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Cantera

OrganizationPopular
K-Dense-AI
cantera

Runs Cantera homogeneous chemical reactors and evaluates ignition delay with mechanism provenance, conservation checks, and numerical refinement. Use for combustion kinetics, closed adiabatic ideal-gas constant-volume or constant-pressure ignition, temperature histories, or mechanism-specific ignition-delay comparisons.

Overview

PublisherK-Dense-AI
Repositoryscientific-agent-skills
Skill namecantera
Stars
48.4K
Forks
4.4K
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by K-Dense-AI on GitHub. Read the source before you install it.

Installation

Install the Cantera AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/cantera
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p .claude/skills
cp -r /tmp/scientific-agent-skills/skills/cantera .claude/skills/cantera
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cantera in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cantera on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cantera is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cantera: homogeneous ignition calculations

When to use

Use for a closed, adiabatic, homogeneous ideal-gas reactor with a known kinetic mechanism, initial temperature, pressure, and mole composition. The bundled helper runs both constant volume and constant pressure cases and reports a precisely defined temperature-based delay. It is not a flame solver or a general reactor-network builder.

A calculation completing successfully establishes numerical execution, not mechanism validity for the fuel, pressure, temperature, diluent, or measured ignition observable. Read references/interpretation.md when choosing a mechanism, comparing experiments, or interpreting unresolved/two-stage ignition.

Workflow

  1. Identify the mechanism and its validated condition range. Preserve its source, version, citation, and any modifications. Check that its phase is ideal-gas and that every reactant, diluent, and tracked species exists. For custom YAML with imports, retain the original dependency files as well as the generated phase snapshot. Custom Python rate extensions additionally need their original code and environment for replay.
  2. Choose constant volume or constant pressure from the physical experiment. Supply K, Pa, seconds, and mole amounts explicitly. mole_amounts is normalized to mole fractions; it is not a mass-fraction mapping. The report includes the normalized initial composition.
  3. Copy assets/hydrogen-ignition.json and change its conditions. The supplied H2/O2/Ar case uses Cantera's bundled h2o2.yaml for an executable numerical example; it is not a recommendation for every hydrogen experiment.
  4. Set a time horizon long enough to observe the temperature rise and the decline of the heating-rate peak. Choose output spacing fine enough to locate that peak. Set a minimum temperature rise to distinguish ignition from negligible heating or numerical noise.
  5. Run the helper and inspect all four histories and the report. Refine again if the delay changes materially, if the maximum approaches a time boundary, or if conservation fails. Compare the temperature and tracked-species histories with the actual ignition definition.
  6. Report the condition set, mechanism hash, reactor constraint, delay definition, output spacing, numerical changes, and scientific limits together with the delay.

Execute the tested example

From the collection root:

bash
uv run --no-project --python 3.12 --with cantera==3.2.0 --with numpy==2.5.3 \
  python skills/cantera/scripts/ignition_delay.py \
  skills/cantera/assets/hydrogen-ignition.json hydrogen-result

Tested on Python 3.12, Cantera 3.2.0, and NumPy 2.5.3. No external solver executable or credentials are needed. Local relative mechanism paths resolve against the configuration file directory before Cantera's built-in data search. Use a new output directory each run.

The 1000 K, 101325 Pa, H2:O2:Ar = 2:1:7 constant-volume example gives about 0.313 ms using the stated max(dT/dt) definition. At 3 ms its temperature is approximately 2920.67 K and agrees with a separate UV equilibrium calculation. These are package regression values, not experimental validation data.

Exact delay and refinement contract

Delay is the time of the global maximum of numpy.gradient(T, time, edge_order=2) on a uniform output grid. It is reported only if the maximum temperature rise reaches minimum_temperature_rise_k and the maximum is at least two sample indices from each boundary. Otherwise delay_s is null and a status explains why. No delay beyond the simulation horizon is extrapolated.

The helper explicitly uses Cantera 3.2's clone=True and reads evolving properties from reactor.phase. The original Solution retains the initial state; do not read it as the reactor's final state. ReactorNet.advance(t) requests an absolute time, and no advance limits are configured, so the output grid remains uniform.

It runs four independent fresh reactors:

RunChange from configured conditions
baselineOriginal settings
finer_outputHalf output spacing, same horizon and solver controls
tighter_solverBoth solver tolerances divided by ten; maximum internal time step halved
longer_horizonTwice the horizon with the original output spacing

numerically_resolved requires all runs to yield delays, relative delay changes within delay_relative_tolerance, and all conservation checks to pass. Agreement on a discrete grid is not a statistical error bar: also report the output spacing. The baseline samples must be between 11 and 50000, leaving room for refinement. Runtime grows with mechanism size, stiffness, and the chosen horizon; integration failures retain Cantera's error text.

Outputs and checks

  • report.json: all input settings, package versions, configuration and mechanism hashes, normalized starting composition, four delay estimates, numerical changes, conservation, and mechanism thermodynamic temperature bounds.
  • baseline.csv, finer_output.csv, tighter_solver.csv, longer_horizon.csv: time, temperature, pressure, volume, mass, total internal energy, total enthalpy, and requested species mole fractions.
  • mechanism.yaml: a Cantera-written snapshot of the loaded phase, species, and reactions. The helper requests write_yaml(precision=17) and saves the exact UTF-8 bytes it hashes, without platform newline conversion. The report also hashes the located original mechanism file. Imported source dependencies are not separately hashed; the snapshot captures the loaded model. Its generated header includes a date, so the snapshot hash identifies the saved artifact and need not match between otherwise identical reruns.

Closed reactors conserve mass and elemental mass fractions. The constant-volume case checks total internal energy; the constant-pressure case checks total enthalpy. Energy error is divided by max(abs(initial_energy_J), 1 J). Diagnostic tolerances are mass relative drift <1e-8, elemental absolute drift <1e-8, energy scaled drift <1e-6, species mass-fraction sum error <1e-8, and species mass fractions >-1e-10. These checks expose numerical issues and do not measure kinetic-model uncertainty.

Check within_thermo_temperature_range separately: it checks saved output states, not every internal integration state. Numerical resolution does not mean species thermodynamic fits stayed within their temperature bounds. The helper cannot assess pressure-dependent kinetic validity from these bounds.

Scope and upstream references

The suite covers both reactor constraints, conservation, final-state agreement with independent Cantera equilibrium, nonigniting conditions, unresolved boundary maxima, refinement, snapshot replay, and invalid composition/conditions. It does not validate shock-tube heat loss, real-gas effects, surfaces, flow devices, flames, or multistage experimental ignition definitions. Build those models only with the necessary physics and their own checks; do not relabel this helper's result as one of them.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cantera AI skill do?

Runs Cantera homogeneous chemical reactors and evaluates ignition delay with mechanism provenance, conservation checks, and numerical refinement. Use for combustion kinetics, closed adiabatic ideal-gas constant-volume or constant-pressure ignition, temperature histories, or mechanism-specific ignition-delay comparisons.

Why use Cantera on TypingMind?

Because you install it once and use it with any model. Cantera is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cantera in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/cantera. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cantera?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cantera?

As many as you like. As long as a model supports skills, you can use Cantera with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cantera AI skill free?

Yes. It is published on GitHub by K-Dense-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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