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Agent skill

Proteomics Identification

by TianGzlab in TianGzlab/OmicsClaw

Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN.

Apache-2.0Auto-check passedResearch & Science

Install Proteomics Identification

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

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

GitHub CLI
$ gh skill install TianGzlab/OmicsClaw proteomics-identification --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-identification .claude/skills/proteomics-identification && 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
proteomics-identification
GitHub stars
161
Token cost
~1.1k tokens
SKILL.md length
431 words
Files
8 (incl. references)
Skills in repo
88
Repo updated
First seen
Licence
Apache-2.0

At a glance

Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN.

  • 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 Identification is an agent skill from TianGzlab/OmicsClaw. Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Skip when raw spectra are the input (run a search engine first); working with protein-quantification tables (use proteomics-ms-qc).

Its SKILL.md is about 1.1k 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_identification.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-identification”

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 Identification loads about 1.1k tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 431 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

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). 431 words, ~1,130 tokens.

Download SKILL.mdSave it as .claude/skills/proteomics-identification/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
proteomics-identification
description
Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Skip when raw spectra are the input (run a search engine first); working with protein-quantification tables (use proteomics-ms-qc).
trigger
peptide identification, database search, MaxQuant, MS-GF+, Comet, Mascot
tags
proteomics, identification, peptides, psm, maxquant, msgf

proteomics-identification

When to use

Confidence filtering uses qvalue, q-value, q_value, PEP, pep or fdr in that order. The default threshold is 0.01. 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-identification')
data = library.demo_data(random_state=42)
result = library.filter_identifications(data, n_spectra=1000)
write_output(result, 'tables/peptides.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 -->
read_table(path: str | Path) -> pd.DataFrame

Read peptide CSV/TSV; pass this function as reader= to read_input.

:param path: Peptide table; txt and tsv suffixes select tab separation. :returns: Table with common MaxQuant column names normalized. :raises OSError: The file cannot be read.

filter_identifications(data: pd.DataFrame, *, fdr_threshold: float=0.01, n_spectra: int | None=None) -> pd.DataFrame

Filter peptide confidence values and return a new table.

:param data: Existing peptide/protein rows with optional qvalue, q-value, q_value, PEP, pep or fdr. :param fdr_threshold: CLI default 0.01; PEP thresholding is not a global FDR estimate. :param n_spectra: Total spectra; CLI default None uses retained PSM count, not an observed identification rate. :returns: Filtered rows with the actual confidence column and summary in attrs. :raises ValueError: Threshold or spectrum count is invalid.

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

Read identification diagnostics.

:param table: Filtered peptide table. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate dictionary with filter provenance and summary. :raises TypeError: The input is not a DataFrame.

score_figure(table: pd.DataFrame)

Plot peptide identification scores.

:param table: Peptide table including score. :returns: A matplotlib Figure without writing files. :raises KeyError: score is absent.

Show full SKILL.md (189 more words)Show less
demo_data(*, random_state: int=42) -> pd.DataFrame

Simulate identifications for one thousand spectra.

:param random_state: CLI seed 42; change for another simulation. :returns: Synthetic peptide identifications, not a search-engine result. :raises ValueError: The seed is invalid.

<!-- api:end -->

Methods and parameters

Confidence filtering uses qvalue, q-value, q_value, PEP, pep or fdr in that order. The default threshold is 0.01. Functions return new DataFrames. run_info(result) reads diagnostic attrs; use keep=False before serialization when those attrs are not needed.

Gotchas

  • filter_identifications warns and records an unfiltered result without confidence columns. PEP thresholding is not global FDR control. run_info marks inferred spectrum totals.
  • 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/peptides.csv
  • report.md
  • result.json
  • 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-identification/proteomics_identification.py --demo --output /tmp/proteomics_identification

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

See also

  • references/methodology.md
  • references/parameters.md
  • references/output_contract.md
  • proteomics-data-import for protein-table normalization; proteomics-de for comparisons.

Dependencies

numpy, pandas, matplotlib

© TianGzlab, Apache-2.0. 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 7 other files (references) in skills/proteomics/proteomics-identification of TianGzlab/OmicsClaw.

  • SKILL.md
  • _api.py
  • examples/example_step.py
  • proteomics_identification.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 Identification 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 Identification compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Proteomics Identification this skillTianGzlab/OmicsClaw161—~1.1kAutomated safety check: PassApache-2.0
Scanpy Single-Cell Analysisdavila7/claude-code-templates32k16 repos~2.8kAutomated safety check: PassMIT
deepTools NGS Toolkitdavila7/claude-code-templates32k13 repos~4.5kAutomated safety check: PassMIT
PyDESeq2 Differential Expressiondavila7/claude-code-templates32k12 repos~4kAutomated safety check: PassMIT
Gtars Genomic Interval Toolkitdavila7/claude-code-templates32k12 repos~1.9kAutomated safety check: PassMIT
LaminDB Biological Data Managementdavila7/claude-code-templates32k12 repos~3.6kAutomated safety check: PassMIT

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Categories

Questions about Proteomics Identification

What does Proteomics Identification do?

Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Proteomics Identification is an agent skill from TianGzlab/OmicsClaw. Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN.

When should I use Proteomics Identification?

Proteomics Identification fits situations like: tasks that involve Bioinformatics.

How do I install Proteomics Identification in Claude Code?

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

How do I install Proteomics Identification in Codex?

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

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

What does Proteomics Identification need to run?

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

Does Proteomics Identification 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 Identification 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 Identification use?

Proteomics Identification 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 Identification use?

About 1.1k tokens (SKILL.md is roughly 4.5k 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 218 tokens, read only when the agent opens those files.

What are the alternatives to Proteomics Identification?

Skills that share tags, products or a category with Proteomics Identification: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 32k stars), deepTools NGS Toolkit (davila7/claude-code-templates, 32k stars), PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars) and Gtars Genomic Interval Toolkit (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 Identification?

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.