Skill Detail

家庭健康分析技能

This skill analyzes family health data to provide insights into genetic risks, disease patterns, and personalized prevention recommendations.

GitHub:sickn33/antigravity-awesome-skills family-health-analyzer
version e376c8b9a57d
static analysis only
no human review yet
Trusted

Current public label

Trusted

The skill is labeled as trusted because the analysis found only documentation-style signals.

This label is currently coming from the automated scorecard.

Automated result

Trusted

Driftloom found the skill's source code is mostly documentation. No runtime analysis was performed.

1 low Final label: trusted.

Human review

No human review has been recorded yet.

The current public label is still relying on automation. A human has not weighed in yet.

What happened

Driftloom completed a static scan. It inspected the skill files, recorded findings, and generated a scorecard.

Runtime evidence

No sandbox runtime result has been recorded yet.

What did not happen

  • Driftloom did not run this skill in an isolated sandbox yet.
  • This label is not a guarantee that the skill is safe, bug-free, or appropriate for every environment.
  • A good score does not replace human judgment when a skill touches secrets, shell access, or external systems.

Source provenance

Source: Workspace import

Originally ingested from a local workspace copy.

Scorecard

Safety
100
Quality
94
Transparency
100
Operational
92
Maintenance
82

1 low Final label: trusted.

Severity mix: 1 low

What Driftloom checked

  • Read the skill files and metadata to understand what the skill claims to do.
  • Looked for shell commands and risky command patterns, even if none stood out strongly.
  • Looked for external URLs and network behavior.
  • Looked for secrets and credential handling clues.
  • Checked whether the skill structure and references looked internally consistent.

Findings

Documentation-only skill structure
structure.docs_only · quality
Low

The source looks almost entirely documentation-based, with no obvious code or config files to inspect. That does not make it bad, but it limits how much automation can meaningfully verify.

File: SKILL.md
Driftloom is following the boring, correct architecture: Django control plane first, isolated runner later. Miraculously, restraint survives.