use this skill to count existing sequencing reads against a supplied guide library or compare already-counted pooled screens. Deliver the count matrix, QC, guide and gene results, contrast provenance, and a short interpretation of enrichment/depletion. This workflow analyzes screens; it does not design guides or infer gene function from a hit alone.

Runtime

The tested source installation and external-runtime caveat are in references/runtime.md. Verify both mageck --version and mageck test --help before an analysis. The bundled Python helper is standard-library only. MAGeCK itself also needs NumPy, SciPy, and the RRA binary. PDF/R reporting is optional and not needed by the helper.

The official release directory still lists 0.5.9.5 as its latest MAGeCK release. Upstream now links the separate MAGeCK2 project; these commands and the helper target MAGeCK 0.5.9.5, not an interchangeable MAGeCK2 installation. This is a local CLI workflow with no service API or authentication.

Workflow

  1. Establish the library version, perturbation modality, sample names, selection direction, biological replicates, baseline material, time point, and batch. Separate sequencing lanes from independent biological replicates. A plasmid baseline and a cell day-zero baseline answer different questions. Require an explicit treatment/control contrast; the helper never silently assigns all unused samples to the control group.
  2. Validate a headerless TSV library containing guide ID, DNA sequence, gene. IDs and sequences must be unique. The helper requires a count-table header beginning sgRNA, Gene, followed by unique nonnumeric sample names such as c1. MAGeCK can interpret numeric names as column indices or count values; rename them before analysis. Guide/gene IDs must have no whitespace. The helper rejects ambiguous sequences and any count/library ID or gene mismatch; resolve intentional multi-target guides explicitly upstream. These are deliberate helper restrictions; native MAGeCK also accepts other input variants.
  3. For FASTQ, inspect read structure and known guide sequences to establish trimming and orientation. Use MAGeCK count, with one space-separated argument per biological sample; comma-join lanes only when they are technical replicates of that same sample. Preserve unmapped-read and count-summary evidence when mapping is poor. A zero-count guide remains in the library; do not drop it to improve QC.
  4. Run QC before statistical testing. Review library representation, median reads per guide, zero fractions, Gini coefficients, and within-condition replicate correlations. The helper's 10% zero and 0.8 correlation flags are review prompts, not universal acceptance thresholds. High correlation can coexist with systematic artifacts. Read depth is not experimental cell coverage. The helper uses a raw-count population Gini; MAGeCK's native count-summary Gini uses log(count + 1) with a finite-sample correction. Do not compare their values or thresholds as the same statistic.
  5. Choose normalization based on the screen. Median normalization assumes most guides are stable. For a strong global shift, supplied validated negative-control guides may support --normalization control. These must be guide IDs, one per line; a gene list is not interchangeable. Biological control samples and negative-control guides serve different roles. At least two controls must be present, and every guide assigned to a control gene must be designated a control. Supplying --control-guides also changes the RRA null distribution, even with median normalization. Record their origin and check their count distribution. MAGeCK 0.5.9.5 switches median normalization to total-count scaling for a zero median or more than 45% zero guides in any selected sample; for control normalization it evaluates the control-guide subset. Check the report's applied method, size factors, and warnings.
  6. Use test for a two-group comparison. --paired requires both lists in corresponding biological order and equal length; matching lengths alone do not establish pairing. The helper reports genes at the requested FDR in both directions and retains full rankings. For a multi-factor design, see references/design.md; do not collapse batches or time courses into an unjustified two-group test.
  7. Inspect guide concordance for leading genes, essential-gene recovery where appropriate, negative controls, replicate consistency, and copy-number artifacts in nuclease knockout screens. Report effect sizes alongside FDR. An enriched guide can indicate resistance, growth advantage, or a sampling artifact depending on the selection; depletion need not imply universal essentiality. Lack of replication or low-count guides weakens inference.
Show full SKILL.md (253 more words)Show less

Commands

Run paths relative to the installed skill directory. Input and output paths refer to the user's analysis directory. The helper refuses to reuse an existing results directory.

bash
# Single-end guide reads; trim and orientation must match the user's library preparation.
mageck count -l library.tsv --fastq c1.fastq.gz c2.fastq.gz t1.fastq.gz t2.fastq.gz \
  --sample-label c1,c2,t1,t2 --trim-5 0 --norm-method none -n counts

python scripts/screen_analysis.py qc --counts counts.count.txt --library library.tsv \
  --control c1 c2 --treatment t1 t2 --output qc.json

python scripts/screen_analysis.py test --counts counts.count.txt --library library.tsv \
  --control c1 c2 --treatment t1 t2 --normalization median --fdr 0.05 --output result

The FASTQ command structure was exercised with a synthetic two-guide library: counts of 30 and 12 were recovered exactly. A separate trimmed, reverse-complemented, two-lane fixture recovered 15, 7, and 0 reads. The two-group helper was exercised with 500 guides and two replicates per condition in unpaired and paired modes; known depleted and enriched genes ranked first in their respective directions and passed FDR 0.05. Sparse fixtures verified the total-normalization fallback. These tests establish execution and signal direction, not real-screen statistical calibration. Real-file paths above are illustrative.

result/report.json records count/library/control-guide SHA-256, control-guide IDs, MAGeCK version, actual arguments, QC, applied normalization, warnings, hit direction, FDR, log2 fold change and rank. The helper explicitly selects median guide LFC aggregation and --remove-zero both: all-zero guides remain in the input/QC but are excluded from ranking. Native MAGeCK also skips NA/na gene labels by default. The helper's --fdr filters completed gene results; it does not set MAGeCK's --gene-test-fdr-threshold, which controls the RRA guide-selection cutoff. Negative and positive FDRs are separate families; their union does not establish joint FDR control across directions or across multiple contrasts. screen.gene_summary.txt, screen.sgrna_summary.txt, normalized counts and the execution log retain the complete evidence. Include the original library, sample sheet and negative-control list in the analysis handoff; the count checksum cannot reconstruct them.

Primary references

© K-Dense-AI, 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 3 other files (scripts, references) in skills/mageck of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • references/design.md
  • references/runtime.md
  • scripts/screen_analysis.py

Open the folder on GitHubat commit 92ace75

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 K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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MFA Pipeline Orchestratoraiming-lab/AutoResearchClaw15k—~923Automated safety check: PassMIT

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Categories

Questions about Mageck

What does Mageck do?

Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect…. Mageck is an agent skill from K-Dense-AI/scientific-agent-skills. Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect sizes and FDR.

When should I use Mageck?

Mageck fits situations like: CRISPRa screen analysis; depletion contrasts; MAGeCK count/test workflows; existing public dependency-score lookup belongs to DepMap.

How do I install Mageck in Claude Code?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill mageck -a claude-code`. Or copy the skill folder (skills/mageck in K-Dense-AI/scientific-agent-skills) into .claude/skills/mageck in your project. Claude Code loads it when a task matches its description.

How do I install Mageck in Codex?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill mageck -a codex`. Or copy the skill folder (skills/mageck in K-Dense-AI/scientific-agent-skills) into .agents/skills/mageck in your project. Codex loads it when a task matches its description.

Can I use Mageck 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 K-Dense-AI/scientific-agent-skills --skill mageck -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mageck, .gemini/skills/mageck, .github/skills/mageck and .opencode/skills/mageck in your project.

What does Mageck need to run?

Going by SKILL.md and its folder, Mageck needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires Python 3.10+ for the helper and MAGeCK 0.5.9.5 with its compiled RRA executable for analysis. Tested runtime uses Python 3.11, NumPy 1.26.4 and SciPy 1.13.1. Source installation needs a C++ compiler; network is needed only for installation. No credentials..

Does Mageck access the network?

SKILL.md names 2 domains. As links in the text: sourceforge.net and github.com. This is read from the text; nothing was executed.

Is Mageck 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 Mageck use?

Mageck 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 Mageck use?

About 2.2k tokens (SKILL.md is roughly 8.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Mageck?

Skills that share tags, products or a category with Mageck: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars), Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars) and Dbsnp Database (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mageck?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.

Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.