Skill by ara.so β Daily 2026 Skills collection.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmodly-image-to-3dExecute the skills CLI command in your project's root directory to begin installation:
Fetches modly-image-to-3d from aradotso/trending-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate modly-image-to-3d. Access via /modly-image-to-3d in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Skill by ara.so β Daily 2026 Skills collection.
Modly is a local, open-source desktop application (Windows/Linux) that converts photos into 3D mesh models using AI models running entirely on your GPU β no cloud, no API keys required.
modly/
βββ src/ # Electron + TypeScript frontend
β βββ main/ # Electron main process
β βββ renderer/ # React UI (renderer process)
β βββ preload/ # IPC bridge
βββ api/ # Python FastAPI backend
β βββ generator.py # Core generation logic
β βββ requirements.txt
βββ resources/
β βββ icons/
βββ launcher.bat # Windows quick-start
βββ launcher.sh # Linux quick-start
βββ package.json
The app runs as an Electron shell over a local Python FastAPI server. Extensions are GitHub repos with a manifest.json + generator.py that plug into the extension system.
# Windows
launcher.bat
# Linux
chmod +x launcher.sh
./launcher.sh
# 1. Clone
git clone https://github.com/lightningpixel/modly
cd modly
# 2. Install JS dependencies
npm install
# 3. Set up Python backend
cd api
python -m venv .venv
# Activate (Windows)
.venv\Scripts\activate
# Activate (Linux/macOS)
source .venv/bin/activate
pip install -r requirements.txt
cd ..
# 4. Run dev mode (starts Electron + Python backend)
npm run dev
# Build installers for current platform
npm run build
# Output goes to dist/
npm run dev # Start app in development mode (hot reload)
npm run build # Package app for distribution
npm run lint # Run ESLint
npm run typecheck # TypeScript type checking
Extensions are GitHub repositories containing:
manifest.json β metadata and model variantsgenerator.py β generation logic implementing the Modly extension interface{
"name": "My 3D Extension",
"id": "my-extension-id",
"description": "Generates 3D models using XYZ model",
"version": "1.0.0",
"author": "Your Name",
"repository": "https://github.com/yourname/my-modly-extension",
"variants": [
{
"id": "model-small",
"name": "Small (faster)",
"description": "Lighter variant for faster generation",
"size_gb": 4.2,
"vram_gb": 6,
"files": [
{
"url": "https://huggingface.co/yourorg/yourmodel/resolve/main/weights.safetensors",
"filename": "weights.safetensors",
"sha256": "abc123..."
}
]
}
]
}
# api/extensions/<extension-id>/generator.py
# Required interface every extension must implement
import sys
import json
from pathlib import Path
def generate(
image_path: str,
output_path: str,
variant_id: str,
models_dir: str,
**kwargs
) -> dict:
"""
Required entry point for all Modly extensions.
Args:
image_path: Path to input image file
output_path: Path where output .glb/.obj should be saved
variant_id: Which model variant to use
models_dir: Directory where downloaded model weights live
Returns:
dict with keys:
success (bool)
output_file (str) β path to generated mesh
error (str, optional)
"""
try:
# Load your model weights
weights = Path(models_dir) / variant_id / "weights.safetensors"
# Run your inference
mesh = run_inference(str(weights), image_path)
# Save output
mesh.export(output_path)
return {
"success": True,
"output_file": output_path
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
https://github.com/lightningpixel/modly-hunyuan3d-mini-extension| Extension | Model |
|---|---|
| modly-hunyuan3d-mini-extension | Hunyuan3D 2 Mini |
The backend runs locally. Key endpoints used by the Electron frontend:
# Typical backend route patterns (api/main.py or similar)
# GET /extensions β list installed extensions
# GET /extensions/{id} β get extension details + variants
# POST /extensions/install β install extension from GitHub URL
# POST /generate β trigger 3D generation
# GET /generate/status β poll generation progress
# GET /models β list downloaded model variants
# POST /models/download β download a model variant
// src/preload/index.ts β exposing backend calls to renderer
import { contextBridge, ipcRenderer } from 'electron'
contextBridge.exposeInMainWorld('modly', {
generate: (imagePath: string, extensionId: string, variantId: string) =>
ipcRenderer.invoke('generate', { imagePath, extensionId, variantId }),
installExtension: (repoUrl: string) =>
ipcRenderer.invoke('install-extension', { repoUrl }),
listExtensions: () =>
ipcRenderer.invoke('list-extensions'),
})
// src/main/ipc-handlers.ts β main process handling
import { ipcMain } from 'electron'
ipcMain.handle('generate', async (_event, { imagePath, extensionId, variantId }) => {
const response = await fetch('http://localhost:PORT/generate', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ image_path: imagePath, extension_id: extensionId, variant_id: variantId }βMake data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
βSave 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- βΊClaude Desktop or compatible AI client
- βΊAccess to product documentation and roadmap tools (Jira, Notion, etc.)
- βΊUnderstanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- βΊStakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- β Not validating competitive researchβverify facts before sharing
- β Accepting user stories without involving engineering team
- β Over-relying on frameworks without qualitative judgment
- β Not customizing outputs to company culture and communication style
- β Skipping stakeholder validation of generated requirements
Best Practices
β Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
β Don't
- βDon't publish competitive analysis without fact-checking
- βDon't finalize user stories without engineering review
- βDon't make prioritization decisions solely on AI scoring
- βDon't skip customer validation of generated requirements
- βDon't ignore company-specific context and culture
π‘ Pro Tips
- β
Provide context: company goals, constraints, customer feedback
- β
Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- β
Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- β
Use skill for 70% generation + 30% customization to company needs
When to Use This
β Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
β Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Productivitysame categoryReviews
4.5β
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35 reviews- OOlivia Dialloβ
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Dec 24, 2024
modly-image-to-3d fits our agent workflows well β practical, well scoped, and easy to wire into existing repos.
- GGanesh Mohaneβ
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Dec 16, 2024
modly-image-to-3d has been reliable in day-to-day use. Documentation quality is above average for community skills.
- SShikha Mishraβ
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Dec 12, 2024
Registry listing for modly-image-to-3d matched our evaluation β installs cleanly and behaves as described in the markdown.
- IIsabella Kapoorβ
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Nov 15, 2024
We added modly-image-to-3d from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- NNaina Sharmaβ
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Nov 11, 2024
Useful defaults in modly-image-to-3d β fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- YYash Thakkerβ
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Nov 3, 2024
modly-image-to-3d reduced setup friction for our internal harness; good balance of opinion and flexibility.
- DDhruvi Jainβ
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Oct 22, 2024
I recommend modly-image-to-3d for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- NNoah Ghoshβ
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Oct 6, 2024
Solid pick for teams standardizing on skills: modly-image-to-3d is focused, and the summary matches what you get after install.
- TTariq Haddadβ
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Oct 2, 2024
modly-image-to-3d has been reliable in day-to-day use. Documentation quality is above average for community skills.
- DDaniel Tandonβ
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Sep 13, 2024
I recommend modly-image-to-3d for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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