synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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.
npx skills add synthetic-sciences/openscience --skill brenda-databasegit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/brenda-database)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/brenda-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/brenda-database/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/brenda-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/brenda-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 678 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.05862 |
| Opus 5.5 | $0.00017 | $0.02345 |
| Sonnet 5.5 | $0.00009 | $0.01172 |
| Haiku 4.5 | $0.00004 | $0.00586 |
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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- brenda-database — 98% identical, 29 lines differ
- brenda-database — 98% identical, 29 lines differ
- brenda-database — 98% identical, 29 lines differ
- brenda-database — 97% identical, 35 lines differ
- brenda-database — 89% identical, 32 lines differ
- brenda-database — 89% identical, 32 lines differ
- brenda-database — 89% identical, 32 lines differ
- brenda-database — 89% identical, 32 lines differ
How it starts
The opening of the file, as written. The whole thing — 727 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. Run the examples with this skill
directory as the working directory: from scripts.brenda_client import ... resolves
scripts/ relative to it.
Get Km Values by EC Number:
from scripts.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')}")
What ships with it
6 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.
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.
- 1mo ago Changed · +6 lines 5c0171f2f7ed
- 1mo ago Changed · +2 lines abef10c884cd
- 1mo ago Changed 73a93313439e
- 1mo ago First seen · 719 lines · 43 tokens per session scan A 205b7099436e
brenda-database is a skill published in the GitHub repository synthetic-sciences/openscience (4,121 stars, last pushed 2d ago), licensed Apache-2.0. It adds 43 tokens to every session and 5,862 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 2026-09-03.
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paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.