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-random-structure-searchnpx skills add learningmatter-mit/AtomisticSkills --skill mat-random-structure-searchgit 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-random-structure-search)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-random-structure-search"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-random-structure-search.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 | $0.00031 | $0.01037 |
| Opus 5 | $0.00015 | $0.00518 |
| Sonnet 5 | $0.00006 | $0.00207 |
| Haiku 4.5 | $0.00003 | $0.00104 |
Grade A, and why
mat-random-structure-search 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Random Structure Search (AIRSS-Style)
Goal
To perform random structure searching (RSS) for a given chemical composition — the approach pioneered by AIRSS (Ab Initio Random Structure Searching, Pickard & Needs 2011). Random crystal structures are generated with sensible geometric constraints, then relaxed with an MLIP to identify low-energy candidates.
[!TIP] This method is complementary to ionic substitution and generative models like MatterGen and DiffCSP++. RSS explores the full potential energy surface without structural bias.
Instructions
-
Generate random structures for the target composition:
# Env: base-agent python .agents/skills/mat-random-structure-search/scripts/generate_random_structures.py \ --composition NaCl \ --num_structures 100 \ --output_dir random_NaCl/The script will:
- Sample random space groups from a list of common inorganic crystal space groups
- Generate random lattice parameters consistent with each crystal system
- Place atoms at random fractional coordinates
- Filter structures for minimum interatomic distances
- Save CIF files and a
generation_manifest.json
Optional parameters:
--spacegroups 225,166,62,14— restrict to specific space groups--volume_min 0.6 --volume_max 1.8— control volume randomization range--seed 42— set random seed for reproducibility
-
Relax all structures with an MLIP:
mcp_mace_relax_structure( structure_data="random_NaCl/", relax_cell=True, fmax=0.02, steps=500, output_dir="relaxed_NaCl/" )Or with MatGL/FairChem — use the same MLIP consistently.
-
Rank by energy: The lowest-energy relaxed structures are the most promising candidates. Check for duplicate structures using pymatgen's
StructureMatcher. -
Validate top candidates: Compute stability (E_hull) for the best candidates to assess thermodynamic viability.
What ships with it
6 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/NaFeO2_search/best_structure.png 151 KB
- examples/NaFeO2_search/generation_manifest.json 10 KB
- examples/NaFeO2_search/README.md 3.1 KB
- examples/NaFeO2_search/relaxed_structures/basin_A_ground_state_R3m.cif 989 B
- examples/NaFeO2_search/relaxed_structures/basin_B_metastable.cif 984 B
- scripts/generate_random_structures.py 13 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 · 95 lines · 31 tokens per session scan A 17ce8dd5c9af
mat-random-structure-search is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 1,037 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 2026-09-03.
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