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space-autonomy-quantum

Autonomous space navigation agent using optical quantum kernels for terrain classification.

aadipapp0 source installs☆ 0 Repository starsv0.1.0

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install aadipapp/space-autonomy-skill

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

Space Autonomy Quantum Skill

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".

Features

  • 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.

Commands

  • navigate: Process a sensor reading and decide on an action.

Source information

Source
github
Repository
openclaw/skills
Path
skills/aadipapp/space-autonomy-skill
Version
0.1.0
Commit
00afc94bca7b9307b6c6c4991144f3ed11507f63
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

README

Space Autonomy Quantum Skill

This skill simulates an autonomous agent for diverse space environments. It relies on Optical Quantum Kernels to classify terrain based on sensor data.

Safety First

Space is unforgiving. This agent implements a "Highest Safety" protocol:

  1. Failsafe Threshold: If the quantum classifier's confidence is below 0.85, the agent immediately triggers SAFE MODE.
  2. Redundancy: Every classification is computed 3 times and averaged to mitigate quantum/optical noise.
  3. Default to Halt: If a hazard is detected OR if the terrain is unknown, the agent stops.

How it Works

The agent compares real-time sensor inputs against a "Knowledge Base" (Safe Ground, Rocks, Voids) using a simulated optical quantum computer.

  • High Kernel Value -> High Similarity -> Confident Classification.
  • Low Kernel Value -> Low Similarity -> Unknown Terrain -> Safe Mode.

Usage

Simulate a sensor reading (vector of 3 values):

Case 1: Clear, safe terrain (matches 'SAFE_FLAT')

python3 scripts/quantum_nav.py "0.1,0.12,0.1"
# Expected: Action: PROCEED

Case 2: Hazardous Rock (matches 'HAZARD_ROCK')

python3 scripts/quantum_nav.py "0.8,0.9,0.7"
# Expected: Action: AVOID / HALT

Case 3: Unknown/Ambiguous Signal

python3 scripts/quantum_nav.py "0.5,0.5,0.5"
# Expected: >>> TRIGGERING FAILSAFE: SAFE MODE <<<

Publishing

clawhub publish

Extracted content

SKILL.md
# Space Autonomy Quantum Skill

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".

## Features
- **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.

## Commands

- `navigate`: Process a sensor reading and decide on an action.
README
# Space Autonomy Quantum Skill

This skill simulates an autonomous agent for diverse space environments. It relies on **Optical Quantum Kernels** to classify terrain based on sensor data.

## Safety First
Space is unforgiving. This agent implements a **"Highest Safety"** protocol:
1.  **Failsafe Threshold**: If the quantum classifier's confidence is below `0.85`, the agent immediately triggers **SAFE MODE**.
2.  **Redundancy**: Every classification is computed 3 times and averaged to mitigate quantum/optical noise.
3.  **Default to Halt**: If a hazard is detected OR if the terrain is unknown, the agent stops.

## How it Works
The agent compares real-time sensor inputs against a "Knowledge Base" (Safe Ground, Rocks, Voids) using a simulated optical quantum computer.
- **High Kernel Value** -> High Similarity -> Confident Classification.
- **Low Kernel Value** -> Low Similarity -> Unknown Terrain -> Safe Mode.

## Usage

Simulate a sensor reading (vector of 3 values):

# Case 1: Clear, safe terrain (matches 'SAFE_FLAT')
```bash
python3 scripts/quantum_nav.py "0.1,0.12,0.1"
# Expected: Action: PROCEED
```

# Case 2: Hazardous Rock (matches 'HAZARD_ROCK')
```bash
python3 scripts/quantum_nav.py "0.8,0.9,0.7"
# Expected: Action: AVOID / HALT
```

# Case 3: Unknown/Ambiguous Signal
```bash
python3 scripts/quantum_nav.py "0.5,0.5,0.5"
# Expected: >>> TRIGGERING FAILSAFE: SAFE MODE <<<
```

## Publishing
```bash
clawhub publish
```

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