Recipe Share Event Materials
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Share Google Drive files with all attendees of a Google Calendar event.
Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.
MITAuto-check passed
Install Mat Synthesis Recommendation
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-recommendation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-recommendation --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-recommendation .claude/skills/mat-synthesis-recommendation && 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-recommendation" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-recommendation into .claude/skills/mat-synthesis-recommendation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-recommendation", 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-recommendationType 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-recommendation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-recommendation --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-recommendation .agents/skills/mat-synthesis-recommendation && 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-recommendation" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-recommendation into .agents/skills/mat-synthesis-recommendation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-recommendation", 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-recommendation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-recommendation --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-recommendation .cursor/skills/mat-synthesis-recommendation && 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-recommendation" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-recommendation into .cursor/skills/mat-synthesis-recommendation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-recommendation", 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-recommendation--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-recommendation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-synthesis-recommendation --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-recommendation .gemini/skills/mat-synthesis-recommendation && 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-recommendation" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-recommendation into .gemini/skills/mat-synthesis-recommendation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-recommendation", 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-recommendationInstalls 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-recommendation -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-recommendation .github/skills/mat-synthesis-recommendation && 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-recommendation" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-recommendation into .github/skills/mat-synthesis-recommendation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-recommendation", 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-recommendation -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-recommendation --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-recommendation .opencode/skills/mat-synthesis-recommendation && 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-recommendation" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-synthesis-recommendation into .opencode/skills/mat-synthesis-recommendation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-synthesis-recommendation", 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.
Facts
- Skill name
mat-synthesis-recommendation- GitHub stars
- 176
- Token cost
- ~1.4k tokens
- SKILL.md length
- 452 words
- Files
- 7 (incl. scripts)
- Skills in repo
- 129
- Repo updated
- First seen
- Licence
- MIT
At a glance
Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.
- Works in 3 steps: Query Synthesis Recipes → Interpret Results → Validate Synthesis Feasibility (Optional)
- SKILL.md covers Goal, Instructions, Examples and Constraints, plus 2 more sections
- Runs Python scripts from its folder; needs MP_API_KEY
What it does
Mat Synthesis Recommendation is an agent skill from learningmatter-mit/AtomisticSkills. Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts (for example `examples/LiCoO2/README.md`, `examples/LiCoO2/synthesis_recipes.json` and `examples/LiFePO4/README.md`).
The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.
Example prompts
- “/mat-synthesis-recommendation”
Requirements
- Python 3
- A credential in MP_API_KEY
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
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.
Network
Links to these hosts (documentation or services it may open):
github.commaterialsproject.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
MP_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Mat Synthesis Recommendation loads about 1.4k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 452 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.
Safety
Auto-check passed
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.
SKILL.md
The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 452 words, ~1,384 tokens.
Download SKILL.mdSave it as
.claude/skills/mat-synthesis-recommendation/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- mat-synthesis-recommendation
- description
- Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.
- metadata.category
- materials
- metadata.venv
- cpu
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:
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/recommend_synthesis.py "LiFePO4" --limit 10 --output synthesis_recipes.jsonParameters:
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
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/../mat-stability/scripts/query_mp_hull.py \
--formula "Li-Fe-P-O" --target "LiFePO4" --output hull_structures/
# Calculate formation energy to verify synthesizability
# Energy above hull (E_hull) < 0.1 eV/atom indicates likely synthesizabilityExamples
Example 1: Basic Query for LiFePO4
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/recommend_synthesis.py "LiFePO4" --limit 5Expected output:
- 5 synthesis recipes ranked by simplicity
- Common precursors: Li₂CO₃, FeC₂O₄, NH₄H₂PO₄
- Typical methods: solid-state reaction, hydrothermal synthesis
- DOI links to papers in J. Electrochem. Soc., Chem. Mater., etc.
Example 2: Filter by Synthesis Type
# Query only hydrothermal synthesis routes for LiCoO2
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/recommend_synthesis.py "LiCoO2" --type hydrothermal --limit 10 --output LiCoO2_hydrothermal.jsonExample 3: Temperature-Constrained Search
# Find low-temperature synthesis routes (< 600°C) for Li2CO3
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/recommend_synthesis.py "Li2CO3" --max-temp 600 --limit 10Show full SKILL.md (216 more words)Show less
Constraints
- API Key Required: Requires Materials Project API key via
MP_API_KEYenvironment variable or~/.atomistic_skills.yamlconfiguration - Environment: This skill requires the
cpuenvironment (includesmp-api,pymatgen) - Coverage Limitations: Not all materials have synthesis recipes in the database
- Database contains ~55,000 recipes for common inorganic materials
- Coverage is best for battery materials, ceramics, and metal oxides
- Organic materials and MOFs have limited coverage
- Text-Mining Accuracy: Recipes are automatically extracted from literature using NLP
- Precursors and procedures are generally accurate but should be verified against the source DOI
- Temperature values may be missing or incomplete in some entries
- Data Freshness: The text-mined database is periodically updated but may not include the most recent publications
Data Source
The synthesis recipes are extracted from scientific literature using natural language processing by the Materials Project team. The underlying datasets include:
- Solid-state synthesis: 19,488+ recipes from the CederGroup text-mined database
- Solution-based synthesis: 35,675+ recipes for solution, hydrothermal, and sol-gel methods
- NLP pipeline: Transformer-based models for paragraph classification, named entity recognition, and synthesis action extraction
References:
- Materials Project Synthesis Explorer: https://materialsproject.org/synthesis
- Text-mined synthesis datasets: CederGroup GitHub
- Kim et al., "A Database of Synthesis Recipes for Inorganic Materials," Sci. Data (2017)
Related Skills
- mat-stability: Verify thermodynamic stability before attempting synthesis
- mat-intercalation-voltage: Calculate electrochemical properties of synthesized cathode materials
Author: Bowen Deng Contact: GitHub @learningmatter-mit
© 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
Files
SKILL.md and 6 other files (scripts) in skills/mat-synthesis-recommendation of learningmatter-mit/AtomisticSkills.
- SKILL.md
- examples/LiCoO2/README.md
- examples/LiCoO2/synthesis_recipes.json
- examples/LiFePO4/README.md
- examples/LiFePO4/synthesis_recipes.json
- resources/synthesis_types.yaml
- scripts/recommend_synthesis.py
Open the folder on GitHubat commit 6257444
Compare with similar skills
Mat Synthesis Recommendation 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 Recommendation this skilllearningmatter-mit/AtomisticSkills | 176 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Recipe Share Event Materialsgoogleworkspace/cli | 31k | — | ~232 | Automated safety check: Pass | Apache-2.0 | |
| Nemo Mbridge Recipe RecommenderNVIDIA/skills | 3.5k | — | ~4.1k | Automated safety check: Pass | Apache-2.0 | |
| Ecc Recipesaffaan-m/ECC | 275k | 1 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Investor Materialsaffaan-m/ECC | 275k | 3 repos | ~268 | Automated safety check: Pass | MIT | |
| Material Designsickn33/agentic-awesome-skills | 47k | 1 repos | ~2.6k | Automated safety check: Pass | MIT |
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Questions about Mat Synthesis Recommendation
What does Mat Synthesis Recommendation do?
Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references. Mat Synthesis Recommendation is an agent skill from learningmatter-mit/AtomisticSkills. Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.
How do I install Mat Synthesis Recommendation in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-recommendation -a claude-code`. Or copy the skill folder (skills/mat-synthesis-recommendation in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-synthesis-recommendation in your project. Claude Code loads it when a task matches its description.
How do I install Mat Synthesis Recommendation in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-synthesis-recommendation -a codex`. Or copy the skill folder (skills/mat-synthesis-recommendation in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-synthesis-recommendation in your project. Codex loads it when a task matches its description.
Can I use Mat Synthesis Recommendation in Cursor, Gemini CLI or GitHub Copilot?
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-recommendation -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-recommendation, .gemini/skills/mat-synthesis-recommendation, .github/skills/mat-synthesis-recommendation and .opencode/skills/mat-synthesis-recommendation in your project.
What does Mat Synthesis Recommendation need to run?
Going by SKILL.md and its folder, Mat Synthesis Recommendation needs Python for the scripts in its folder and credentials named MP_API_KEY. Our summary lists: Python 3; A credential in MP_API_KEY.
Does Mat Synthesis Recommendation access the network?
SKILL.md names 2 domains. As links in the text: github.com and materialsproject.org. This is read from the text; nothing was executed.
Is Mat Synthesis Recommendation safe to install?
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.
What licence does Mat Synthesis Recommendation use?
Mat Synthesis Recommendation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mat Synthesis Recommendation use?
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Mat Synthesis Recommendation?
Skills that share tags, products or a category with Mat Synthesis Recommendation: Recipe Share Event Materials (googleworkspace/cli, 31k stars), Nemo Mbridge Recipe Recommender (NVIDIA/skills, 3.5k stars), Ecc Recipes (affaan-m/ECC, 275k stars) and Investor Materials (affaan-m/ECC, 275k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mat Synthesis Recommendation?
learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 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.