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 agentmods add skills/learningmatter-mit/atomisticskills/mat-melting-pointnpx skills add learningmatter-mit/AtomisticSkills --skill mat-melting-pointgit 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-melting-point)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-melting-point"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-melting-point.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00024 | $0.01838 |
| Opus 5 | $0.00012 | $0.00919 |
| Sonnet 5 | $0.00005 | $0.00368 |
| Haiku 4.5 | $0.00002 | $0.00184 |
Grade A, and why
mat-melting-point scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Melting Point
Goal
To determine the thermodynamic melting temperature ($T_m$) of a bulk material by equilibrating a solid-liquid interface in an NVE ensemble.
Instructions
-
Background Research:
- Search for the approximate melting point ($T_m$) and boiling/evaporation point ($T_{vap}$) of the material.
- Choose a melting temperature $T_{melt}$ where $T_m < T_{melt} \ll T_{vap}$.
- MD Parameters: Refer to the mat-md-monitors skill for best practices on timesteps and monitors. In general, use a 2.0 fs timestep for systems without Hydrogen.
-
Phase Preparation:
- Solid: Create a supercell using
create_supercell.py.
# Env: base-agent python .agents/skills/mat-melting-point/scripts/create_supercell.py [input_structure.cif] [solid_supercell.cif] --min_length 20.0- Liquid: Melt a block using 1D-NPT (with mask) to ensure matching dimensions.
mcp_mace_run_md( structure_data="solid_supercell.cif", temperature=2000, # REPLACE with T_melt from Step 1 ensemble="npt", steps=5000, timestep=2.0, # Use 2.0 fs for most inorganic systems pressure=1.0, # Apply positive pressure (1-2 bar) to prevent evaporation pressure_mask=[1, 0, 0], # REQUIRED: Must match the stacking axis (e.g., x-axis) output_dir="melt_stage" )- Visual Inspection (CRITICAL): Sometimes the cell does not fully melt within the specified MD steps. You MUST use the
mcp_base_visualize_structuretool to generate an image of the finalliquid.cifstructure (or trajectory) and have the VLM visually inspect the image to confirm that the long-range crystalline order has been destroyed and the cell is completely melted. If it has not, you must run the MD with a higher temperature or for more steps.
- Solid: Create a supercell using
-
Interface Creation: Use
create_interface.pyto concatenate the two phases.# Env: base-agent python .agents/skills/mat-melting-point/scripts/create_interface.py solid.cif liquid.cif --axis 0 --output interface.cif -
Relaxation: Perform an ionic relaxation using the
relax_structureMCP tool withrelax_cell=True. This allows the unit cell to adjust (shrink/expand) to match the density, and remove the interface energy created by stacking the two cells.mcp_mace_relax_structure(structure_data="interface.cif", relax_cell=True) -
Phase Verification: Before running production MD, verify solid-liquid coexistence in all structures.
What ships with it
11 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.
- examples/NaCl/coexistence_structure.png 496 KB
- examples/NaCl/interface_structure.png 468 KB
- examples/NaCl/nvt_structure.png 477 KB
- examples/NaCl/README.md 2.5 KB
- examples/NaCl/temperature_profile.png 90 KB
- scripts/check_phase.py 4.2 KB runs code
- scripts/create_interface.py 1.8 KB runs code
- scripts/create_supercell.py 2.3 KB runs code
- scripts/get_features.py 1.3 KB runs code
- scripts/monitor_melting.py 7.9 KB runs code
- scripts/test_exact_copy.py 1.2 KB runs code
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.
- today First seen · 165 lines · 24 tokens per session scan A e75e9aede33e
mat-melting-point is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 1,838 once invoked, about $0.0001 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 2026-09-03.
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