Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Autonomous space navigation agent using optical quantum kernels for terrain classification.
Autonomous space navigation agent using optical quantum kernels for terrain classification.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
This skill simulates an autonomous agent for space exploration that uses Optical Quantum Kernels to classify terrain. It emphasizes highest safety by implementing strict confidence thresholds. If the quantum classifier is uncertain, the agent triggers a failsafe "SAFE MODE".
Quantum Perception: Uses simulated optical quantum interference to recognize terrain features. Safety Failsafe: Automatically halts if classification confidence is below 0.8. Autonomous Decision Making: Decides to "Navigate" or "Avoid" based on quantum kernel results.
navigate: Process a sensor reading and decide on an action.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.