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Agent skill

Proteomics Structural

by TianGzlab in TianGzlab/OmicsClaw

Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO /…

MITAuto-check passedResearch & Science

Install Proteomics Structural

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

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

GitHub CLI
$ gh skill install TianGzlab/OmicsClaw proteomics-structural --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-structural .claude/skills/proteomics-structural && 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-structural
GitHub stars
161
Token cost
~1.3k tokens
SKILL.md length
398 words
Files
6 (incl. references)
Skills in repo
95
Repo updated
First seen
Licence
MIT

At a glance

Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO /…

  • Works in 5 steps: Load CSV (--input ) or generate a demo… → If fdr column present, filter to… → Derive link_type from protein_a ==… → …
  • 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 Structural is an agent skill from TianGzlab/OmicsClaw. Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO / DSBU) max distance. Skip when raw spectra are the input (run XlinkX / pLink / xiSEARCH first); no XL-MS experiment was performed.

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 `references/methodology.md`, `references/output_contract.md` and `references/parameters.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-structural”

Requirements

  • Python 3

Workflow steps

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

  1. Load CSV (--input ) or generate a demo at output_dir/demo_crosslinks.csv (struct_proteomics.py:102).
  2. If fdr column present, filter to df[df["fdr"] <= --fdr] (struct_proteomics.py:126); otherwise pass-through (:130).
  3. Derive link_type from protein_a == protein_b comparison when both columns are present (struct_proteomics.py:134-141); otherwise count all…
  4. If distance_angstrom column present, compute satisfaction rate vs CROSSLINKER_CONSTRAINTS[--crosslinker] (struct_proteomics.py:147-167)…
  5. Write tables/crosslinks.csv (struct_proteomics.py:282) + tables/inter_protein_crosslinks.csv (only if non-empty, :287) + report.md +…

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 Structural 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 87 tokens; SKILL.md has 398 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~87
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). 398 words, ~1,303 tokens.

Download SKILL.mdSave it as .claude/skills/proteomics-structural/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
proteomics-structural
description
Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO / DSBU) max distance. Skip when raw spectra are the input (run XlinkX / pLink / xiSEARCH first); no XL-MS experiment was performed.
version
0.5.0
author
OmicsClaw
license
MIT
emoji
🏗️
tags
proteomics, structural, xl-ms, crosslinking, dss, bs3, dsso, dsbu, edc
requires
numpy, pandas

proteomics-structural

When to use

The user has a cross-linking MS (XL-MS) results CSV (from XlinkX, pLink, xiSEARCH, etc.) and wants a summary: intra- vs inter-protein classification, optional FDR filtering, and distance-constraint validation against the per-crosslinker max distance (Rappsilber (2011) Cα-Cα bounds).

--crosslinker {DSS,BS3,EDC,DSSO,DSBU} (default DSS) sets the max-distance threshold (CROSSLINKER_CONSTRAINTS at struct_proteomics.py:43-49: DSS/BS3/DSSO/DSBU = 30 Å, EDC = 20 Å). --fdr (default 0.05) filters by the fdr column when present.

This skill does NOT run an XL-MS search engine — feed it the already-searched results.

Inputs & Outputs

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

Inputs

  • File types: .csv

Outputs

  • tables/crosslinks.csv
  • tables/inter_protein_crosslinks.csv
  • report.md
  • result.json

Flow

  1. Load CSV (--input <crosslinks.csv>) or generate a demo at output_dir/demo_crosslinks.csv (struct_proteomics.py:102).
  2. If fdr column present, filter to df[df["fdr"] <= --fdr] (struct_proteomics.py:126); otherwise pass-through (:130).
  3. Derive link_type from protein_a == protein_b comparison when both columns are present (struct_proteomics.py:134-141); otherwise count all rows as intra (:142-145).
  4. If distance_angstrom column present, compute satisfaction rate vs CROSSLINKER_CONSTRAINTS[--crosslinker] (struct_proteomics.py:147-167); add per-row constraint_satisfied boolean column.
  5. Write tables/crosslinks.csv (struct_proteomics.py:282) + tables/inter_protein_crosslinks.csv (only if non-empty, :287) + report.md + result.json (:290).

Gotchas

  • Required input columns are protein_a and protein_b (lowercase, with underscore-letter — NOT protein1 / protein2). struct_proteomics.py:134 checks {"protein_a", "protein_b"}.issubset(df_filtered.columns). Without both, ALL rows silently classify as intra-protein (:142-145) — n_inter = 0 even on a real inter-protein dataset. XlinkX exports use Protein A / Protein B; rename first.
  • --crosslinker drives the distance-constraint check, NOT just metadata. struct_proteomics.py:148 sets max_distance = CROSSLINKER_CONSTRAINTS.get(crosslinker.upper(), 30.0) — the active threshold for constraint_satisfied column + constraint_satisfaction_rate summary. Choices: DSS / BS3 / DSSO / DSBU = 30 Å, EDC = 20 Å (Rappsilber 2011 Cα-Cα bounds).
  • Distance check is OPT-IN by distance_angstrom column presence. Without that column, constraint_satisfaction_rate defaults to 100% (struct_proteomics.py:170) — the constraint feature is silently skipped, not failed. Pass distance_angstrom (Cα-Cα predicted distance from a 3D model) for a real check.
  • fdr filter is OPT-IN by column presence. struct_proteomics.py:126 only filters when fdr exists — without that column, EVERY input row is kept regardless of --fdr. Pre-add an fdr column (or a placeholder of zeros) if you need the filter to bite.
  • --input REQUIRED unless --demo. struct_proteomics.py:270 raises ValueError("--input required when not using --demo").
  • tables/inter_protein_crosslinks.csv only appears when there ARE inter-protein links. A purely-intra dataset writes only tables/crosslinks.csv. Downstream consumers should check file existence.
Show full SKILL.md (43 more words)Show less

Key CLI

bash
# Demo
python omicsclaw.py run proteomics-structural --demo --output /tmp/xl_demo

# Real XL-MS data, default DSS / 5% FDR
python omicsclaw.py run proteomics-structural \
  --input crosslinks.csv --output results/

# DSBU at 1% FDR
python omicsclaw.py run proteomics-structural \
  --input crosslinks.csv --output results/ \
  --crosslinker DSBU --fdr 0.01

# EDC (zero-length, 20 Å threshold)
python omicsclaw.py run proteomics-structural \
  --input crosslinks.csv --output results/ \
  --crosslinker EDC --fdr 0.05

See also

  • references/parameters.md — every CLI flag
  • references/methodology.md — XL-MS workflow, Rappsilber Cα-Cα bounds, FDR caveats
  • references/output_contract.md — tables/crosslinks.csv schema, derived columns
  • Adjacent skills: proteomics-data-import (parallel — peptide / protein-level workflows), proteomics-ptm (parallel — PTM analysis), proteomics-quantification (parallel — protein abundance), proteomics-enrichment (downstream — pathway enrichment on inter-protein partners)

© TianGzlab, 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 5 other files (references) in skills/proteomics/proteomics-structural of TianGzlab/OmicsClaw.

  • SKILL.md
  • references/methodology.md
  • references/output_contract.md
  • references/parameters.md
  • skill.yaml
  • struct_proteomics.py

Open the folder on GitHubat commit 6fbd79f

Compare with similar skills

Proteomics Structural 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 Structural compared with similar skills
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Proteomics Structural this skillTianGzlab/OmicsClaw161—~1.3kAutomated safety check: PassMIT
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Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

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Categories

Questions about Proteomics Structural

What does Proteomics Structural do?

Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO /…. Proteomics Structural is an agent skill from TianGzlab/OmicsClaw. Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO / DSBU) max distance.

When should I use Proteomics Structural?

Proteomics Structural fits situations like: tasks that involve Bioinformatics.

How do I install Proteomics Structural in Claude Code?

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

How do I install Proteomics Structural in Codex?

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

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

What does Proteomics Structural need to run?

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

Does Proteomics Structural 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 Structural 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 Structural use?

Proteomics Structural 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 Structural 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 482 tokens, read only when the agent opens those files.

What are the alternatives to Proteomics Structural?

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

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.