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cantera

Runs Cantera homogeneous chemical reactors and evaluates ignition delay with mechanism provenance, conservation checks, and numerical refinement.

Install / Use

npx skills add K-Dense-AI/scientific-agent-skills --skill cantera

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

92/100

Supported Platforms

Universal

Our assessment of cantera

cantera scores 92/100 on our quality scale, 919th of 4,583 Development & Engineering skills we index (top 21%).

Its SKILL.md is 8.4 KB long, split into 7 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

With 46,441 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
15/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 16 days ago, so cantera is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

cantera compared with similar skills

All 4 of these similar skills score higher than cantera; compare them before choosing.

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Frequently asked questions

How do I install cantera?
Run npx skills add K-Dense-AI/scientific-agent-skills --skill cantera. The install tabs above show the steps for each supported agent.
Which AI agents does cantera work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is cantera safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is cantera still maintained?
The repository was last updated 16 days ago, so cantera is actively maintained.

name: cantera description: 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. license: MIT compatibility: Requires Python 3.12-3.14, Cantera 3.2.0, and NumPy. Installation needs network access; simulations run locally without credentials. Custom mechanisms must be available as Cantera YAML files. metadata: version: "1.1" skill-author: K-Dense Inc. tested-package-version: "3.2.0" last-reviewed: "2026-09-30"

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:

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:

| Run | Change from configured conditions | | --- | --- | | baseline | Original settings | | finer_output | Half output spacing, same horizon and solver controls | | tighter_solver | Both solver tolerances divided by ten; maximum internal time step halved | | longer_horizon | Twice 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.

Related Skills

View on GitHub
GitHub Stars46.4k
CategoryDevelopment
Updated16d ago
Forks4.2k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions
cantera — Universal Skill: Install & Safety Check | SkillAgent