brenda-database

brenda-database is a skill for Claude Code, Codex from Zaoqu-Liu/ScienceClaw. It costs 43 tokens per session (5,865 once invoked), scanned A, a copy of brenda-database, MIT.

A connection to BRENDA, a database of enzyme information collected from scientific literature.

In plain words
What is it for?
Find values such as Km and kcat, reaction equations, substrates, organisms, optimal conditions, inhibition data, and pathway information.
Why use it?
It provides enzyme measurements and reaction details that would otherwise need to be gathered manually from research sources.

Skill for Claude CodeCodex

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

Good fit Find values such as Km and kcat, reaction equations, substrates, organisms, optimal conditions, inhibition data, and pathway information.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zaoqu-liu/scienceclaw/brenda-database
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 Zaoqu-Liu/ScienceClaw --skill brenda-database
Clone the repo
git clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClaw

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/zaoqu-liu/scienceclaw/brenda-database.svg)](https://agentmods.dev/skills/zaoqu-liu/scienceclaw/brenda-database)
Your own site
<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/brenda-database"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/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 5,865 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 89% 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 5.1 $0.00043 $0.05865
Opus 5 $0.00022 $0.02933
Sonnet 5 $0.00009 $0.01173
Haiku 4.5 $0.00004 $0.00587

Measured 8d ago against content hash 65bce5d9af87, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, 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 8d ago.

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

89% identical to brenda-database — 22 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.

skills/brenda-database/SKILL.md · 719 lines

How it starts

The opening of the file, as written. The whole thing — 719 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 · 719 lines

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. 8d ago First seen · 719 lines · 43 tokens per session scan A 65bce5d9af87

Subscribe to this mod's changes

brenda-database is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 5,865 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to brenda-database, differing in 22 lines, and is treated as a copy.

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