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-synthesis-recommendationnpx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-recommendationgit 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-synthesis-recommendation)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-synthesis-recommendation"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-synthesis-recommendation.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 | $0.01282 |
| Opus 5 | $0.00015 | $0.00641 |
| Sonnet 5 | $0.00006 | $0.00256 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
mat-synthesis-recommendation 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthesis Recommendation
Goal
To provide experimentally validated synthesis routes for target inorganic materials by querying Materials Project's text-mined database of synthesis recipes extracted from scientific literature. This skill returns precursor materials, synthesis procedures, reaction equations, and DOI references to published papers.
Instructions
1. Query Synthesis Recipes
Search for synthesis recipes for a target material using the Materials Project API:
# Env: base-agent
python .agents/skills/mat-synthesis-recommendation/scripts/recommend_synthesis.py "LiFePO4" --limit 10 --output synthesis_recipes.json
Parameters:
formula: Target material formula (e.g.,"LiFePO4","Li2CO3","NMC811")--limit: Maximum number of recipes to display (default: 10)--output: Optional JSON file to save results--type: Filter by synthesis type (e.g.,"solid-state","hydrothermal","sol-gel")--min-temp: Minimum synthesis temperature in °C--max-temp: Maximum synthesis temperature in °C
Output: Recipes are automatically ranked by:
- Simplicity: Fewer precursors preferred
- Temperature: Lower synthesis temperatures preferred
- Synthesis type: Common methods (solid-state, hydrothermal) ranked higher
2. Interpret Results
Each recipe contains:
- Target material: Normalized chemical formula
- Precursors: Starting materials/reagents
- Synthesis type: Method category (solid-state, hydrothermal, sol-gel, etc.)
- Procedure: Step-by-step synthesis description from the paper
- Reaction equation: Balanced chemical equation (when available)
- DOI: Link to the source publication for full experimental details
3. Validate Synthesis Feasibility (Optional)
Cross-check the recommended precursors with other skills:
# Check if target material is thermodynamically stable
# See: ../mat-stability/SKILL.md
python .agents/skills/mat-stability/scripts/calculate_stability.py target.cif --output stability_analysis.json
# Calculate formation energy to verify synthesizability
# Energy above hull (E_hull) < 0.1 eV/atom indicates likely synthesizability
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.
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 · 125 lines · 31 tokens per session scan A 1e05801efaf7
mat-synthesis-recommendation 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 1,282 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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