Transport analysis
Use when the task is to analyze transport-relevant quantities from DFT-derived results, including carrier-type tendency, effective-mass estimates, simple DOS-informed transport trends, thermoelectric-style screening descriptors, bipolar-risk checks, multi-candidate ranking, and compact markdown reports from finished calculations.From its SKILL.md
npx -y skills add chatmaterials/transport-analysisAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
2.0 KB, 311 tokens by cl100k_base, as published. Nobody here has run it
Transport Analysis
Use this skill for transport-oriented post-processing rather than generic workflow setup.
When to use
- estimate whether the electronic structure suggests electron- or hole-like transport tendency
- estimate an effective mass from a simple band-edge dispersion
- summarize a transport trend from band-edge and DOS information
- derive compact thermoelectric-style screening descriptors such as activation energy and transport quality score
- estimate bipolar-conduction risk from the band gap and temperature
- rank multiple candidate datasets with a compact screening heuristic
- write a compact transport-analysis report from existing data
Use the bundled helpers
scripts/analyze_carrier_type.pyEstimate a carrier-type tendency from a band structure and Fermi level.scripts/analyze_effective_mass.pyEstimate an effective mass from a simple band-edge dispersion.scripts/analyze_transport_trend.pySummarize a simple transport trend from band-edge and DOS information and derive compact quality descriptors.scripts/analyze_bipolar_risk.pyEstimate a compact bipolar-conduction risk from the band gap and temperature.scripts/compare_transport_candidates.pyRank multiple candidate datasets with a compact gap-plus-mass screening heuristic.scripts/export_transport_report.pyExport a markdown transport-analysis report.
Guardrails
- Treat these outputs as compact descriptors, not a full Boltzmann transport workflow.
- State the assumed Fermi level and occupied-band count explicitly.
- Distinguish qualitative trend analysis from quantitative transport coefficients.
What ships with it: 23 files
28.7 KB alongside SKILL.md, 7 of them executable
agents/
- openai.yaml266 B
fixtures/
- band/bands.dat54 B
- candidates/heavy/bands.dat54 B
- candidates/heavy/dos.dat42 B
- candidates/heavy/effective_mass.dat57 B
- candidates/metallic/bands.dat52 B
- candidates/metallic/dos.dat42 B
- candidates/narrow-gap/bands.dat52 B
- candidates/narrow-gap/dos.dat42 B
- candidates/narrow-gap/effective_mass.dat60 B
- dos/dos.dat42 B
- effective_mass/effective_mass.dat57 B
references/
- transport.md767 B
scripts/
- analyze_bipolar_risk.pyruns1.9 KB
- analyze_carrier_type.pyruns2.0 KB
- analyze_effective_mass.pyruns2.9 KB
- analyze_transport_trend.pyruns4.0 KB
- compare_transport_candidates.pyruns5.4 KB
- export_transport_report.pyruns4.2 KB
- run_regression.pyruns3.9 KB
- .gitignore34 B
- LICENSE1.1 KB
- README.md1.7 KB