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 learningmatter-mit/AtomisticSkills --skill mat-calphad-property-diagramgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/mat-calphad-property-diagram)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-calphad-property-diagram"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-calphad-property-diagram/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/learningmatter-mit/atomisticskills/mat-calphad-property-diagram"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-calphad-property-diagram.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00031 | $0.00513 |
| Opus 5 | $0.00015 | $0.00257 |
| Sonnet 5 | $0.00006 | $0.00103 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
mat-calphad-property-diagram 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 9d 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.
What it actually says
mat-calphad-property-diagram
Goal
To predict the equilibrium phase stability, phase fractions, and other extensive thermodynamic properties for a fixed multi-component alloy at different temperatures using PyCalphad. Very useful for modeling solidification, heat treatment paths, and precipitation sequences.
Instructions
1. Identify Thermodynamic Database
You must obtain a legitimate .tdb (Thermodynamic Data Base) file for the chemical system.
2. Plot Equilibrium Phase Fractions
Calculate what phases are present, and their molar fractions, across a cooling/heating schedule for a fixed composition.
# Env: calphad-agent
python .agents/skills/mat-calphad-property-diagram/scripts/plot_phase_fractions.py path/to/database.tdb --elements Element1 Element2 --composition Element2 0.3 --t-range 300 1000 10 --output research_dir/phase_fractions.png
--composition: The solute element and its molar fraction (e.g.Zn 0.3means 30 mol% Zn).--t-range:START STOP STEPin Kelvin. Ensure solving across liquidus and solidus.
Examples
Evaluating phase fractions for an Al-40%Zn alloy as it cools:
# Env: calphad-agent
python .agents/skills/mat-calphad-property-diagram/scripts/plot_phase_fractions.py .agents/skills/mat-calphad-phase-diagram/examples/Al-Zn/alzn_mey.tdb --elements Al Zn --composition Zn 0.4 --t-range 300 900 10 --output phase_fractions.png
Constraints
- Environments: Scripts require the
calphad-agentConda environment. - Only plots equilibrium step (lever-rule). For non-equilibrium fast solidification (Scheil), custom scripting is required.
References
- Richard Otis and Zi-Kui Liu. "pycalphad: CALPHAD-based Computational Thermodynamics in Python." Journal of Open Research Software (2017).
Author: Bowen Deng Contact: GitHub @learningmatter-mit
What ships with it
3 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.
- 9d ago First seen · 47 lines · 31 tokens per session scan A 5f263ddea813
mat-calphad-property-diagram is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (162 stars, last pushed 5d ago), licensed MIT. It adds 31 tokens to every session and 513 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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