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Agent skill

Proteomics Ptm

by TianGzlab in TianGzlab/OmicsClaw

Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al.

Apache-2.0Auto-check passedResearch & Science

Install Proteomics Ptm

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

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

GitHub CLI
$ gh skill install TianGzlab/OmicsClaw proteomics-ptm --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-ptm .claude/skills/proteomics-ptm && 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-ptm
GitHub stars
161
Token cost
~989 tokens
SKILL.md length
360 words
Files
8 (incl. references)
Skills in repo
88
Repo updated
First seen
Licence
Apache-2.0

At a glance

Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al.

  • 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
  • Tasks that involve Internationalization

What it does

Proteomics Ptm is an agent skill from TianGzlab/OmicsClaw. Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by localizationprobability), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip when raw spectra are the input; you only need protein-level abundance (use proteomics-quantification).

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_ptm.py`).

It sits in Research & Science, covering Bioinformatics and Internationalization. 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
  • Tasks that involve Internationalization

Example prompts

  • “/proteomics-ptm”

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 Ptm loads about 989 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 94 tokens; SKILL.md has 360 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~989
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). 360 words, ~989 tokens.

Download SKILL.mdSave it as .claude/skills/proteomics-ptm/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
proteomics-ptm
description
Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by `localization_probability`), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip when raw spectra are the input; you only need protein-level abundance (use proteomics-quantification).
trigger
PTM, phosphorylation, acetylation, ubiquitination, modification, motif
tags
proteomics, ptm, phosphorylation, acetylation, ubiquitination, site-localization

proteomics-ptm

When to use

Class I starts at loc_threshold (default 0.75); Class II starts at 0.50. The PTM type is case-sensitive. 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-ptm')
data = library.demo_data(random_state=42)
result = library.classify_sites(data)
write_output(result, 'tables/ptm_sites.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 -->
classify_sites(data: pd.DataFrame, *, loc_threshold: float=0.75) -> pd.DataFrame

Return a new PTM table with localization confidence classes.

:param data: Site rows with protein and ptm_type; localization_probability is optional. :param loc_threshold: CLI default 0.75 for Class I; Class II starts at 0.50. :returns: Classified sites with summary diagnostics in attrs. :raises ValueError: Required columns are absent or the threshold is outside [0.5, 1].

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

Read PTM classification diagnostics.

:param table: Classified PTM sites. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate dictionary with localization threshold and summary. :raises TypeError: The input is not a DataFrame.

class_figure(table: pd.DataFrame)

Plot site counts by localization class.

:param table: Classified PTM sites containing site_class. :returns: A matplotlib Figure without writing files. :raises KeyError: site_class is absent.

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

Generate synthetic PTM sites in memory.

:param random_state: CLI seed 42; change for another simulation. :returns: Two hundred synthetic site records. :raises ValueError: The seed is invalid.

<!-- api:end -->
Show full SKILL.md (144 more words)Show less

Methods and parameters

Class I starts at loc_threshold (default 0.75); Class II starts at 0.50. The PTM type is case-sensitive. Functions return new DataFrames. run_info(result) reads diagnostic attrs; use keep=False before serialization when those attrs are not needed.

Gotchas

  • classify_sites returns Unknown when localization_probability is absent. tables/ptm_class_I_sites.csv can be empty.
  • 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/ptm_sites.csv
  • tables/ptm_class_I_sites.csv
  • report.md
  • result.json
  • demo_ptm_sites.csv is written only with --demo.

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-ptm/proteomics_ptm.py --demo --output /tmp/proteomics_ptm

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-ptm of TianGzlab/OmicsClaw.

  • SKILL.md
  • _api.py
  • examples/example_step.py
  • proteomics_ptm.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 Ptm 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 Ptm compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Proteomics Ptm this skillTianGzlab/OmicsClaw161—~989Automated safety check: PassApache-2.0
Spatial S5 DownstreamQING1105/ezST101—~513Automated safety check: PassMIT
Bio Proteomics Ptm AnalysisFreedomIntelligence/OpenClaw-Medical-Skills3.1k1 repos~1.2kAutomated safety check: PassNone
Bio Proteomics Peptide IdentificationGPTomics/bioSkills1.2k1 repos~5.3kAutomated safety check: PassMIT
Bio Proteomics Ptm AnalysisGPTomics/bioSkills1.2k1 repos~6.9kAutomated safety check: PassMIT
Scanpy Single-Cell Analysisdavila7/claude-code-templates32k15 repos~2.8kAutomated safety check: PassMIT

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Categories

Questions about Proteomics Ptm

What does Proteomics Ptm do?

Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Proteomics Ptm is an agent skill from TianGzlab/OmicsClaw.) from a per-site CSV — site-class assignment (Olsen et al.

When should I use Proteomics Ptm?

Proteomics Ptm fits situations like: tasks that involve Bioinformatics; tasks that involve Internationalization.

How do I install Proteomics Ptm in Claude Code?

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

How do I install Proteomics Ptm in Codex?

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

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

What does Proteomics Ptm need to run?

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

Does Proteomics Ptm 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 Ptm 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 Ptm use?

Proteomics Ptm 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 Ptm use?

About 989 tokens (SKILL.md is roughly 4k 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 203 tokens, read only when the agent opens those files.

What are the alternatives to Proteomics Ptm?

Skills that share tags, products or a category with Proteomics Ptm: Spatial S5 Downstream (QING1105/ezST, 101 stars), Bio Proteomics Ptm Analysis (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Proteomics Peptide Identification (GPTomics/bioSkills, 1.2k stars) and Bio Proteomics Ptm Analysis (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Proteomics Ptm?

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.