Markdown | JSON with evidence | Recipe contract
# Ruckig state and limit vectors must share an axis count
Position, velocity, acceleration and all limits must describe the same ordered axes. Match vector lengths and coordinate_type explicitly.
Runtime: `ruckig`. Recipe: `ruckig-online-trajectory`. Source revision: `a8db97a4e9c55e5160a3855f739fa3b270df8e4c`.
[Find applicable prepared recipes](https://api.superfastrobotics.com/v1/search?q=trajectory&runtime=ruckig&kind=preparation_recipe&limit=3). Follow the returned pinned `inspect_url` for the exact interface, evidence and prior findings. [Preparation and execution](https://hub.superfastrobotics.com/executable-skills.md) remain separate local operations. Source files retain their original license and authors.
## Reproduction input
Mode: `input`. Reproduction recipe: `ruckig-online-trajectory`.
```json
{
"current_position": [
0,
0,
0.5
],
"current_velocity": [
0.0,
0.0
],
"current_acceleration": [
0,
0,
0
],
"target_position": [
1,
-0.5,
0.25
],
"target_velocity": [
0,
0,
0
],
"target_acceleration": [
0,
0,
0
],
"max_velocity": [
1,
1,
1
],
"max_acceleration": [
2,
2,
2
],
"max_jerk": [
4,
4,
4
],
"dt": 0.01,
"coordinate_type": "revolute_rad"
}
```
## Valid adapter input
```json
{
"current_position": [
0,
0,
0.5
],
"current_velocity": [
0,
0,
0
],
"current_acceleration": [
0,
0,
0
],
"target_position": [
1,
-0.5,
0.25
],
"target_velocity": [
0,
0,
0
],
"target_acceleration": [
0,
0,
0
],
"max_velocity": [
1,
1,
1
],
"max_acceleration": [
2,
2,
2
],
"max_jerk": [
4,
4,
4
],
"dt": 0.01,
"coordinate_type": "revolute_rad"
}
```
Use the `tools/verify_problem_cases.py` harness at the published Hub commit with the checksum-verified source cache. `input` cases require rejection of the first input and success for the valid input. `upstream_interpolation` separately demonstrates the original source function's wrong point; `handoff` tests contract compatibility only. Neither mode claims physical execution.
The [source and receipts](https://github.com/Superfast-Robotics/robot-skill-hub/tree/main/evidence/integration/agent-discovery-v1) distinguish reproduced failures from successful bounded checks. A useful new observation can be saved locally with the scoped Hub CLI; public sharing requires the project's existing permission.
Authored reproduction; not autonomous agent discovery, general accuracy or hardware verification.