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Agent skill

Chem TS Optimization

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.

MITAuto-check passed

Install Chem TS Optimization

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill chem-ts-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills chem-ts-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/chem-ts-optimization .claude/skills/chem-ts-optimization && rm -rf skills-src

Use ~/.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/

Facts

Skill name
chem-ts-optimization
GitHub stars
175
Token cost
~876 tokens
SKILL.md length
286 words
Files
10 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.

  • SKILL.md covers Scope, Tool, Arguments and Outputs, plus 4 more sections
  • Runs Shell and Python scripts from its folder

What it does

Chem TS Optimization is an agent skill from learningmatter-mit/AtomisticSkills. Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.

Its SKILL.md is about 880 tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts (for example `examples/acetonitrile/README.md`, `examples/acetonitrile/output/ts_optimization_results.json` and `examples/acetonitrile/run_example.sh`).

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

  • “/chem-ts-optimization”

Requirements

  • Python 3
  • A Bash shell

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/ (Shell and 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

Chem TS Optimization loads about 876 tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 286 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~876

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). 286 words, ~876 tokens.

Download SKILL.mdSave it as .claude/skills/chem-ts-optimization/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
chem-ts-optimization
description
Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.
metadata.category
chemistry
metadata.venv
fairchem, mlip

TS Optimization with Sella

Optimize a transition-state guess and check whether it is a first-order saddle point.

Scope

  • Domain: molecular chemistry only (non-periodic systems).
  • Trigger: user has a TS guess and needs TS optimization plus frequency validation.
  • Exclusions: periodic diffusion/path workflows (use chem-neb-barrier instead).

Tool

optimize_ts_sella.py

Runs Sella TS optimization followed by finite-difference vibrations.

Use with MACE
bash
${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/scripts/optimize_ts_sella.py \
  --ts_guess ts_guess.xyz \
  --model_type mace \
  --model_name MACE-OFF23-small \
  --fmax 0.02 \
  --steps 500 \
  --imag_cutoff_cm1 -50.0 \
  --output_dir results/ts_opt
Use with FAIRChem (UMA)
bash
${CLAUDE_SKILL_DIR}/../../venv/run fairchem python ${CLAUDE_SKILL_DIR}/scripts/optimize_ts_sella.py \
  --ts_guess ts_guess.xyz \
  --model_type fairchem \
  --model_name uma-s-1p1 \
  --task_name omol \
  --fmax 0.02 \
  --steps 500 \
  --imag_cutoff_cm1 -50.0 \
  --output_dir results/ts_opt

Arguments

  • --ts_guess: required TS guess geometry (XYZ supported by ASE I/O).
  • --model_type: required backend (mace or fairchem).
  • --model_name: optional model identifier/checkpoint.
  • --task_name: optional model head/task (for UMA molecular runs use omol).
  • --device: auto|cpu|cuda (default auto).
  • --fmax: Sella convergence threshold in eV/A (default 0.02).
  • --steps: maximum TS optimization steps (default 500).
  • --vib_delta: finite-difference displacement in A (default 0.01).
  • --vib_nfree: finite-difference stencil size (2 or 4, default 2).
  • --imag_cutoff_cm1: imaginary mode cutoff in cm^-1 (default -50.0).
  • --keep_vib_cache: optional flag to keep vibration cache files in output_dir/vib.
  • --output_dir: required output directory.

Outputs

  • ts_optimized.xyz: optimized TS geometry.
  • ts_opt.traj: TS optimization trajectory.
  • ts_opt.log: optimizer log.
  • ts_optimization_results.json: run summary and pass/fail decision.
  • vib/ cache files only when --keep_vib_cache is set.

ts_optimization_results.json fields include:

  • run/model metadata
  • convergence (sella_converged, optimization_steps, max_force_eV_per_A)
  • vibrational data (all_frequencies_cm1, imaginary_modes)
  • classification (n_imag_below_cutoff, is_first_order_saddle)

TS Pass Criterion

A structure is accepted as first-order saddle only if:

  • exactly one frequency satisfies frequency < imag_cutoff_cm1

Default criterion: exactly one mode below -50 cm^-1.

Model Guidance

  • Recommended for molecules:
    • MACE-OFF23-small / MACE-OFF23-medium
    • uma-s-1p1 with --task_name omol
  • Use the same backend/model/head across reactant/product/TS optimization and TS validation.

Prerequisites And Constraints

  • Run in mlip (MACE) or fairchem (FairChem) depending on backend (venv/run <venv> ...).
  • Script enforces pbc=False (non-periodic only).
  • TS guess quality matters; poor guesses can converge to minima or higher-order saddles.

Examples

See examples/ directory for sample inputs and outputs.

Author: Juno Nam Contact: GitHub @recisic

© 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 9 other files (scripts) in skills/chem-ts-optimization of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/acetonitrile/README.md
  • examples/acetonitrile/output/ts_opt.traj
  • examples/acetonitrile/output/ts_optimization_results.json
  • examples/acetonitrile/output/ts_optimized.xyz
  • examples/acetonitrile/product_ch3nc.xyz
  • examples/acetonitrile/reactant_ch3cn.xyz
  • examples/acetonitrile/run_example.sh
  • examples/acetonitrile/ts_guess.xyz
  • scripts/optimize_ts_sella.py

Open the folder on GitHubat commit 7f2d86d

Compare with similar skills

Chem TS Optimization 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.

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Prompt Optimizeraffaan-m/ECC274k2 repos~2.4kAutomated safety check: PassMIT
Cost Optimizeruvnet/ruflo74k—~997Automated safety check: NotesMIT

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Questions about Chem TS Optimization

What does Chem TS Optimization do?

Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes. Chem TS Optimization is an agent skill from learningmatter-mit/AtomisticSkills. Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.

How do I install Chem TS Optimization in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-ts-optimization -a claude-code`. Or copy the skill folder (skills/chem-ts-optimization in learningmatter-mit/AtomisticSkills) into .claude/skills/chem-ts-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Chem TS Optimization in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-ts-optimization -a codex`. Or copy the skill folder (skills/chem-ts-optimization in learningmatter-mit/AtomisticSkills) into .agents/skills/chem-ts-optimization in your project. Codex loads it when a task matches its description.

Can I use Chem TS Optimization 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 chem-ts-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chem-ts-optimization, .gemini/skills/chem-ts-optimization, .github/skills/chem-ts-optimization and .opencode/skills/chem-ts-optimization in your project.

What does Chem TS Optimization need to run?

Going by SKILL.md and its folder, Chem TS Optimization needs a shell and Python for the scripts in its folder. Our summary lists: Python 3; A Bash shell.

Does Chem TS Optimization 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 Chem TS Optimization 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 Chem TS Optimization use?

Chem TS Optimization 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 Chem TS Optimization use?

About 876 tokens (SKILL.md is roughly 3.5k 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 Chem TS Optimization?

Skills that share tags, products or a category with Chem TS Optimization: SQL Optimization (github/awesome-copilot, 40k stars), Agent Performance Optimizer (ruvnet/ruflo, 74k stars), Database Optimizer (davila7/claude-code-templates, 32k stars) and Prompt Optimizer (affaan-m/ECC, 274k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chem TS Optimization?

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.