Agent skill

Proteomics Quantification

Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein).

MITAuto-check passedResearch & Science

Install Proteomics Quantification

skills CLI

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

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-quantification --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-quantification .claude/skills/proteomics-quantification && 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-quantification" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-quantification into .claude/skills/proteomics-quantification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-quantification", 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-quantification

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-quantification -a codex

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-quantification --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-quantification .agents/skills/proteomics-quantification && 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-quantification" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-quantification into .agents/skills/proteomics-quantification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-quantification", 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-quantification -a cursor

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-quantification --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-quantification .cursor/skills/proteomics-quantification && 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-quantification" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-quantification into .cursor/skills/proteomics-quantification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-quantification", 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-quantification

--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-quantification -a gemini-cli

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

GitHub CLI

$ gh skill install TianGzlab/OmicsClaw proteomics-quantification --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-quantification .gemini/skills/proteomics-quantification && 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-quantification" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-quantification into .gemini/skills/proteomics-quantification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-quantification", 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-quantification

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-quantification -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-quantification .github/skills/proteomics-quantification && 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-quantification" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-quantification into .github/skills/proteomics-quantification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-quantification", 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-quantification -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-quantification --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-quantification .opencode/skills/proteomics-quantification && 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-quantification" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-quantification into .opencode/skills/proteomics-quantification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "proteomics-quantification", 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-quantification
GitHub stars: 161
Token cost: ~1.3k tokens
SKILL.md length: 384 words
Files: 6 (incl. references)
Skills in repo: 95
Repo updated: Jul 28, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein).

Works in 4 steps: Load CSV (--input ) or generate a demo… → Dispatch on --method… → Aggregate per protein → …
Tasks that involve Bioinformatics
SKILL.md covers When to use, Inputs & Outputs, Flow and Gotchas, plus 2 more sections
Runs Python scripts from its folder; calls python

What it does

Proteomics Quantification is an agent skill from TianGzlab/OmicsClaw. Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use proteomics-ms-qc); label-based TMT / iTRAQ workflows (search upstream first).

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `proteomics_quantification.py`, `references/methodology.md` and `references/output_contract.md`).

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 MIT.

When your agent uses it

Tasks that involve Bioinformatics

Example prompts

“/proteomics-quantification”

Requirements

Python 3

Workflow steps

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

Load CSV (--input ) or generate a demo (--demo).
Dispatch on --method (proteomics_quantification.py:156); validate required columns per method.
Aggregate per protein
Write tables/protein_abundance.csv (proteomics_quantification.py:277) + report.md + result.json (:283).

What it can do on your machine

Read from SKILL.md and the folder at commit 6fbd79f. 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 Quantification loads about 1.3k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 384 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~84
When it runs · the whole SKILL.md, loaded when a task matches: ~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~1.8k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from TianGzlab/OmicsClaw at commit 6fbd79f, republished under its MIT licence (© TianGzlab). 384 words, ~1,309 tokens.

Download SKILL.mdSave it as .claude/skills/proteomics-quantification/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: proteomics-quantification
description: Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use proteomics-ms-qc); label-based TMT / iTRAQ workflows (search upstream first).
version: 0.5.0
author: OmicsClaw
license: MIT
emoji: 📏
tags: proteomics, quantification, lfq, ibaq, spectral-counting
requires: numpy, pandas

proteomics-quantification

When to use

The user has a peptide / PSM table and wants protein-level abundance via one of:

lfq (default) — Label-Free Quantification by intensity summation. Requires an intensity column.
ibaq — intensity-Based Absolute Quantification (intensity / theoretical tryptic peptide count). Requires an intensity column AND ONE OF: a per-protein sequence column (in-silico digested by the script) OR a pre-computed n_theoretical_peptides integer column. Without either, the script silently estimates unique_peptides × 1.5.
spectral_count — PSM count per protein (no intensity needed).

Pick with --method {lfq,spectral_count,ibaq} (default lfq). For TMT / iTRAQ label-based workflows, perform the search-engine quant first; this skill is intensity- / count-only.

Inputs & Outputs

<!-- AUTO-GENERATED from skill.yaml (interface) — do not edit by hand. Regenerate: python scripts/generate_skill_md.py <skill_dir> -->

Inputs

Modalities: lfq
File types: .csv
Accepts artifact proteomics.peptide_table (csv)

Outputs

tables/protein_abundance.csv
report.md
result.json
Produces artifact proteomics.abundance_matrix as tables/protein_abundance.csv (csv)

Flow

Load CSV (--input <peptides.csv>) or generate a demo (--demo).
Dispatch on --method (proteomics_quantification.py:156); validate required columns per method.
Aggregate per protein:
- lfq: sum intensity per protein.
- ibaq: sum intensity per protein, divide by n_theoretical_peptides. Source order at proteomics_quantification.py:115-130: sequence (compute on the fly) → n_theoretical_peptides (use as-is) → unique_peptides × 1.5 (silent estimate with warning).
- spectral_count: count PSMs per protein.
Write tables/protein_abundance.csv (proteomics_quantification.py:277) + report.md + result.json (:283).

Show full SKILL.md (197 more words)Show less

Gotchas

lfq and ibaq require an intensity column; method enforces this. proteomics_quantification.py:77 raises ValueError("Input requires an 'intensity' column for LFQ"); :109 raises the same for iBAQ. spectral_count only needs row counts (no intensity).
ibaq requires either sequence OR n_theoretical_peptides; otherwise it SILENTLY ESTIMATES. proteomics_quantification.py:115-130 checks for sequence first (in-silico digest at :42-72, K/R not before P, length 7-30), then n_theoretical_peptides, otherwise falls back to unique_peptides × 1.5 with only a logger warning. The wrong column name (theoretical_peptides instead of n_theoretical_peptides) silently triggers the estimate path — always pass one of the two correct columns.
Unknown --method raises ValueError. proteomics_quantification.py:156 rejects values outside ("lfq", "spectral_count", "ibaq"). The argparse choices= already enforces this — the :156 raise is defence-in-depth for direct library calls.
--input REQUIRED unless --demo. proteomics_quantification.py:269 raises ValueError("--input required").
Missing intensities in lfq are summed as 0. pd.Series.sum(skipna=True) is the default — proteins with all-NaN intensities yield 0, indistinguishable from "all detected as zero". Pre-filter or impute upstream if NaN-vs-zero matters.

Key CLI

bash

# Demo (LFQ default)
python omicsclaw.py run proteomics-quantification --demo --output /tmp/quant_demo

# LFQ on real peptides
python omicsclaw.py run proteomics-quantification \
  --input peptides.csv --output results/ --method lfq

# iBAQ via per-protein sequence (in-silico digest)
python omicsclaw.py run proteomics-quantification \
  --input peptides_with_sequence.csv --output results/ --method ibaq

# iBAQ via pre-computed n_theoretical_peptides
python omicsclaw.py run proteomics-quantification \
  --input peptides_with_n_theo.csv --output results/ --method ibaq

# Spectral counting
python omicsclaw.py run proteomics-quantification \
  --input psms.csv --output results/ --method spectral_count

Files

SKILL.md and 5 other files (references) in skills/proteomics/proteomics-quantification of TianGzlab/OmicsClaw.

SKILL.md
proteomics_quantification.py
references/methodology.md
references/output_contract.md
references/parameters.md
skill.yaml

Open the folder on GitHubat commit 6fbd79f

Compare with similar skills

Proteomics Quantification 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 Quantification compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Proteomics Quantification this skillTianGzlab/OmicsClaw	161	—	~1.3k	Automated safety check: Pass	MIT	2 mo ago
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills	3.2k	2 repos	~3k	Automated safety check: Notes	Apache-2.0	22 days ago
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills	48k	1 repos	~3.2k	Automated safety check: Pass	MIT	2 days ago
Clinvar Databasegoogle-deepmind/science-skills	3.2k	2 repos	~3.9k	Automated safety check: Notes	Apache-2.0	22 days ago
Metabolic Study Planneraiming-lab/AutoResearchClaw	15k	—	~1.9k	Automated safety check: Pass	MIT	1 mo ago
Dbsnp Databasegoogle-deepmind/science-skills	3.2k	2 repos	~3.4k	Automated safety check: Notes	Apache-2.0	22 days ago

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Questions about Proteomics Quantification

What does Proteomics Quantification do?

Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Proteomics Quantification is an agent skill from TianGzlab/OmicsClaw. Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein).

When should I use Proteomics Quantification?

Proteomics Quantification fits situations like: tasks that involve Bioinformatics.

How do I install Proteomics Quantification in Claude Code?

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

How do I install Proteomics Quantification in Codex?

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

Can I use Proteomics Quantification 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-quantification -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-quantification, .gemini/skills/proteomics-quantification, .github/skills/proteomics-quantification and .opencode/skills/proteomics-quantification in your project.

What does Proteomics Quantification need to run?

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

Does Proteomics Quantification 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 Quantification 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 Quantification use?

Proteomics Quantification 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 Proteomics Quantification use?

About 1.3k tokens (SKILL.md is roughly 5.2k 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 461 tokens, read only when the agent opens those files.

What are the alternatives to Proteomics Quantification?

Skills that share tags, products or a category with Proteomics Quantification: 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 Proteomics Quantification?

TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 95 skills in this directory. The repository was last updated on July 28, 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 Quantification

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

proteomics-quantification

When to use

Inputs & Outputs

Flow

Gotchas

Key CLI

See also

Files

Compare with similar skills

Similar skills

More from TianGzlab/OmicsClaw

Related

Questions about Proteomics Quantification