trust-calibration

trust-calibration is a UX design & wireframing skill for AI agents, published by Owl-Listener in the ai-design-skills repository on GitHub. Skillspace rates trust-calibration D (65/100) and it passed the safety scan; the rating is based on static analysis only.

Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.

Grade
D
Quality
65 / 100
Safety
Passed
GitHub stars
142

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubOwl-Listener/ai-design-skills

Our review

Architecture
94 / 100
Maturity
Basic: a single SKILL.md
Security scan
No issues found
License
MIT
Adoption
Adopted by 1 other repositories, in 1 collections
Scope
Portable: works in any project. Generic AI trust-calibration UX design principle.

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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