Pydaqua SpaceAutonomySkill
PassAudited by ClawScan on May 1, 2026.
Overview
This appears to be a local terrain-classification simulation with no evidence of credential access, data exfiltration, persistence, or system-changing behavior.
This skill looks safe to review as a local simulation, but verify any Python/numpy setup yourself and do not rely on its safety claims for real autonomous navigation or hardware control.
Findings (2)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
The skill may not run unless Python and numpy are available, and users may need to decide how to install that dependency safely.
The code depends on numpy, while the registry metadata declares no required binaries, environment variables, config paths, or install specification. This is a dependency/provenance completeness issue, not evidence of malicious behavior.
import numpy as np
Declare Python/numpy requirements in metadata or documentation, and install dependencies only from trusted package sources.
A user could over-trust the simulation if they read the safety wording as a guarantee of real-world reliability.
The artifacts make strong safety claims for an autonomous-navigation simulation. The code appears limited to printing decisions, but users should not treat the wording as validated safety assurance for real navigation.
It emphasizes **highest safety** by implementing strict confidence thresholds. If the quantum classifier is uncertain, the agent triggers a failsafe "SAFE MODE".
Treat this as a demo or simulation unless independently validated, and avoid connecting its output directly to real systems without separate safety controls.