evo-proteomics-excel-formulas

evo-proteomics-excel-formulas is a skill for Claude Code, Codex from OpenLAIR/OpenSkill. It costs 60 tokens per session (998 once invoked), scanned A, original, Apache-2.0.

A Python utility for writing quantitative proteomics calculations into Excel workbooks. Quantitative proteomics measures protein levels, and the tool adds lookups, group averages, standard deviations, and log2 fold-change formulas while preserving formatting.

In plain words
What is it for?
Use it to populate protein lookups, calculate control and treated group statistics, and add fold-change formulas to a proteomics workbook.
Why use it?
It removes manual formula writing for common protein-expression comparisons and keeps the workbook's existing appearance. The formulas remain in Excel for calculation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to populate protein lookups, calculate control and treated group statistics, and add fold-change formulas to a proteomics workbook.

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Install with agentmods
npx agentmods add skills/openlair/openskill/evo-proteomics-excel-formulas
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add OpenLAIR/OpenSkill --skill evo-proteomics-excel-formulas
Clone the repo
git clone --depth 1 https://github.com/OpenLAIR/OpenSkill

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

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README.md
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Your own site
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<a href="https://agentmods.dev/skills/openlair/openskill/evo-proteomics-excel-formulas"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-proteomics-excel-formulas.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 998 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00060 $0.00998
Opus 5.5 $0.00024 $0.00399
Sonnet 5.5 $0.00012 $0.00200
Haiku 4.5 $0.00006 $0.00100

Measured 1mo ago against content hash cf8bf59e5f75, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

evo-proteomics-excel-formulas scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/utils.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

tasks-evolved/protein-expression-analysis/environment/skills/evo-proteomics-excel-formulas/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

evo-proteomics-excel-formulas

Utilities for populating Excel workbooks with proteomics Excel formulas (INDEX-MATCH lookups, AVERAGE, STDEV.S, fold-change) while preserving formatting.

Key Principles

  1. Write Excel FORMULAS, not computed values - The task requires formulas.
  2. Load with data_only=False to preserve existing formulas and formatting.
  3. _xlfn. prefix for STDEV.S: openpyxl requires _xlfn.STDEV.S(...) to avoid #NAME? errors.
  4. AVERAGE is a core OOXML function - no prefix needed.
  5. Data is log2-transformed: Log2FC = Treated_Mean - Control_Mean; FC = 2^Log2FC.
  6. Preserve formatting: Setting cell.value preserves existing fill, font, border.
  7. Use wb["SheetName"] not deprecated get_sheet_by_name().

Task Sheet Layout (protein_expression.xlsx)

  • Row 9: Group labels ("Control" / "Treated") in cols C-L
  • Row 10: Sample names in cols C-L (match Data sheet headers)
  • A11:A20: 10 target Protein_IDs; B11:B20: Gene_Symbols
  • C11:L20 (yellow): INDEX-MATCH formulas looking up from Data sheet
  • B24:K27 (yellow): Group statistics
    • Row 24 = Control Mean (AVERAGE), Row 25 = Control StdDev (STDEV.S)
    • Row 26 = Treated Mean (AVERAGE), Row 27 = Treated StdDev (STDEV.S)
    • Columns B-K map to proteins in rows 11-20
  • A32:B41: Protein IDs and Gene Symbols
  • C32:C41 (yellow): Fold Change = 2^(Log2FC)
  • D32:D41 (yellow): Log2 FC = Treated Mean - Control Mean

Data Sheet Layout

  • Row 1: Headers (Protein_ID, Gene_Symbol, Description, then 50 sample names in D1:BA1)
  • Rows 2-201: 200 proteins with log2 expression values in D2:BA201
  • Column A: Protein_IDs

Usage (one-shot)

import sys
sys.path.insert(0, '/app/environment/skills/evo-proteomics-excel-formulas/scripts')
from utils import populate_proteomics_task

populate_proteomics_task('/root/protein_expression.xlsx')

Usage (step-by-step)

import sys
sys.path.insert(0, '/app/environment/skills/evo-proteomics-excel-formulas/scripts')
from utils import (
    load_workbook_preserving,
    save_workbook_safely,
    classify_group_columns,
    fill_expression_lookup_formulas,
    fill_group_stats_formulas,
    fill_fold_change_formulas,
)

wb = load_workbook_preserving('/root/protein_expression.xlsx')
ws_task = wb['Task']

# Step 1: INDEX-MATCH lookups in C11:L20
fill_expression_lookup_formulas(ws_task, data_sheet_name='Data',
                                 data_max_row=201, data_max_col_letter='BA',
                                 data_start_col_letter='D')

# Step 2: Group stats in B24:K27
groups = classify_group_columns(ws_task)
fill_group_stats_formulas(ws_task, groups)

# Step 3: Fold change in A32:D41
fill_fold_change_formulas(ws_task)

save_workbook_safely(wb, '/root/protein_expression.xlsx')

Read the full file on GitHub · 109 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 109 lines · 60 tokens per session scan A cf8bf59e5f75

Subscribe to this mod's changes

evo-proteomics-excel-formulas is a skill published in the GitHub repository OpenLAIR/OpenSkill (97 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 998 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-11.

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