Agent skill

Pointcloud Generator

Apache-2.0Auto-check passed

Install Pointcloud Generator

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a claude-code

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

GitHub CLI

$ gh skill install lamm-mit/scienceclaw pointcloud-generator --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/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pointcloud-generator .claude/skills/pointcloud-generator && 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/

Paste into Claude Code

Install the "pointcloud-generator" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/pointcloud-generator into .claude/skills/pointcloud-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pointcloud-generator", 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

$skill-installer install https://github.com/lamm-mit/scienceclaw/tree/main/skills/pointcloud-generator

Type 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.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw pointcloud-generator --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pointcloud-generator .agents/skills/pointcloud-generator && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "pointcloud-generator" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/pointcloud-generator into .agents/skills/pointcloud-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pointcloud-generator", 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.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw pointcloud-generator --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pointcloud-generator .cursor/skills/pointcloud-generator && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "pointcloud-generator" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/pointcloud-generator into .cursor/skills/pointcloud-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pointcloud-generator", 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

$ gemini skills install https://github.com/lamm-mit/scienceclaw.git --path skills/pointcloud-generator

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw pointcloud-generator --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pointcloud-generator .gemini/skills/pointcloud-generator && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "pointcloud-generator" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/pointcloud-generator into .gemini/skills/pointcloud-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pointcloud-generator", 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.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw pointcloud-generator

Installs 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).

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pointcloud-generator .github/skills/pointcloud-generator && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "pointcloud-generator" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/pointcloud-generator into .github/skills/pointcloud-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pointcloud-generator", 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.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw pointcloud-generator --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pointcloud-generator .opencode/skills/pointcloud-generator && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "pointcloud-generator" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/pointcloud-generator into .opencode/skills/pointcloud-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pointcloud-generator", 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: pointcloud-generator
GitHub stars: 244
Token cost: ~963 tokens
SKILL.md length: 167 words
Files: 3 (incl. scripts)
Skills in repo: 85
Repo updated: Aug 21, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

SKILL.md covers Usage, Output JSON (stdout), Output Files and Geometry Parameters (from spec), plus 2 more sections
Runs Python scripts from its folder; calls python3 and jq

What it does

Pointcloud Generator is an agent skill from lamm-mit/scienceclaw. Generate a colour-coded 3D point cloud (.xyz + .pcd) for bioinspired hierarchical ribbed membrane lattices — Cricket wing harp layer, Cicada tymbal corrugation layer, and multi-scale hierarchical lattice layer. Each structural element is analytically sampled (pure numpy, no LLM, no OpenSCAD) and assigned a distinct RGB colour. Produces ASCII XYZ, PCL v0.7 ASCII PCD, and a 4-panel PNG (isometric, top-XY, side-XZ, side-YZ). Chainable downstream of pointcloud-generator or upstream of fem-analysis.

Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/pointcloud_generator.py`).

It works with NumPy. The licence is Apache-2.0.

Example prompts

“/pointcloud-generator”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. 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 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python3
- jq
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

Pointcloud Generator loads about 963 tokens when it runs. Until then it costs about 130 tokens; SKILL.md has 167 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~130
When it runs · the whole SKILL.md, loaded when a task matches: ~963

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 lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 167 words, ~963 tokens.

Download SKILL.mdSave it as .claude/skills/pointcloud-generator/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.

name: pointcloud-generator
description: Generate a colour-coded 3D point cloud (.xyz + .pcd) for bioinspired hierarchical ribbed membrane lattices — Cricket wing harp layer, Cicada tymbal corrugation layer, and multi-scale hierarchical lattice layer. Each structural element is analytically sampled (pure numpy, no LLM, no OpenSCAD) and assigned a distinct RGB colour. Produces ASCII XYZ, PCL v0.7 ASCII PCD, and a 4-panel PNG (isometric, top-XY, side-XZ, side-YZ). Chainable downstream of pointcloud-generator or upstream of fem-analysis.
metadata.category: materials-design
metadata.requires: numpy, matplotlib, mpl_toolkits

Point Cloud Generator

Generates a 3D point cloud for a Hierarchical Ribbed Membrane Lattice inspired by Gryllus bimaculatus (cricket wing harp) and cicada tymbal geometry.

Three structurally distinct Z-layers are sampled analytically:

Layer	Z offset	Structural feature	Colour
Cricket harp	0 mm	Base membrane + diagonal file ridge + parallel harp veins	grey / red / orange
Cicada tymbal	+1.5 mm	Cosine-graded corrugation ribs (tall centre, zero at edges)	blue
Hierarchical lattice	+3.0 mm	Primary / secondary / tertiary rib scales (3 densities)	dark / mid / light green

Usage

bash

# From inline JSON spec
python3 {baseDir}/scripts/pointcloud_generator.py \
  --spec '{"biological_inspiration":"Cricket wing harp + Cicada tymbal",
           "rib_spacing_mm":2.5,"thickness_mm":0.4,"aspect_ratio":2.5,"num_scales":3}' \
  --output-dir /tmp/pointcloud_out

# From spec file
python3 {baseDir}/scripts/pointcloud_generator.py \
  --spec-file /path/to/spec.json \
  --output-dir /tmp/pointcloud_out

Output JSON (stdout)

json

{
  "xyz_path":  "/tmp/pointcloud_out/membrane_lattice.xyz",
  "pcd_path":  "/tmp/pointcloud_out/membrane_lattice.pcd",
  "png_path":  "/tmp/pointcloud_out/pointcloud_views.png",
  "total_points": 18348,
  "bounding_box_mm": {"x_min":0,"x_max":50,"y_min":0,"y_max":120,"z_min":-0.07,"z_max":3.80},
  "layers": {
    "cricket_harp":       {"points": 11496, "z_mm": 0.0},
    "cicada_tymbal":      {"points": 3000,  "z_mm": 1.5},
    "hierarchical_lattice":{"points": 3852, "z_mm": 3.0}
  }
}

Output Files

File	Format	Description
`membrane_lattice.xyz`	ASCII `x y z r g b`	Standard XYZ+RGB, one point per line
`membrane_lattice.pcd`	PCL v0.7 ASCII	Compatible with PCL, CloudCompare, Open3D
`pointcloud_views.png`	PNG	4-panel matplotlib figure, dark background

Geometry Parameters (from spec)

Field	Default	Effect
`rib_spacing_mm`	2.5	Primary rib pitch; secondary = ÷3, tertiary = ÷6
`thickness_mm`	0.4	Base membrane thickness; controls Z roughness
`aspect_ratio`	2.5	H = W × aspect_ratio (W fixed at 50 mm)
`num_scales`	3	Number of rib hierarchy levels (2 or 3)

Chaining

bash

# Generate point cloud then run FEM on same spec
PCD=$(python3 skills/pointcloud-generator/scripts/pointcloud_generator.py \
        --spec '{"rib_spacing_mm":2.5,"thickness_mm":0.4}' \
        --output-dir /tmp/out | jq -r '.pcd_path')

python3 skills/fem-analysis/scripts/mechanism_analysis.py \
  --stl /tmp/out/membrane_lattice.xyz \   # FEM reads bounding box from any 3D file
  --topology cricket_harp \
  --rib-spacing-mm 2.5 --rib-height-mm 0.8

Colour Legend

■ grey   (180,180,180)  Base membrane
■ red    (220, 50, 50)  Diagonal file ridge (cricket harp)
■ orange (220,140, 50)  Harp veins (X-parallel)
■ blue   ( 50,100,220)  Cicada tymbal corrugation ribs (graded height)
■ dark ■ (20,120,50)    Primary ribs — level 1 (coarse, 2.5 mm pitch)
■ mid  ■ (80,180,80)    Secondary ribs — level 2 (medium, 0.83 mm pitch)
■ light■ (160,220,160)  Tertiary ribs — level 3 (fine, 0.42 mm pitch)

Files

SKILL.md and 2 other files (scripts) in skills/pointcloud-generator of lamm-mit/scienceclaw.

SKILL.md
scripts/__pycache__/pointcloud_generator.cpython-313.pyc
scripts/pointcloud_generator.py

Open the folder on GitHubat commit ab9aba1

Compare with similar skills

Pointcloud Generator 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.

Pointcloud Generator compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Pointcloud Generator this skilllamm-mit/scienceclaw	244	—	~963	Automated safety check: Pass	Apache-2.0	1 mo ago
Cantera Ignition DelayK-Dense-AI/scientific-agent-skills	48k	2 repos	~2.2k	Automated safety check: Pass	MIT	2 days ago
Tushare Datazillionare/zillionare	319	2 repos	~2.3k	Automated safety check: Pass	None	today
Exploratory Data AnalysisOleafly/Oleafly	206	3 repos	~3.4k	Automated safety check: Notes	MIT	today
FAISS Similarity SearchOrchestra-Research/AI-Research-SKILLs	13k	7 repos	~1.3k	Automated safety check: Pass	MIT	3 mo ago
Python Performance Optimizationwshobson/agents	40k	13 repos	~814	Automated safety check: Pass	MIT	3 days ago

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Questions about Pointcloud Generator

What does Pointcloud Generator do?

Generate a colour-coded 3D point cloud (.xyz + .pcd) for bioinspired hierarchical ribbed membrane lattices — Cricket wing harp layer, Cicada tymbal corrugation layer, and multi-scale hierarchical…. Pointcloud Generator is an agent skill from lamm-mit/scienceclaw.pcd) for bioinspired hierarchical ribbed membrane lattices — Cricket wing harp layer, Cicada tymbal corrugation layer, and multi-scale hierarchical lattice layer.

How do I install Pointcloud Generator in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a claude-code`. Or copy the skill folder (skills/pointcloud-generator in lamm-mit/scienceclaw) into .claude/skills/pointcloud-generator in your project. Claude Code loads it when a task matches its description.

How do I install Pointcloud Generator in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill pointcloud-generator -a codex`. Or copy the skill folder (skills/pointcloud-generator in lamm-mit/scienceclaw) into .agents/skills/pointcloud-generator in your project. Codex loads it when a task matches its description.

Can I use Pointcloud Generator 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 lamm-mit/scienceclaw --skill pointcloud-generator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pointcloud-generator, .gemini/skills/pointcloud-generator, .github/skills/pointcloud-generator and .opencode/skills/pointcloud-generator in your project.

What does Pointcloud Generator need to run?

Going by SKILL.md and its folder, Pointcloud Generator needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and jq). Our summary lists: Python 3.

Does Pointcloud Generator 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 Pointcloud Generator 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 Pointcloud Generator use?

Pointcloud Generator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pointcloud Generator use?

About 963 tokens (SKILL.md is roughly 3.9k 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 Pointcloud Generator?

Skills that share tags, products or a category with Pointcloud Generator: Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars), Tushare Data (zillionare/zillionare, 319 stars), Exploratory Data Analysis (Oleafly/Oleafly, 206 stars) and FAISS Similarity Search (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pointcloud Generator?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.

Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Pointcloud Generator

Install Pointcloud Generator

Facts

At a glance

What it does

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Point Cloud Generator

Usage

Output JSON (stdout)

Output Files

Geometry Parameters (from spec)

Chaining

Colour Legend

Files

Compare with similar skills

Similar skills

Cantera Ignition Delay

Tushare Data

Exploratory Data Analysis

FAISS Similarity Search

Python Performance Optimization

13C Metabolic Flux Analysis

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Questions about Pointcloud Generator

Install Pointcloud Generator

Facts

At a glance

What it does

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Point Cloud Generator

Usage

Output JSON (stdout)

Output Files

Geometry Parameters (from spec)

Chaining

Colour Legend

Files

Compare with similar skills

Similar skills

More from lamm-mit/scienceclaw

Related

Questions about Pointcloud Generator