Cargo Space Packer helps you check whether a load fits, organize it by weight and density, and plan how to secure it for the trip.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cargo-space-packer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install cargo-space-packer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Cargo Space Packer?
Cargo Space Packer is a command-line planning skill for loading pickup beds, trailers, and moving trucks. It goes beyond basic volume calculations by applying an approximately 80% practical capacity ceiling, density-based stacking guidance, and a forward-biased load plan to help protect cargo and support stable handling.
Use its fit, load-map, and checklist workflows to estimate truck capacity, arrange mixed cargo, and plan tie-downs based on load weight and anchor style. Openclaw Skills users get a practical framework for planning a move or haul before cargo is loaded; always confirm vehicle payload, axle ratings, anchor ratings, and applicable regulations independently.
Cargo Space Packer Use Cases
- Estimate whether boxes and furniture fit in a pickup bed, enclosed trailer, or 10-, 15-, 20-, or 26-foot rental truck.
- Compare a cargo cube total with usable capacity using the skill’s 80% practical packing ceiling.
- Plan mixed loads containing dense boxes, appliances, furniture, and soft goods.
- Create a top-down and side-view load map with heavy cargo forward and lighter items higher or farther back.
- Estimate tie-down needs and review anchor placement for a given cargo weight and hauling surface.
- Check a proposed load for common risks, including rear-heavy placement, unsupported stacking, and insufficient restraints.
How Cargo Space Packer Works
- Describe cargo as dimension-and-count entries, such as
L120xW80xH60:2, and provide the available space dimensions. - Run the
fitcommand to compare cargo volume with the available space, accounting for the approximately 80% practical packing ceiling. - Run
loadmapto get a top-down and side-view arrangement that follows the load-pyramid guidance: heavy items low and forward, medium items in the middle, and light or bulky items higher or toward the rear. - Apply the density classes and placement rules. Keep dense items on the floor, position furniture securely, and load mattresses and sofas vertically along a wall where appropriate.
- Run
checklistwith the cargo weight and surface type to review tie-down guidance. Account for working load limits and angle-related strength loss. - Verify the plan before departure: check axle and payload limits, secure items with suitable rated anchors and straps, protect cargo from strap damage, and confirm doors close without pressing on the load.
Cargo Space Packer Setup
The documented workflow uses Python 3 and the scripts/cargo_packer.py command-line script. The SKILL.md does not specify a separate package installation or configuration process; run the script from the skill directory where that path is available.
# Check whether Python 3 is available
python3 --version
# Estimate whether cargo fits in the available space
python3 scripts/cargo_packer.py fit \
--cargo 'L120xW80xH60:2,L60xW40xH40:5' \
--space 'L400xW200xH200'
# Generate a load map with a tie-down plan
python3 scripts/cargo_packer.py loadmap \
--cargo 'L120xW80xH60:2,L60xW40xH40:5' \
--space 'L400xW200xH200'
# Review a securing checklist for a 900 lb load on a trailer
python3 scripts/cargo_packer.py checklist \
--weight-lb 900 \
--surface trailer
Replace the example dimensions, item counts, weight, and surface with the actual load details. The documented cargo format uses centimeters and LxWxH:count entries; the checklist example specifies weight in pounds.
Cargo Space Packer Data Schema & Taxonomy
The documented interface accepts cargo and space dimensions as command-line arguments. Cargo entries use this pattern:
| Field | Format | Example |
|---|---|---|
| Cargo dimensions | L<length>xW<width>xH<height> |
L120xW80xH60 |
| Item count | :<count> after dimensions |
L120xW80xH60:2 |
| Multiple cargo entries | Comma-separated entries | L120xW80xH60:2,L60xW40xH40:5 |
| Available space | L<length>xW<width>xH<height> |
L400xW200xH200 |
| Checklist weight | --weight-lb <value> |
--weight-lb 900 |
| Hauling surface | --surface <type> |
--surface trailer |
The skill organizes placement advice into density classes: A (dense), B (medium), C (light), F (furniture), and F-on-end (vertical furniture such as mattresses and sofas). It documents three command workflows—fit, loadmap, and checklist—but does not specify a persistent data store or files generated by those commands. Treat their results as planning guidance and verify load limits and restraint ratings against the actual vehicle and equipment.
Cargo Space Packer Advanced Features
- Practical capacity planning: Uses an approximately 80% ceiling rather than assuming every cubic inch of truck space is usable.
- Density-based stacking: Separates dense, medium, light, furniture, and on-end furniture classes, with guidance on what can safely sit above each class.
- Load-pyramid placement: Prioritizes heavy, sturdy items low and forward, with a 60/40 front-biased weight-distribution guideline.
- Visual load planning: The
loadmapworkflow is documented to provide top-down and side views plus a tie-down plan. - Tie-down calculations: Applies a conservative total working-load-limit floor of 2× cargo weight, notes roughly 70% WLL retention at a 45-degree strap angle, and gives practical strap-count and anchor-spacing guidance.
- Surface-aware checklist: The
checklistcommand accepts cargo weight and a hauling surface to produce securing guidance. - Damage and stability checks: Calls attention to crushed boxes, unprotected strap contact, rear-heavy loads, and relying on friction instead of restraints.
- No documented hooks or multi-agent workflow: The supplied SKILL.md describes a local Python CLI workflow and does not specify event hooks, automations, or multi-agent coordination.
SKILL.md
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