Experimental Design
aiming-lab/AutoResearchClaw
Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw.
Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures.
MITAuto-check passed
Install Mat Structure Novelty
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-structure-novelty -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-structure-novelty --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-structure-novelty .claude/skills/mat-structure-novelty && 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-structure-novelty" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-structure-novelty into .claude/skills/mat-structure-novelty/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-structure-novelty", 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-structure-noveltyType 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-structure-novelty -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-structure-novelty --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-structure-novelty .agents/skills/mat-structure-novelty && 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-structure-novelty" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-structure-novelty into .agents/skills/mat-structure-novelty/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-structure-novelty", 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-structure-novelty -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-structure-novelty --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-structure-novelty .cursor/skills/mat-structure-novelty && 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-structure-novelty" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-structure-novelty into .cursor/skills/mat-structure-novelty/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-structure-novelty", 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-structure-novelty--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-structure-novelty -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-structure-novelty --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-structure-novelty .gemini/skills/mat-structure-novelty && 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-structure-novelty" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-structure-novelty into .gemini/skills/mat-structure-novelty/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-structure-novelty", 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-structure-noveltyInstalls 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-structure-novelty -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-structure-novelty .github/skills/mat-structure-novelty && 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-structure-novelty" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-structure-novelty into .github/skills/mat-structure-novelty/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-structure-novelty", 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-structure-novelty -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-structure-novelty --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-structure-novelty .opencode/skills/mat-structure-novelty && 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-structure-novelty" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-structure-novelty into .opencode/skills/mat-structure-novelty/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-structure-novelty", 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-structure-novelty- GitHub stars
- 175
- Token cost
- ~1k tokens
- SKILL.md length
- 447 words
- Files
- 13 (incl. scripts)
- Skills in repo
- 129
- Repo updated
- First seen
- Licence
- MIT
At a glance
Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures.
- Works in 2 steps: Execute Direct Novelty Check → Literature Search (Mandatory)
- SKILL.md covers Goal, Instructions, Examples and Constraints
- Runs Python scripts from its folder
What it does
Mat Structure Novelty is an agent skill from learningmatter-mit/AtomisticSkills. Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts (for example `examples/complex-match/README.md`, `examples/complex-match/experimental_match.json` and `examples/complex-match/novel_match.json`).
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-structure-novelty”
Requirements
- Python 3
Workflow steps
2 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 7f2d86d. 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.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Mat Structure Novelty loads about 1k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 447 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 7f2d86d, republished under its MIT licence (© learningmatter-mit). 447 words, ~1,023 tokens.
Download SKILL.mdSave it as
.claude/skills/mat-structure-novelty/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.- name
- mat-structure-novelty
- description
- Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures.
- metadata.category
- materials
- metadata.venv
- cpu
mat-structure-novelty
Goal
To determine whether a user-provided structure (or list of structures) has been previously reported. This is done by matching the target structure against:
- Known polymorphs in the Materials Project (MP). MP entries contain
theoreticaltags, and experimental structures will overlap with the Inorganic Crystal Structure Database (ICSD). - Any arbitrary candidate structure(s) provided by the user.
Instructions
Step 1: Execute Direct Novelty Check
Use the match_structure.py script to perform a symmetry-aware structural comparison. The script accepts a single target CIF or an entire directory of targets to run in bulk.
Option A: Automatic Materials Project Matching (Default) If you do not pass a second argument, the script will automatically query the Materials Project API for all theoretical and experimental polymorphs corresponding to the target formulas, and match your structures against them:
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/match_structure.py generated_cifs/ --output batch_results.jsonOption B: Local Candidate Matching If you want to match against a specific subset of structures (like a local ICSD dump) or just compare two specific structures, pass the explicitly downloaded candidates directory or file:
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/match_structure.py target_structure_1.cif target_structure_2.xyz --output match_results.jsonLiterature Fallback (Novel/Unmatched Structures):
If the script fails to find any structural match among the candidates in the Materials Project, you should perform a literature search to see if the material has been synthesized.
When searching the literature for the structure, ONLY use the composition as input (for example, "Li3ZrCl6" or "Li3InCl6"). Do not include the space group or crystal system in the search query, as papers often do not index those exact terms in searchable abstracts.
After finding papers that report the composition, you must read the paper and compare the structure described in the literature with your candidate polymorph to determine if they match.
[!IMPORTANT] If a literature match is reported but the full text is not available (Open Access = False) and you are unable to definitively read the paper to confirm the exact reported structure matches yours, you MUST explicitly tell the user that "literature full text is not available and the structure cannot be conclusively confirmed".
Show full SKILL.md (118 more words)Show less
Step 2: Literature Search (Mandatory)
If the structure is entirely novel, or just to know if the composition itself has been heavily studied or synthesized in particular conditions, you MUST perform a literature search using the search_literature MCP tool.
mcp_search_literature(
query="synthesis of Li10GeP2S12",
limit=5,
download=False
)Examples
Checking if a generated structure has been experimentally reported:
# This automatically searches MP for LiFePO4 structures and compares against generated_LFP.cif
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/match_structure.py generated_LFP.cif --output match_results.jsonConstraints
- Environments: The script uses the standard
StructureMatcherfrompymatgen. It MUST be run in thecpuenvironment. - Match Tolerances: The structural matching relies on fractional length tolerance (
--ltol), site tolerance (--stol), and angle tolerance (--angle_tol). The defaults (0.2, 0.3, 5.0) are typically suitable for DFT-relaxed comparison against MP structures, but can be tweaked if the test structure is highly distorted or unrelaxed.
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 12 other files (scripts) in skills/mat-structure-novelty of learningmatter-mit/AtomisticSkills.
- SKILL.md
- examples/complex-match/README.md
- examples/complex-match/candidates/mp-696128.cif
- examples/complex-match/candidates/mp-696138.cif
- examples/complex-match/candidates/mp-942733.cif
- examples/complex-match/experimental_match.json
- examples/complex-match/known_experimental.cif
- examples/complex-match/novel_match.json
- examples/complex-match/novel_structure.cif
- examples/literature-fallback-match/Li2ZrCl6.cif
- examples/literature-fallback-match/README.md
- examples/literature-fallback-match/fallback_match.json
- scripts/match_structure.py
Open the folder on GitHubat commit 7f2d86d
Compare with similar skills
Mat Structure Novelty 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 Structure Novelty this skilllearningmatter-mit/AtomisticSkills | 175 | — | ~1k | Automated safety check: Pass | MIT | |
| Experimental Designaiming-lab/AutoResearchClaw | 15k | — | ~286 | Automated safety check: Pass | MIT | |
| Find Matching Tenderssickn33/agentic-awesome-skills | 47k | 1 repos | ~1k | Automated safety check: Pass | Apache-2.0 | |
| Experimentationcbrock84/headcount | 2k | — | ~971 | Automated safety check: Pass | MIT | |
| Og URL Matchthedaviddias/Front-End-Checklist | 74k | — | ~543 | Automated safety check: Pass | MIT | |
| HTML Ppt Zhangzara Matnexu-io/open-design | 100k | — | ~1.2k | Automated safety check: Pass | MIT |
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Questions about Mat Structure Novelty
What does Mat Structure Novelty do?
Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures. Mat Structure Novelty is an agent skill from learningmatter-mit/AtomisticSkills. Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures.
How do I install Mat Structure Novelty in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-structure-novelty -a claude-code`. Or copy the skill folder (skills/mat-structure-novelty in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-structure-novelty in your project. Claude Code loads it when a task matches its description.
How do I install Mat Structure Novelty in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-structure-novelty -a codex`. Or copy the skill folder (skills/mat-structure-novelty in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-structure-novelty in your project. Codex loads it when a task matches its description.
Can I use Mat Structure Novelty 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-structure-novelty -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-structure-novelty, .gemini/skills/mat-structure-novelty, .github/skills/mat-structure-novelty and .opencode/skills/mat-structure-novelty in your project.
What does Mat Structure Novelty need to run?
Going by SKILL.md and its folder, Mat Structure Novelty needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Mat Structure Novelty access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is Mat Structure Novelty 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 Structure Novelty use?
Mat Structure Novelty 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 Structure Novelty use?
About 1k tokens (SKILL.md is roughly 4.1k 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 Structure Novelty?
Skills that share tags, products or a category with Mat Structure Novelty: Experimental Design (aiming-lab/AutoResearchClaw, 15k stars), Find Matching Tenders (sickn33/agentic-awesome-skills, 47k stars), Experimentation (cbrock84/headcount, 2k stars) and Og URL Match (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mat Structure Novelty?
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.