brenda-database

brenda-database is a skill for Claude Code, Codex from nevergoodstudy-hub/wechat-article-summarizer. It costs 43 tokens per session (5,705 once invoked), scanned A, a copy of brenda-database, MIT.

A connection to BRENDA, a scientific database of enzyme information gathered from research literature. It provides biochemical details such as reaction data, kinetic measurements, organisms, and conditions.

In plain words
What is it for?
It helps find Km and kcat measurements, reaction equations, substrate specificity, organism data, optimal conditions, inhibition data, and cofactor requirements.
Why use it?
It avoids searching many research papers and databases separately when investigating enzyme behavior or suitability.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/nevergoodstudy-hub/wechat-article-summarizer/brenda-database
Any agent
npx skills add nevergoodstudy-hub/wechat-article-summarizer --skill brenda-database
Clone the repo
git clone --depth 1 https://github.com/nevergoodstudy-hub/wechat-article-summarizer

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.

agentmods badge for brenda-database

README.md
[![agentmods](https://agentmods.dev/badge/skills/nevergoodstudy-hub/wechat-article-summarizer/brenda-database.svg)](https://agentmods.dev/skills/nevergoodstudy-hub/wechat-article-summarizer/brenda-database)
Your own site
<a href="https://agentmods.dev/skills/nevergoodstudy-hub/wechat-article-summarizer/brenda-database"><img src="https://agentmods.dev/badge/skills/nevergoodstudy-hub/wechat-article-summarizer/brenda-database.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,705 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 98% copy Near-identical to another mod 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 $0.00043 $0.05705
Opus 5 $0.00022 $0.02853
Sonnet 5 $0.00009 $0.01141
Haiku 4.5 $0.00004 $0.00571

Measured yesterday against content hash c39195731ece, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

brenda-database 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 yesterday.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/brenda_queries.py, scripts/brenda_visualization.py, scripts/enzyme_pathway_builder.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.

Origin

This is a copy

98% identical to brenda-database — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.warp/skills/brenda-database/SKILL.md · 718 lines

How it starts

The opening of the file, as written. The whole thing — 718 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BRENDA Database

Overview

BRENDA (BRaunschweig ENzyme DAtabase) is the world's most comprehensive enzyme information system, containing detailed enzyme data from scientific literature. Query kinetic parameters (Km, kcat), reaction equations, substrate specificities, organism information, and optimal conditions for enzymes using the official SOAP API. Access over 45,000 enzymes with millions of kinetic data points for biochemical research, metabolic engineering, and enzyme discovery.

When to Use This Skill

This skill should be used when:

  • Searching for enzyme kinetic parameters (Km, kcat, Vmax)
  • Retrieving reaction equations and stoichiometry
  • Finding enzymes for specific substrates or reactions
  • Comparing enzyme properties across different organisms
  • Investigating optimal pH, temperature, and conditions
  • Accessing enzyme inhibition and activation data
  • Supporting metabolic pathway reconstruction and retrosynthesis
  • Performing enzyme engineering and optimization studies
  • Analyzing substrate specificity and cofactor requirements

Core Capabilities

1. Kinetic Parameter Retrieval

Access comprehensive kinetic data for enzymes:

Get Km Values by EC Number:

from brenda_client import get_km_values

# Get Km values for all organisms
km_data = get_km_values("1.1.1.1")  # Alcohol dehydrogenase

# Get Km values for specific organism
km_data = get_km_values("1.1.1.1", organism="Saccharomyces cerevisiae")

# Get Km values for specific substrate
km_data = get_km_values("1.1.1.1", substrate="ethanol")

Parse Km Results:

for entry in km_data:
    print(f"Km: {entry}")
    # Example output: "organism*Homo sapiens#substrate*ethanol#kmValue*1.2#commentary*"

Extract Specific Information:

from scripts.brenda_queries import parse_km_entry, extract_organism_data

for entry in km_data:
    parsed = parse_km_entry(entry)
    organism = extract_organism_data(entry)
    print(f"Organism: {parsed['organism']}")
    print(f"Substrate: {parsed['substrate']}")
    print(f"Km value: {parsed['km_value']}")
    print(f"pH: {parsed.get('ph', 'N/A')}")
    print(f"Temperature: {parsed.get('temperature', 'N/A')}")

Read the full file on GitHub · 718 lines

Files

What ships with it

4 files 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. yesterday First seen · 718 lines · 43 tokens per session scan A c39195731ece

Subscribe to this mod's changes

brenda-database is a skill published in the GitHub repository nevergoodstudy-hub/wechat-article-summarizer (5 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 5,705 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to brenda-database, differing in 3 lines, and is treated as a copy.

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