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Agent skill

Tellurium

by K-Dense-AI in K-Dense-AI/scientific-agent-skills

Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus…

MITAuto-check passedResearch & Science

Install Tellurium

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill tellurium -a claude-code

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

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills tellurium --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/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tellurium .claude/skills/tellurium && 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
tellurium
GitHub stars
48k
Used in
1 other repo
Token cost
~2k tokens
SKILL.md length
775 words
Files
5 (incl. scripts, references, assets)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus…

  • Works in 6 steps: Inspect the supplied model's… → Check units before interpreting a… → Select concentration outputs and an… → …
  • Reaction-network time courses
  • SKILL.md covers When to use, Runtime, Workflow and Run the executable reference, plus 2 more sections
  • Runs Python scripts from its folder; calls uv and python

What it does

Tellurium is an agent skill from K-Dense-AI/scientific-agent-skills. Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts, reference files and assets (for example `assets/experiment.json`, `references/experiments.md` and `scripts/kinetic_experiment.py`). Compatibility notes: Requires Python 3.11 with Tellurium 2.2.13.1, libRoadRunner 2.10.0, Antimony 3.2.0, python-libsbml 5.21.2, python-libsedml 2.0.34 and python-libcombine…

It sits in Research & Science. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.

When your agent uses it

  • Reaction-network time courses
  • Kinetic parameters
  • Concentration dynamics and reproducible simulation experiments
  • Steady-state constraint-based metabolic flux analysis belongs to cobrapy

Example prompts

  • “Use the tellurium skill to simulate biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares…”
  • “/tellurium”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires Python 3.11 with Tellurium 2.2.13.1, libRoadRunner 2.10.0, Antimony 3.2.0, python-libsbml 5.21.2, python-libsedml 2.0.34 and python-libcombine 0.2.20. Network is needed for installation only. Native wheels were tested on macOS ARM64. No credentials or external services.

Workflow steps

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

  1. Inspect the supplied model's compartments, species, initial conditions, boundary species,
  2. Check units before interpreting a trajectory. SBML reaction rates have amount/time units;
  3. Select concentration outputs and an experiment in the JSON format described in
  4. Run baseline and desired constant-global-parameter changes. Every scenario starts from a
  5. Examine finite outputs, signs, relevant conservation relations and timescales. Check solver
  6. Review the COMBINE replay comparison, model warnings and units in report.json. The helper

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. 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:

    • uv
    • python

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

  • Network

    Links to these hosts (documentation or services it may open):

    • tellurium.readthedocs.io
    • github.com
    • sed-ml.org

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

  • Compatibility

    Requires Python 3.11 with Tellurium 2.2.13.1, libRoadRunner 2.10.0, Antimony 3.2.0, python-libsbml 5.21.2, python-libsedml 2.0.34 and python-libcombine 0.2.20. Network is needed for installation only. Native wheels were tested on macOS ARM64. No credentials or external services.

    From compatibility in the SKILL.md frontmatter.

Context cost

Tellurium loads about 2k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 107 tokens; SKILL.md has 775 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~107
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.9k

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 passed

The automated check found no risky patterns in SKILL.md.

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 K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 775 words, ~2,025 tokens.

Download SKILL.mdSave it as .claude/skills/tellurium/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
tellurium
description
Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.
compatibility
Requires Python 3.11 with Tellurium 2.2.13.1, libRoadRunner 2.10.0, Antimony 3.2.0, python-libsbml 5.21.2, python-libsedml 2.0.34 and python-libcombine 0.2.20. Network is needed for installation only. Native wheels were tested on macOS ARM64. No credentials or external services.
license
MIT
metadata.version
1.1
metadata.skill-author
K-Dense Inc.
metadata.upstream-version
2.2.13.1
metadata.last-reviewed
2026-10-01

Tellurium kinetic experiments

When to use

Use this skill for deterministic reaction-network trajectories and independent parameter conditions from a local model. The helper performs SBML consistency checks, CVODE integration and an actual COMBINE archive replay. It exports each condition's exact SBML and the SED-ML experiment rather than handing off an unrecorded notebook state.

Runtime

bash
uv venv --python 3.11 kinetic-env
uv pip install --python kinetic-env/bin/python tellurium==2.2.13.1 libroadrunner==2.10.0 \
  antimony==3.2.0 python-libsbml==5.21.2 python-libsedml==2.0.34 python-libcombine==0.2.20

The full workflow ran with these packages on macOS ARM64. It constructs SED-ML with libSEDML and archives with Tellurium/libCombine; PhraSEDML is not required by this helper. Headless runs can set MPLBACKEND=Agg. No plotting window is opened by the helper.

The six pinned releases were rechecked against official PyPI metadata on 2026-10-01. RoadRunner's documentation site still displays an old version banner; the solver settings below were also checked against released 2.10.0 source and the installed native runtime.

Workflow

  1. Inspect the supplied model's compartments, species, initial conditions, boundary species, reactions, parameter definitions and rules/events. Identify the scientific question and distinguish a mechanistic kinetic model from a flux-balance reconstruction. Record the source model, version and any literature parameters; do not treat an example model as experimentally calibrated.
  2. Check units before interpreting a trajectory. SBML reaction rates have amount/time units; species may have concentration or amount semantics. In a fixed-volume first-order model, k*A*cell converts concentration dependence into amount/time. The helper checks SBML consistency and retains every warning, including undefined units. Undefined units are reported as empty/indeterminable, not silently assumed to mean SI.
  3. Select concentration outputs and an experiment in the JSON format described in references/experiments.md. Time values use the model's own time units. The tested helper outputs concentration for species with hasOnlySubstanceUnits=false; it rejects amount-only selections to avoid changing their meaning during SED-ML replay. Zero-dimensional compartments and rate-rule models are also rejected; the latter need a separate tolerance workflow because RoadRunner 2.10.0 can order scalar tolerances differently from states.
  4. Run baseline and desired constant-global-parameter changes. Every scenario starts from a fresh SBML model, so previous final concentrations cannot leak into the next condition. Changes to species initial values, compartment volume, assignment rules or time-varying inputs require explicit model changes and corresponding tests; they are not parameter mutations hidden in this helper.
  5. Examine finite outputs, signs, relevant conservation relations and timescales. Check solver sensitivity by repeating at stricter tolerances when the scientific interpretation depends on small differences. A smooth curve or zero archive-replay error does not establish model validity or parameter identifiability. Never clip negative concentrations to hide solver or model problems.
  6. Review the COMBINE replay comparison, model warnings and units in report.json. The helper replays the archive it generated and compares every selected value against the direct trajectories. Deliver the archive, report, source model, experiment config and CSV curves.
Show full SKILL.md (330 more words)Show less

Run the executable reference

assets/first-order.ant defines the closed reaction A → B in a constant 1-L compartment, initially A=1 and B=0 mol/L, with k=0.2 per second. assets/experiment.json runs baseline and k=0.4 per second from 0 to 10 s. From the skill directory, point the interpreter to the environment created above:

bash
MPLBACKEND=Agg kinetic-env/bin/python scripts/kinetic_experiment.py \
  --model assets/first-order.ant --format antimony --experiment assets/experiment.json \
  --output kinetic-reference

# The SBML branch was also exercised; replace these filenames with actual user inputs.
MPLBACKEND=Agg kinetic-env/bin/python scripts/kinetic_experiment.py \
  --model model.xml --format sbml --experiment experiment.json --output kinetic-analysis

Output directories must be new. The reference was executed, including Antimony-to-SBML conversion, libSBML checks, both direct integrations, SED-ML creation and COMBINE replay. Both conditions matched the analytical A(t)=exp(-k*t), B(t)=1-A(t) within 2e-8 absolute/relative tolerance; A+B was conserved within 1e-10, and archive replay matched direct output exactly on the tested stack. Additional checks use a 5-L compartment and an initial amount of 10 mol (2 mol/L), resolve every SED-ML species XPath against its actual SBML file, and verify the solver tolerance scaling. That verifies this controlled example; arbitrary SBML packages, events, delays or stochastic models are not covered by those tests.

Artifacts

FileContents
baseline.csv, other scenario CSVsTime and selected concentrations, with bracketed species headers
model_<scenario>.xmlExact independent SBML condition used by both execution routes
experiment.sedmlUniform time course, CVODE/tolerances, models, tasks and output selections
experiment.omexThose SBML files plus the master SED-ML and archive manifest
report.jsonVersions, input/archive checksums, parameters, units, validation findings, initial state tolerance vectors, minimum concentrations and replay differences

The libSEDML findings in the report are parse diagnostics. Successful execution and equality provide additional evidence that this generated uniform-course experiment works in Tellurium; they do not certify every SED-ML feature or every simulator's compatibility.

In RoadRunner 2.10.0, the JSON absolute_tolerance value is a scalar adjustment factor for state/amount tolerances, not a uniform concentration error bound. The archive records that meaning as KISAO:0000571; inspect initial_state_absolute_tolerances and the detailed explanation in references/experiments.md. Declared SBML XPath namespaces and explicit stiff/uniform-output settings prevent successful Tellurium replay from hiding missing archive context.

Primary references

© K-Dense-AI, 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 4 other files (scripts, references, assets) in skills/tellurium of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • assets/experiment.json
  • assets/first-order.ant
  • references/experiments.md
  • scripts/kinetic_experiment.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Last30daysmvanhorn/last30days-skill64k—~7.9kAutomated safety check: NotesMIT

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Categories

Questions about Tellurium

What does Tellurium do?

Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus…. Tellurium is an agent skill from K-Dense-AI/scientific-agent-skills. Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives.

When should I use Tellurium?

Tellurium fits situations like: reaction-network time courses; kinetic parameters; concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.

How do I install Tellurium in Claude Code?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill tellurium -a claude-code`. Or copy the skill folder (skills/tellurium in K-Dense-AI/scientific-agent-skills) into .claude/skills/tellurium in your project. Claude Code loads it when a task matches its description.

How do I install Tellurium in Codex?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill tellurium -a codex`. Or copy the skill folder (skills/tellurium in K-Dense-AI/scientific-agent-skills) into .agents/skills/tellurium in your project. Codex loads it when a task matches its description.

Can I use Tellurium 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 K-Dense-AI/scientific-agent-skills --skill tellurium -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tellurium, .gemini/skills/tellurium, .github/skills/tellurium and .opencode/skills/tellurium in your project.

What does Tellurium need to run?

Going by SKILL.md and its folder, Tellurium needs Python for the scripts in its folder and the command-line tools its instructions call (uv and python). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires Python 3.11 with Tellurium 2.2.13.1, libRoadRunner 2.10.0, Antimony 3.2.0, python-libsbml 5.21.2, python-libsedml 2.0.34 and python-libcombine 0.2.20. Network is needed for installation only. Native wheels were tested on macOS ARM64. No credentials or external services..

Does Tellurium access the network?

SKILL.md names 3 domains. As links in the text: tellurium.readthedocs.io, github.com and sed-ml.org. This is read from the text; nothing was executed.

Is Tellurium safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. 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 Tellurium use?

Tellurium 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 Tellurium use?

About 2k tokens (SKILL.md is roughly 8.1k 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 1.8k tokens, read only when the agent opens those files.

What are the alternatives to Tellurium?

Skills that share tags, products or a category with Tellurium: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tellurium?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.

Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.