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Agent skill

Brenda Database

by aipoch in aipoch/medical-research-skills

Programmatic access to the BRENDA enzyme database via the SOAP API; use when you need kinetic constants (Km, kcat, Vmax), reaction equations, enzyme properties (pH/temperature optima, stability), or…

MITAuto-check: notesResearch & Science

Install Brenda Database

skills CLI
$ npx skills add aipoch/medical-research-skills --skill brenda-database -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills brenda-database --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/brenda-database' .claude/skills/brenda-database && 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
brenda-database
GitHub stars
2k
Token cost
~1.6k tokens
SKILL.md length
647 words
Files
4 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Programmatic access to the BRENDA enzyme database via the SOAP API; use when you need kinetic constants (Km, kcat, Vmax), reaction equations, enzyme properties (pH/temperature optima, stability), or…

  • Works in 3 steps: Set credentials (either in your shell or… → Run the query script → Minimal Python example (calling the…
  • You need kinetic constants (Km
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 8 more sections
  • Runs Python scripts from its folder; calls python and uv; needs BRENDA_PASSWORD

What it does

Brenda Database is an agent skill from aipoch/medical-research-skills. Programmatic access to the BRENDA enzyme database via the SOAP API; use when you need kinetic constants (Km, kcat, Vmax), reaction equations, enzyme properties (pH/temperature optima, stability), or enzyme discovery by EC/substrate/product.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `brenda-database_audit_result_v2.json`, `references/api_reference.md` and `scripts/brenda_queries.py`).

It sits in Research & Science. It works with Python. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need kinetic constants (Km
  • Reaction equations
  • Enzyme properties (pH/temperature optima
  • Enzyme discovery by EC/substrate/product

Example prompts

  • “/brenda-database”

Requirements

  • Python 3

Workflow steps

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

  1. Set credentials (either in your shell or a .env file loaded by your environment)
  2. Run the query script
  3. Minimal Python example (calling the script functions; adjust function names to match scripts/brenda_queries.py)

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • BRENDA_PASSWORD

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Brenda Database loads about 1.6k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 647 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:26
    tication via environment variables or a `.env` file.
  • NoteMentions a .env fileSKILL.md:42
    credentials (either in your shell or a `.env` file loaded by your environment):
  • NoteMentions a .env fileSKILL.md:83
    re read from environment variables or a `.env`-backed environment setup.

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); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 647 words, ~1,551 tokens.

Download SKILL.mdSave it as .claude/skills/brenda-database/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
brenda-database
description
Programmatic access to the BRENDA enzyme database via the SOAP API; use when you need kinetic constants (Km, kcat, Vmax), reaction equations, enzyme properties (pH/temperature optima, stability), or enzyme discovery by EC/substrate/product.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You need kinetic parameters (e.g., Km, kcat, Vmax) for a specific enzyme, organism, or EC number.
  • You want reaction equations/stoichiometry associated with an enzyme (by EC number or enzyme name).
  • You need enzyme property data such as optimal pH/temperature, stability, inhibitors, or activators.
  • You want to discover enzymes by searching for a substrate, product, or EC number.
  • You need to automate retrieval of BRENDA data in a Python pipeline (e.g., for modeling, annotation, or curation).

Key Features

  • SOAP-based programmatic access to the BRENDA database.
  • Retrieval of:
    • Kinetic data: Km, kcat, Vmax
    • Reaction information: reaction equations and related metadata
    • Enzyme discovery: search by substrate/product/EC number
    • Enzyme properties: pH/temperature optima, stability, inhibitors/activators
  • Built-in handling of BRENDA SOAP responses that are returned as complex delimited strings, with parsing performed by the provided script.
  • Credential-based authentication via environment variables or a .env file.

Dependencies

  • Python 3.x
  • zeep (SOAP client)
  • requests

Install (example):

bash
uv pip install zeep requests

Example Usage

  1. Set credentials (either in your shell or a .env file loaded by your environment):
bash
export BRENDA_EMAIL="your_email@example.com"
export BRENDA_PASSWORD="your_password"
  1. Run the query script:
bash
python scripts/brenda_queries.py
  1. Minimal Python example (calling the script functions; adjust function names to match scripts/brenda_queries.py):
python
import os
from scripts.brenda_queries import BrendaClient

email = os.environ["BRENDA_EMAIL"]
password = os.environ["BRENDA_PASSWORD"]

client = BrendaClient(email=email, password=password)

# Example: retrieve Km values for an EC number
km_records = client.get_km_values(ec_number="1.1.1.1")
for r in km_records:
    print(r)

# Example: retrieve reactions for an EC number
reactions = client.get_reactions(ec_number="1.1.1.1")
for rxn in reactions:
    print(rxn)

For a complete list of available API methods and parameters, see: references/api_reference.md.

Implementation Details

  • Authentication / Connection

    • The skill initializes a SOAP client (via zeep) using BRENDA credentials (email/password).
    • Credentials are read from environment variables or a .env-backed environment setup.

  • Query Execution

    • The script calls SOAP methods such as get_km_values and get_reactions (and other supported endpoints for properties and discovery).

  • Response Parsing

    • BRENDA SOAP responses are often returned as single strings containing multiple records and fields, using delimiters (e.g., patterns like organism*E. coli#value*...).
    • scripts/brenda_queries.py is responsible for:
      • Splitting records into entries
      • Extracting key/value fields
      • Normalizing parsed results into Python-friendly structures (e.g., dicts/lists)

  • Output Structure

    • Parsed results are returned as structured Python objects suitable for downstream filtering (by organism, literature reference, conditions, etc.), depending on the endpoint and available fields.

When Not to Use

  • Do not use this skill when the required source data, identifiers, files, or credentials are missing.
  • Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
  • Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

  • A clearly specified task goal aligned with the documented scope.
  • All required files, identifiers, parameters, or environment variables before execution.
  • Any domain constraints, formatting requirements, and expected output destination if applicable.
Show full SKILL.md (243 more words)Show less
  1. Validate the request against the skill boundary and confirm all required inputs are present.
  2. Select the documented execution path and prefer the simplest supported command or procedure.
  3. Produce the expected output using the documented file format, schema, or narrative structure.
  4. Run a final validation pass for completeness, consistency, and safety before returning the result.

Output Contract

  • Return a structured deliverable that is directly usable without reformatting.
  • If a file is produced, prefer a deterministic output name such as brenda_database_result.md unless the skill documentation defines a better convention.
  • Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

  • Validate required inputs before execution and stop early when mandatory fields or files are missing.
  • Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
  • Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
  • Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

  • If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
  • If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
  • If partial output is returned, label it clearly and identify which checks could not be completed.

Quick Validation

Run this minimal verification path before full execution when possible:

bash
python scripts/brenda_queries.py --help

Expected output format:

text
Result file: brenda_database_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any

© aipoch, 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 3 other files (scripts, references) in scientific-skills/Evidence Insight/brenda-database of aipoch/medical-research-skills.

  • SKILL.md
  • brenda-database_audit_result_v2.json
  • references/api_reference.md
  • scripts/brenda_queries.py

Open the folder on GitHubat commit 686e09d

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Works with

Categories

Questions about Brenda Database

What does Brenda Database do?

Programmatic access to the BRENDA enzyme database via the SOAP API; use when you need kinetic constants (Km, kcat, Vmax), reaction equations, enzyme properties (pH/temperature optima, stability), or…. Brenda Database is an agent skill from aipoch/medical-research-skills. Programmatic access to the BRENDA enzyme database via the SOAP API; use when you need kinetic constants (Km, kcat, Vmax), reaction equations, enzyme properties (pH/temperature optima, stability), or enzyme discovery by EC/substrate/product.

When should I use Brenda Database?

Brenda Database fits situations like: you need kinetic constants (Km; reaction equations; enzyme properties (pH/temperature optima; enzyme discovery by EC/substrate/product.

How do I install Brenda Database in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill brenda-database -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/brenda-database in aipoch/medical-research-skills) into .claude/skills/brenda-database in your project. Claude Code loads it when a task matches its description.

How do I install Brenda Database in Codex?

Run `npx skills add aipoch/medical-research-skills --skill brenda-database -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/brenda-database in aipoch/medical-research-skills) into .agents/skills/brenda-database in your project. Codex loads it when a task matches its description.

Can I use Brenda Database 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 aipoch/medical-research-skills --skill brenda-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/brenda-database, .gemini/skills/brenda-database, .github/skills/brenda-database and .opencode/skills/brenda-database in your project.

What does Brenda Database need to run?

Going by SKILL.md and its folder, Brenda Database needs Python for the scripts in its folder, the command-line tools its instructions call (python and uv) and credentials named BRENDA_PASSWORD. Our summary lists: Python 3.

Does Brenda Database access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Brenda Database safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Brenda Database use?

Brenda Database 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 Brenda Database use?

About 1.6k tokens (SKILL.md is roughly 6.2k 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 224 tokens, read only when the agent opens those files.

What are the alternatives to Brenda Database?

Skills that share tags, products or a category with Brenda Database: GitHub Deep Research (bytedance/deer-flow, 83k stars), Last30days (mvanhorn/last30days-skill, 64k stars), Networkx (zLanqing/codex-claude-academic-skills, 4.6k stars) and Nature-Style Scientific Figures (Yuan1z0825/nature-skills, 46k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Brenda Database?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.