Skills auto-proteomics

Public OpenClaw skill for low-token routing and downstream analysis of processed DDA LFQ proteomics inputs. Use when the user already has protein-level quantification tables such as MaxQuant-style `proteinGroups.txt` and needs a clear two-group downstream workflow.

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/billwanttobetop/auto-proteomics" ~/.claude/skills/openclaw-skills-auto-proteomics && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/billwanttobetop/auto-proteomics" ~/.openclaw/skills/openclaw-skills-auto-proteomics && rm -rf "$T"
manifest: skills/billwanttobetop/auto-proteomics/SKILL.md
source content

Auto Proteomics

Author: Guo Xuan 郭轩
Contact: xguo608@connect.hkust-gz.edu.cn

auto-proteomics
is a public
v0.x
skill for processed proteomics downstream work.

The current public promise is intentionally narrow:

  • one shipped runnable workflow:
    dda-lfq-processed
  • one public input family: processed DDA LFQ protein-level tables
  • one public comparison model:
    group-a
    vs
    group-b

Everything else in this repository should be read as routing context, internal prototype, or future scaffold unless a document explicitly marks it as part of the public promise. Presence of a script, schema, or branch document does not mean the route is publicly supported. In particular,

dia-quant
is intentionally exposed as an internal prototype route for correct routing and contract validation, not as a shipped public workflow.

Use this skill when

  • the user already has processed protein-level quantification output
  • the main table is MaxQuant-like
    proteinGroups.txt
  • the goal is QC, normalized matrix generation, and two-group differential protein analysis
  • the user wants a low-token, file-driven workflow instead of a long chat-only protocol

Do not use this skill when

  • the user starts from raw spectra and needs search/identification
  • the request is primarily DIA, phosphoproteomics, enrichment, or multi-omics execution
  • the task requires more than one comparison design in the current release
  • the user only wants generic statistics with no proteomics context

Public promise in
v0.x

Shipped and supported now:

  • route processed DDA LFQ downstream requests into
    dda-lfq-processed
  • validate the expected processed-input shape
  • generate matrix, QC, differential tables, report, and manifest outputs

Not promised yet:

  • raw-spectrum search pipelines
  • DIA public execution support
  • phosphoproteomics execution
  • enrichment execution
  • multi-omics execution
  • generalized study-design handling beyond the current two-group path

Internal prototype route available for routing only:

  • dia-quant
    may be selected only when the request is explicitly about processed DIA quant tables that fit the checked-in DIA contract
  • selecting
    dia-quant
    means internal prototype triage, never a public
    v0.x
    execution recommendation

Important boundary:

  • non-shipped branches may contain scaffold or prototype execution files for internal framework development
  • smaller models must not treat those files as public runnable recommendations unless a route is explicitly marked
    shipped

Minimal workflow

  1. Read
    references/WORKFLOW_INDEX.yaml
  2. If the route is unclear, run
    scripts/decision/route_proteomics.py
  3. Check that the request fits the public
    v0.x
    boundary
  4. Run
    scripts/workflows/dda_lfq_processed.sh
  5. Use
    references/
    for runtime, onboarding, and development rules

Public runnable entrypoint

bash scripts/workflows/dda_lfq_processed.sh \
  --input-dir <run_dir> \
  --protein-groups <proteinGroups.txt> \
  --summary <summary.txt> \
  --parameters <parameters.txt> \
  --output-dir <output_dir> \
  --group-a <condition_a> \
  --group-b <condition_b>

Input contract

Required:

  • proteinGroups.txt
    with
    LFQ intensity *
    or
    Intensity *
    columns
  • summary.txt
    with
    Raw file
    and
    Experiment
    columns

Optional:

  • parameters.txt

Output contract

The shipped workflow produces:

  • normalized protein matrix files under
    matrix/
  • QC outputs under
    qc/
  • differential protein tables under
    stats/
  • REPORT.md
  • summary.json
  • run_manifest.json

Repository layers

  • SKILL.md
    : public entry and release boundary
  • references/WORKFLOW_INDEX.yaml
    : machine-readable routing and shipped-vs-non-shipped map
  • references/BRANCH_FRAMEWORK.md
    : standard branch contract for future routes
  • references/branches/
    : per-branch specs for scaffold and prototype workflows
  • references/DIA_INPUT_SCHEMA.md
    : first narrow schema for DIA prototype intake
  • scripts/workflows/dda_lfq_processed.sh
    : shipped workflow entrypoint
  • shipped public guidance lives in documents that explicitly describe the processed DDA
    v0.x
    path
  • non-shipped reference docs exist for internal framework development and must not be surfaced as public support

Read next

  • references/WORKFLOW_INDEX.yaml
  • references/RUNTIME_REQUIREMENTS.md
  • references/BRANCH_FRAMEWORK.md
  • references/DEMO_INPUT_GUIDE.md
  • references/DEVELOPMENT_GUIDE.md