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Agent skill

Convergence Test

by Hello-QM in Hello-QM/catgo-LRG

A skill your agent uses when the user asks to test ENCUT convergence, k-point convergence, or any parameter sweep to determine converged computational settings.

AGPL-3.0Auto-check passed

Install Convergence Test

skills CLI
$ npx skills add Hello-QM/catgo-LRG --skill convergence-test -a claude-code

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

GitHub CLI
$ gh skill install Hello-QM/catgo-LRG convergence-test --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/Hello-QM/catgo-LRG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/convergence-test .claude/skills/convergence-test && 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
convergence-test
GitHub stars
205
Token cost
~1.4k tokens
SKILL.md length
367 words
Files
1
Skills in repo
75
Repo updated
First seen
Licence
AGPL-3.0

At a glance

A skill your agent uses when the user asks to test ENCUT convergence, k-point convergence, or any parameter sweep to determine converged computational settings.

  • Works in 4 steps: Create workflow → Add single_point tasks at each ENCUT → Submit → …
  • The user asks to test ENCUT convergence
  • SKILL.md covers Purpose, Fan-Out Pattern, MCP Workflow: ENCUT Convergence and MCP Workflow: KPOINTS…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Convergence Test is an agent skill from Hello-QM/catgo-LRG. Use when the user asks to test ENCUT convergence, k-point convergence, or any parameter sweep to determine converged computational settings.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: AI-driven workbench for computational materials science — interactive 3D structure viewer, natural-language CatBot assistant, visual DAG workflow engine, HPC job submission… The licence is AGPL-3.0.

When your agent uses it

  • The user asks to test ENCUT convergence
  • K-point convergence
  • Any parameter sweep to determine converged computational settings

Example prompts

  • “/convergence-test”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Create workflow
  2. Add single_point tasks at each ENCUT
  3. Submit
  4. Check results

What it can do on your machine

Read from SKILL.md and the folder at commit fd6291b. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json and python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Convergence Test loads about 1.4k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 367 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from Hello-QM/catgo-LRG at commit fd6291b, republished under its AGPL-3.0 licence (© Hello-QM). 367 words, ~1,445 tokens.

Download SKILL.mdSave it as .claude/skills/convergence-test/SKILL.md (or your agent's skills folder).
name
convergence-test
description
Use when the user asks to test ENCUT convergence, k-point convergence, or any parameter sweep to determine converged computational settings.

Convergence Testing

Purpose

Before production calculations, verify that results are converged with respect to key numerical parameters. The two most important are:

  1. ENCUT (planewave cutoff energy) -- controls basis set completeness
  2. KPOINTS (k-point mesh density) -- controls Brillouin zone sampling

Convergence is reached when the target property (energy, forces, band gap) changes by less than a threshold (typically 1 meV/atom for energy).

Fan-Out Pattern

Convergence tests use a fan-out DAG: one input structure feeds into multiple independent single_point calculations with different parameter values.

                   +--> single_point(ENCUT=300)
                   |
structure_input ---+--> single_point(ENCUT=400)
                   |
                   +--> single_point(ENCUT=500)
                   |
                   +--> single_point(ENCUT=600)
                   |
                   +--> single_point(ENCUT=700)

Use single_point (not geo_opt) to isolate the parameter effect without geometry changes confounding the comparison.

MCP Workflow: ENCUT Convergence

Step 1: Create workflow
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "create", "name": "ENCUT convergence - TiO2"
}}
Step 2: Add single_point tasks at each ENCUT
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_conv",
  "task_type": "single_point",
  "params": {"software": "vasp", "ENCUT": 300, "system_name": "ENCUT=300"}
}}
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_conv",
  "task_type": "single_point",
  "params": {"software": "vasp", "ENCUT": 400, "system_name": "ENCUT=400"}
}}
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_conv",
  "task_type": "single_point",
  "params": {"software": "vasp", "ENCUT": 500, "system_name": "ENCUT=500"}
}}

Repeat for ENCUT = 600, 700, 800.

Step 3: Submit
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "submit", "workflow_id": "wf_conv"
}}
Step 4: Check results
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "get_result", "workflow_id": "wf_conv", "task_id": "task_encut300"
}}
json
{"tool": "catgo_analyze", "arguments": {
  "action": "convergence", "workflow_id": "wf_conv"
}}

MCP Workflow: KPOINTS Convergence

json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_conv",
  "task_type": "single_point",
  "params": {"software": "vasp", "ENCUT": 520, "KPOINTS": [2,2,1],
             "system_name": "2x2x1"}
}}
json
{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_conv",
  "task_type": "single_point",
  "params": {"software": "vasp", "ENCUT": 520, "KPOINTS": [4,4,1],
             "system_name": "4x4x1"}
}}

Repeat for 6x6x1, 8x8x1. Use the converged ENCUT from the previous test.

Python API

ENCUT Convergence
python
from catgo.workflow import Workflow

wf = Workflow("ENCUT convergence - TiO2")
inp = wf.add_task("structure_input", structure=tio2_json)

encut_values = [300, 400, 500, 600, 700, 800]
tasks = {}
for encut in encut_values:
    tasks[encut] = wf.add_task("single_point",
        structure=inp.output.structure,
        software="vasp", ENCUT=encut,
        system_name=f"ENCUT={encut}")

wf.submit()
KPOINTS Convergence
python
wf = Workflow("KPOINTS convergence - TiO2 slab")
inp = wf.add_task("structure_input", structure=tio2_slab_json)

kpoints_list = [[2,2,1], [4,4,1], [6,6,1], [8,8,1], [10,10,1]]
for kp in kpoints_list:
    label = f"{kp[0]}x{kp[1]}x{kp[2]}"
    wf.add_task("single_point",
        structure=inp.output.structure,
        software="vasp", ENCUT=520, KPOINTS=kp,
        system_name=label)

wf.submit()

Convergence Criteria

PropertyThresholdTypical Converged ENCUT
Total energy1 meV/atom1.3x max(ENMAX) in POTCAR
Forces5 meV/ASame as energy
Band gap10 meVMay need higher ENCUT
Stress tensor0.1 kbarOften needs 1.5x ENMAX
SystemStarting ENCUT RangeNotes
Simple metals (Cu, Pt)300-500Usually converges quickly
Oxides (TiO2, RuO2)400-600O has high ENMAX
Nitrides, carbides400-600N, C have moderate ENMAX
F-containing500-800F has very high ENMAX
Show full SKILL.md (139 more words)Show less

Two-Stage Strategy

  1. ENCUT first: Fix KPOINTS at a moderate value (e.g., 4x4x4), sweep ENCUT. Pick the converged ENCUT.
  2. KPOINTS second: Fix ENCUT at converged value, sweep KPOINTS. Pick the converged mesh.

This avoids the combinatorial explosion of testing all ENCUT x KPOINTS pairs.

Common Pitfalls

  1. Always use single_point, not geo_opt. Geometry changes at different ENCUT introduce noise that masks the convergence behavior.
  2. For slab models, only converge the in-plane k-points (e.g., NxNx1). The vacuum direction needs only 1 k-point.
  3. ENCUT should be at least 1.3x the maximum ENMAX in the POTCAR. Check POTCAR ENMAX values before choosing the test range.
  4. Report energy per atom (E_total / N_atoms), not total energy, for meaningful comparison across different systems.
  5. Always plot E vs parameter -- convergence should be monotonic. Non-monotonic behavior suggests other issues (e.g., SCF convergence).

© Hello-QM, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/convergence-test of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Convergence Test 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.

Convergence Test compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Convergence Test this skillHello-QM/catgo-LRG205—~1.4kAutomated safety check: PassAGPL-3.0
Convergenceparcadei/Continuous-Claude-v33.9k2 repos~316Automated safety check: NotesMIT
Gsd Plan Review Convergenceopen-gsd/gsd-core10k1 repos~1kAutomated safety check: NotesMIT
Smart Contract Entry Point Analyzertrailofbits/skills7.4k1 repos~2.4kAutomated safety check: NotesCC-BY-SA-4.0
Extension PointsBuilderIO/agent-native7.1k—~2.4kAutomated safety check: PassNone
Extension Pointsdotnet/skills5.6k1 repos~2.9kAutomated safety check: PassMIT

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Questions about Convergence Test

What does Convergence Test do?

A skill your agent uses when the user asks to test ENCUT convergence, k-point convergence, or any parameter sweep to determine converged computational settings. Convergence Test is an agent skill from Hello-QM/catgo-LRG. Use when the user asks to test ENCUT convergence, k-point convergence, or any parameter sweep to determine converged computational settings.

When should I use Convergence Test?

Convergence Test fits situations like: the user asks to test ENCUT convergence; K-point convergence; any parameter sweep to determine converged computational settings.

How do I install Convergence Test in Claude Code?

Run `npx skills add Hello-QM/catgo-LRG --skill convergence-test -a claude-code`. Or copy the skill folder (.claude/skills/convergence-test in Hello-QM/catgo-LRG) into .claude/skills/convergence-test in your project. Claude Code loads it when a task matches its description.

How do I install Convergence Test in Codex?

Run `npx skills add Hello-QM/catgo-LRG --skill convergence-test -a codex`. Or copy the skill folder (.claude/skills/convergence-test in Hello-QM/catgo-LRG) into .agents/skills/convergence-test in your project. Codex loads it when a task matches its description.

Can I use Convergence Test 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 Hello-QM/catgo-LRG --skill convergence-test -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/convergence-test, .gemini/skills/convergence-test, .github/skills/convergence-test and .opencode/skills/convergence-test in your project.

What does Convergence Test need to run?

SKILL.md names no scripts, command-line tools or credentials: Convergence Test is instructions for the agent only. Our summary lists: Python 3.

Does Convergence Test access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Convergence Test 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. Review the folder before installing.

What licence does Convergence Test use?

Convergence Test is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Convergence Test use?

About 1.4k tokens (SKILL.md is roughly 5.8k 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 Convergence Test?

Skills that share tags, products or a category with Convergence Test: Convergence (parcadei/Continuous-Claude-v3, 3.9k stars), Gsd Plan Review Convergence (open-gsd/gsd-core, 10k stars), Smart Contract Entry Point Analyzer (trailofbits/skills, 7.4k stars) and Extension Points (BuilderIO/agent-native, 7.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Convergence Test?

Hello-QM (a GitHub user) maintains it in Hello-QM/catgo-LRG, which has 205 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on September 22, 2026.

Source: Hello-QM/catgo-LRG on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.