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JimLiu/baoyu-skills
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Extract structured synthesis procedures from a folder of PDFs using the LeMat-Synth GeneralSynthesisOntology schema, producing one JSON file per paper with per-material synthesis records.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-extraction --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mat-synthesis-extraction .claude/skills/mat-synthesis-extraction && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "mat-synthesis-extraction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-extraction into .claude/skills/mat-synthesis-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-extraction", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-extractionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-extraction --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mat-synthesis-extraction .agents/skills/mat-synthesis-extraction && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mat-synthesis-extraction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-extraction into .agents/skills/mat-synthesis-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-extraction", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-extraction --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mat-synthesis-extraction .cursor/skills/mat-synthesis-extraction && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mat-synthesis-extraction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-extraction into .cursor/skills/mat-synthesis-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-extraction", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/mat-synthesis-extraction--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-extraction --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mat-synthesis-extraction .gemini/skills/mat-synthesis-extraction && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mat-synthesis-extraction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-extraction into .gemini/skills/mat-synthesis-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-extraction", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-extractionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mat-synthesis-extraction .github/skills/mat-synthesis-extraction && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mat-synthesis-extraction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-extraction into .github/skills/mat-synthesis-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-extraction", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-extraction --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mat-synthesis-extraction .opencode/skills/mat-synthesis-extraction && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mat-synthesis-extraction" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-extraction into .opencode/skills/mat-synthesis-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-extraction", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
mat-synthesis-extractionExtract structured synthesis procedures from a folder of PDFs using the LeMat-Synth GeneralSynthesisOntology schema, producing one JSON file per paper with per-material synthesis records.
Mat Synthesis Extraction is an agent skill from learningmatter-mit/AtomisticSkills. Extract structured synthesis procedures from a folder of PDFs using the LeMat-Synth GeneralSynthesisOntology schema, producing one JSON file per paper with per-material synthesis records.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `examples/pt-cu-alloy-co-oxidation/README.md` and `scripts/parse_pdfs.py`).
It sits in Documents & Office. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 7f2d86d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
arxiv.orggithub.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Mat Synthesis Extraction loads about 2.2k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 502 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from learningmatter-mit/AtomisticSkills at commit 7f2d86d, republished under its MIT licence (© learningmatter-mit). 502 words, ~2,208 tokens.
.claude/skills/mat-synthesis-extraction/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.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.
Run the PDF parser to extract plain text from all PDFs in the input folder.
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/parse_pdfs.py \
--pdf-dir /path/to/pdf_folder \
--output-dir /path/to/output/textsThis produces:
.txt file per PDF (named <paper_stem>.txt)parse_summary.json listing extraction status and character countsInspect 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.
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
For each (paper_text, material_name) pair from Step 2, extract the structured synthesis ontology. Use the system prompt and JSON schema below.
System prompt:
You are a helpful assistant that extracts the structured synthesis for a specific material from the paper text.
Focus ONLY on the synthesis procedure for the specified material. Search through the entire paper text to find the synthesis procedure that describes how this specific material is made.
IMPORTANT: You must output ONLY a valid JSON object with a "structured_synthesis" field. Do not include any reasoning, explanations, or markdown formatting.
If you cannot find a synthesis procedure for the specified material, return a minimal structure with the material name and an empty synthesis.User message template:
Paper text:
<PAPER_TEXT>
Extract the synthesis procedure for: <MATERIAL_NAME>Required JSON output schema (GeneralSynthesisOntology):
{
"structured_synthesis": {
"target_compound": "string (required) — composition and description of the target",
"target_compound_type": "one of: 'metals & alloys' | 'ceramics & glasses' | 'polymers & soft matter' | 'composites' | 'semiconductors & electronic' | 'nanomaterials' | 'two-dimensional materials' | 'framework & porous materials' | 'biomaterials & biological' | 'liquid materials' | 'hybrid & organic-inorganic' | 'functional materials & catalysts' | 'energy & sustainability' | 'smart & responsive materials' | 'emerging & quantum materials' | 'other'",
"synthesis_method": "one of: 'PVD' | 'CVD' | 'arc discharge' | 'ball milling' | 'spray pyrolysis' | 'electrospinning' | 'sol-gel' | 'hydrothermal' | 'solvothermal' | 'precipitation' | 'coprecipitation' | 'combustion' | 'microwave-assisted' | 'sonochemical' | 'template-directed' | 'solid-state' | 'flux growth' | 'float zone & Bridgman' | 'arc melting & induction melting' | 'spark plasma sintering' | 'electrochemical deposition' | 'chemical bath deposition' | 'liquid-phase epitaxy' | 'self-assembly' | 'atomic layer deposition' | 'molecular beam epitaxy' | 'pulsed laser deposition' | 'ion implantation' | 'lithographic patterning' | 'wet impregnation' | 'incipient wetness impregnation' | 'mechanical mixing' | 'solution-based' | 'mechanochemical' | 'other'",
"starting_materials": [
{
"name": "string",
"amount": "number or null",
"unit": "string or null — e.g. 'g', 'mL', 'mmol', 'wt%'",
"purity": "string or null — e.g. '99.9%', 'ACS grade'",
"vendor": "string or null"
}
],
"steps": [
{
"step_number": "integer",
"action": "one of: 'add' | 'mix' | 'heat' | 'cool' | 'reflux' | 'age' | 'filter' | 'wash' | 'dry' | 'reduce' | 'calcine' | 'dissolve' | 'precipitate' | 'centrifuge' | 'sonicate' | 'anneal' | 'ion exchange' | 'impregnate'",
"description": "string or null",
"materials": [{"name": "string", "amount": "number or null", "unit": "string or null", "purity": "string or null", "vendor": "string or null"}],
"equipment": [{"name": "string", "instrument_vendor": "string or null", "settings": "string or null"}],
"conditions": {
"temperature": "number or null",
"temp_unit": "string or null — 'C', 'K', or 'F'",
"duration": "number or null",
"time_unit": "string or null — 'h', 'min', 's', 'days'",
"pressure": "number or null",
"pressure_unit": "string or null",
"atmosphere": "string or null — e.g. 'air', 'N2', 'Ar'",
"stirring": "boolean or null",
"stirring_speed": "number or null",
"ph": "number or null"
}
}
],
"equipment": [{"name": "string", "instrument_vendor": "string or null", "settings": "string or null"}],
"notes": "string or null"
}
}Retry strategy: If extraction fails or JSON is invalid, retry with slightly increased temperature (0.3, then 0.5). If all retries fail, write a minimal record:
{"target_compound": "<material_name>", "target_compound_type": "other", "synthesis_method": "other", "starting_materials": [], "steps": [], "equipment": [], "notes": "Extraction failed."}After extracting all materials for a paper, write one JSON output file per paper.
Output file: <output_dir>/<paper_stem>_synthesis.json
Output format:
{
"paper": "<paper_stem>",
"source_pdf": "<original_pdf_filename>",
"materials_found": ["Material A", "Material B"],
"syntheses": [
{
"material": "Material A",
"synthesis": { ... }
},
{
"material": "Material B",
"synthesis": { ... }
}
]
}Write all output JSONs to the same --output-dir used in Step 1 (or a dedicated subdirectory).
Inspect the extracted JSONs. Key things to verify:
target_compound matches the material identified in Step 2synthesis_method is not "other" unless genuinely ambiguoussteps list is non-empty for papers that clearly describe synthesisstarting_materials includes amounts/units where reported in the paperFlag papers where steps is empty and notes contains "Extraction failed" for manual review.
See examples/pt-cu-alloy-co-oxidation/ for a worked example using a catalysis paper from ChemRxiv.
parse_pdfs.py) requires cpu (pymupdf installed).cpu for the parsing script.input_configs.yaml in the output directory for reproducibility.[1] Lederbauer et al., "LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature", arXiv, 2025. arXiv:2510.26824
Author: Magdalena Lederbauer Contact: GitHub @mlederbauer
© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (scripts) in skills/mat-synthesis-extraction of learningmatter-mit/AtomisticSkills.
Open the folder on GitHubat commit 7f2d86d
Mat Synthesis Extraction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mat Synthesis Extraction this skilllearningmatter-mit/AtomisticSkills | 175 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Markdown Article FormatterJimLiu/baoyu-skills | 26k | 6 repos | ~3.5k | Automated safety check: Pass | MIT | |
| MarkitdownImCa0/just-laws | 781 | 14 repos | ~3.2k | Automated safety check: Notes | MIT | |
| Obsidian MarkdownAtmosphere/atmosphere | 3.8k | 20 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| DOCXrvdbreemen/OTGW-firmware | 207 | 33 repos | ~4.3k | Automated safety check: Pass | Proprietary | |
| Gzh Designisjiamu/gzh-design-skill | 3.9k | 1 repos | ~2.2k | Automated safety check: Pass | AGPL-3.0 |
JimLiu/baoyu-skills
Reformats plain text or Markdown articles with frontmatter, a title, a summary, headings, bold, lists and code blocks, and saves a separate formatted copy.
ImCa0/just-laws
Convert files and office documents to Markdown. An agent skill from ImCa0/just-laws.
Atmosphere/atmosphere
Create and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax.
rvdbreemen/OTGW-firmware
A skill your agent uses whenever the user wants to create, read, edit, or manipulate Word documents (.docx files).
isjiamu/gzh-design-skill
微信公众号文章排版引擎,将 Markdown 转换为可直接粘贴到公众号编辑器的 HTML。主题风格从 references/theme-index.md 注册的自定义主题库中选取,自动章节编号、关键词下划线标记、引言卡片、目录导航、代码块、图片/GIF、作者签名。支持 Markdown / Word(.docx) / PDF / 纯文本输入(非 Markdown…
HKUDS/DeepTutor
Reads, creates and edits Word .docx files with python-docx, and drops to raw OOXML for tracked changes, comments and byte-exact edits.
learningmatter-mit/AtomisticSkills
Define a docking search box (center coordinates + box dimensions in Angstroms) from a co-crystal ligand, binding-site residues, or a saved JSON specification.
learningmatter-mit/AtomisticSkills
Build a solvated, charge-neutralized protein-ligand complex for OpenMM molecular dynamics simulation.
learningmatter-mit/AtomisticSkills
Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank).
learningmatter-mit/AtomisticSkills
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
learningmatter-mit/AtomisticSkills
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
learningmatter-mit/AtomisticSkills
Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al.
Categories
Extract structured synthesis procedures from a folder of PDFs using the LeMat-Synth GeneralSynthesisOntology schema, producing one JSON file per paper with per-material synthesis records. Mat Synthesis Extraction is an agent skill from learningmatter-mit/AtomisticSkills. Extract structured synthesis procedures from a folder of PDFs using the LeMat-Synth GeneralSynthesisOntology schema, producing one JSON file per paper with per-material synthesis records.
Mat Synthesis Extraction fits situations like: documents & Office work in your project.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a claude-code`. Or copy the skill folder (skills/mat-synthesis-extraction in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-synthesis-extraction in your project. Claude Code loads it when a task matches its description.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a codex`. Or copy the skill folder (skills/mat-synthesis-extraction in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-synthesis-extraction in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-extraction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mat-synthesis-extraction, .gemini/skills/mat-synthesis-extraction, .github/skills/mat-synthesis-extraction and .opencode/skills/mat-synthesis-extraction in your project.
Going by SKILL.md and its folder, Mat Synthesis Extraction needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md names 2 domains. As links in the text: arxiv.org and github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Mat Synthesis Extraction is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Mat Synthesis Extraction: Markdown Article Formatter (JimLiu/baoyu-skills, 26k stars), Markitdown (ImCa0/just-laws, 781 stars), Obsidian Markdown (Atmosphere/atmosphere, 3.8k stars) and DOCX (rvdbreemen/OTGW-firmware, 207 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 2026.
Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.