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-extractionnpx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extractiongit 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-extraction)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-synthesis-extraction"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-synthesis-extraction.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 | $0.00039 | $0.02191 |
| Opus 5 | $0.00019 | $0.01095 |
| Sonnet 5 | $0.00008 | $0.00438 |
| Haiku 4.5 | $0.00004 | $0.00219 |
Grade A, and why
mat-synthesis-extraction 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mat-synthesis-extraction
Goal
Given a folder of scientific paper PDFs, extract all synthesis procedures described in each paper and structure them according to the GeneralSynthesisOntology developed in LeMat-Synth [1]. Output is one JSON file per paper containing a list of per-material synthesis records.
The ontology captures: target compound, compound type, synthesis method, starting materials (with amounts/units/purity), sequential process steps (with actions, conditions, equipment), and overall equipment list.
Instructions
Step 1 — Parse PDFs to text
Run the PDF parser to extract plain text from all PDFs in the input folder.
# Env: base-agent
python .agents/skills/mat-synthesis-extraction/scripts/parse_pdfs.py \
--pdf-dir /path/to/pdf_folder \
--output-dir /path/to/output/texts
This produces:
- One
.txtfile per PDF (named<paper_stem>.txt) - A
parse_summary.jsonlisting extraction status and character counts
Inspect parse_summary.json to confirm all PDFs extracted successfully. Papers with "status": "empty" are likely scanned images — skip them or obtain a text-layer PDF.
Step 2 — Extract synthesized material names
For each .txt file produced in Step 1, identify which materials are synthesized in the paper. Read the paper text and extract a comma-separated list of synthesized compound names.
System prompt to use:
You are a materials science expert. Given the full text of a scientific paper, identify ALL distinct materials that are synthesized (not just characterized or used as reagents). Return ONLY a comma-separated list of chemical names or formulas (e.g. "NiCo2O4, CoFe2O4, Fe3O4"). If no synthesis is described, return an empty string.
Input: full paper text from Step 1 Output: comma-separated string of material names → split into a Python list
Step 3 — Extract GeneralSynthesisOntology per material
For each (paper_text, material_name) pair from Step 2, extract the structured synthesis ontology. Use the system prompt and JSON schema below.
What ships with it
2 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 · 192 lines · 39 tokens per session scan A 082582fc6af3
mat-synthesis-extraction is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 2,191 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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