Agent skill

Proteomics Ms Qc

Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV.

Apache-2.0Auto-check passedResearch & Science

Install Proteomics Ms Qc

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a claude-code

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-ms-qc --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/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/proteomics/proteomics-ms-qc .claude/skills/proteomics-ms-qc && 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/

Paste into Claude Code

Install the "proteomics-ms-qc" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc into .claude/skills/proteomics-ms-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-ms-qc", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-ms-qc --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/proteomics/proteomics-ms-qc .agents/skills/proteomics-ms-qc && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "proteomics-ms-qc" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc into .agents/skills/proteomics-ms-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-ms-qc", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-ms-qc --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/proteomics/proteomics-ms-qc .cursor/skills/proteomics-ms-qc && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "proteomics-ms-qc" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc into .cursor/skills/proteomics-ms-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-ms-qc", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/TianGzlab/OmicsClaw.git --path skills/proteomics/proteomics-ms-qc

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-ms-qc --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/proteomics/proteomics-ms-qc .gemini/skills/proteomics-ms-qc && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "proteomics-ms-qc" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc into .gemini/skills/proteomics-ms-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-ms-qc", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-ms-qc

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/proteomics/proteomics-ms-qc .github/skills/proteomics-ms-qc && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "proteomics-ms-qc" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc into .github/skills/proteomics-ms-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-ms-qc", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-ms-qc --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/proteomics/proteomics-ms-qc .opencode/skills/proteomics-ms-qc && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "proteomics-ms-qc" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc into .opencode/skills/proteomics-ms-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-ms-qc", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: proteomics-ms-qc
GitHub stars: 161
Token cost: ~987 tokens
SKILL.md length: 370 words
Files: 8 (incl. references)
Skills in repo: 88
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV.

Tasks that involve Bioinformatics
SKILL.md covers When to use, Use from a step, API and Methods and parameters, plus 5 more sections
Runs Python scripts from its folder; calls python

What it does

Proteomics Ms Qc is an agent skill from TianGzlab/OmicsClaw. Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Skip when raw mzML / RAW spectra are the input (run a search engine first); peptide-level QC is needed (use proteomics-identification).

Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `proteomics_ms_qc.py`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper. The licence is Apache-2.0.

When your agent uses it

Tasks that involve Bioinformatics

Example prompts

“/proteomics-ms-qc”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 90a3bec. 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 (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

Proteomics Ms Qc loads about 987 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 83 tokens; SKILL.md has 370 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~83
When it runs · the whole SKILL.md, loaded when a task matches: ~987
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~1.2k

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 TianGzlab/OmicsClaw at commit 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 370 words, ~987 tokens.

Download SKILL.mdSave it as .claude/skills/proteomics-ms-qc/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.

name: proteomics-ms-qc
description: Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Skip when raw mzML / RAW spectra are the input (run a search engine first); peptide-level QC is needed (use proteomics-identification).
trigger: MS QC, mass spec QC, PTXQC, rawTools
tags: proteomics, qc, ms, maxquant, intensity, missing-values, cv

proteomics-ms-qc

When to use

Numeric columns excluding metadata-like names are samples; if none remain, all numeric columns are used. Zero and NaN count as missing. Use existing search-engine tables; this skill does not search raw spectra.

Use from a step

python

from skills._sdk.notebook import load_skill, write_output
library = load_skill('proteomics-ms-qc')
data = library.demo_data(random_state=42)
result = library.quality_control(data)
write_output(result, 'tables/qc_metrics.csv')

For real data, use read_input and pass any read_table helper as reader=. The executable examples/example_step.py also checks the result and writes a Figure.

API

<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->

`quality_control(data: pd.DataFrame) -> pd.DataFrame`

Return a one-row QC table without changing input intensities.

:param data: Protein rows and numeric intensity columns; metadata-like names are excluded when possible. :returns: Scalar QC metrics; per-sample completeness and selected columns are in run_info. :raises ValueError: There are no proteins or numeric intensity columns.

`run_info(table: pd.DataFrame, *, keep: bool=True) -> dict`

Read QC diagnostics.

:param table: Output of quality_control. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate dictionary with selected sample columns and completeness. :raises TypeError: The input is not a DataFrame.

`completeness_figure(table: pd.DataFrame)`

Plot detected protein percentages per sample.

:param table: QC table retaining run_info attributes. :returns: A matplotlib Figure without writing files. :raises KeyError: QC diagnostics are absent.

`demo_data(*, random_state: int=42) -> pd.DataFrame`

Generate a synthetic protein intensity table.

:param random_state: CLI seed 42; change for another simulation. :returns: One hundred proteins and five sample columns. :raises ValueError: The seed is invalid.

<!-- api:end -->

Show full SKILL.md (158 more words)Show less

Methods and parameters

Numeric columns excluding metadata-like names are samples; if none remain, all numeric columns are used. Zero and NaN count as missing. Functions return new DataFrames. run_info(result) reads diagnostic attrs; use keep=False before serialization when those attrs are not needed.

Gotchas

quality_control reports per-protein CV across positive intensities. run_info retains the actual sample columns and per-sample completeness.
demo_data uses seed 42, matching the CLI; every demo is synthetic.
run_info lives in DataFrame attrs and is not preserved by CSV serialization.

Inputs and outputs

The CLI reads CSV tables and writes:

tables/qc_metrics.csv
report.md
result.json
demo_proteomics.csv is written only with --demo.
reproducibility/commands.sh records the CLI invocation template.

Functions return data and Figures without writing files. Steps own their outputs. Demo mode also writes its synthetic input when the original CLI used a file.

CLI

bash

python skills/proteomics/proteomics-ms-qc/proteomics_ms_qc.py --demo --output /tmp/proteomics_ms_qc

For real input replace --demo with --input <table>.

Dependencies

numpy, pandas, matplotlib

Files

SKILL.md and 7 other files (references) in skills/proteomics/proteomics-ms-qc of TianGzlab/OmicsClaw.

SKILL.md
_api.py
examples/example_step.py
proteomics_ms_qc.py
references/methodology.md
references/output_contract.md
references/parameters.md
tests/test_api.py

Open the folder on GitHubat commit 90a3bec

Compare with similar skills

Proteomics Ms Qc next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Proteomics Ms Qc compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Proteomics Ms Qc this skillTianGzlab/OmicsClaw	161	—	~987	Automated safety check: Pass	Apache-2.0	2 days ago
Scanpy Single-Cell Analysisdavila7/claude-code-templates	32k	15 repos	~2.8k	Automated safety check: Pass	MIT	today
deepTools NGS Toolkitdavila7/claude-code-templates	32k	12 repos	~4.5k	Automated safety check: Pass	MIT	today
LaminDB Biological Data Managementdavila7/claude-code-templates	32k	12 repos	~3.6k	Automated safety check: Pass	MIT	today
PyDESeq2 Differential Expressiondavila7/claude-code-templates	32k	11 repos	~4k	Automated safety check: Pass	MIT	today
Gtars Genomic Interval Toolkitdavila7/claude-code-templates	32k	11 repos	~1.9k	Automated safety check: Pass	MIT	today

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Questions about Proteomics Ms Qc

What does Proteomics Ms Qc do?

Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Proteomics Ms Qc is an agent skill from TianGzlab/OmicsClaw. Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV.

When should I use Proteomics Ms Qc?

Proteomics Ms Qc fits situations like: tasks that involve Bioinformatics.

How do I install Proteomics Ms Qc in Claude Code?

Run `npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a claude-code`. Or copy the skill folder (skills/proteomics/proteomics-ms-qc in TianGzlab/OmicsClaw) into .claude/skills/proteomics-ms-qc in your project. Claude Code loads it when a task matches its description.

How do I install Proteomics Ms Qc in Codex?

Run `npx skills add TianGzlab/OmicsClaw --skill proteomics-ms-qc -a codex`. Or copy the skill folder (skills/proteomics/proteomics-ms-qc in TianGzlab/OmicsClaw) into .agents/skills/proteomics-ms-qc in your project. Codex loads it when a task matches its description.

Can I use Proteomics Ms Qc 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 TianGzlab/OmicsClaw --skill proteomics-ms-qc -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/proteomics-ms-qc, .gemini/skills/proteomics-ms-qc, .github/skills/proteomics-ms-qc and .opencode/skills/proteomics-ms-qc in your project.

What does Proteomics Ms Qc need to run?

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

Does Proteomics Ms Qc 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 Proteomics Ms Qc 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 Proteomics Ms Qc use?

Proteomics Ms Qc is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Proteomics Ms Qc use?

About 987 tokens (SKILL.md is roughly 3.9k 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 235 tokens, read only when the agent opens those files.

What are the alternatives to Proteomics Ms Qc?

Skills that share tags, products or a category with Proteomics Ms Qc: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 32k stars), deepTools NGS Toolkit (davila7/claude-code-templates, 32k stars), LaminDB Biological Data Management (davila7/claude-code-templates, 32k stars) and PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Proteomics Ms Qc?

TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on October 7, 2026.

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

Install Proteomics Ms Qc

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

proteomics-ms-qc

When to use

Use from a step

API

quality_control(data: pd.DataFrame) -> pd.DataFrame

run_info(table: pd.DataFrame, *, keep: bool=True) -> dict

completeness_figure(table: pd.DataFrame)

demo_data(*, random_state: int=42) -> pd.DataFrame

Methods and parameters

Gotchas

Inputs and outputs

CLI

See also

Dependencies

Files

Compare with similar skills

Similar skills

More from TianGzlab/OmicsClaw

Related

Questions about Proteomics Ms Qc

`quality_control(data: pd.DataFrame) -> pd.DataFrame`

`run_info(table: pd.DataFrame, *, keep: bool=True) -> dict`

`completeness_figure(table: pd.DataFrame)`

`demo_data(*, random_state: int=42) -> pd.DataFrame`