the-invisible-eye

Translate surveillance mechanisms into felt lived experiences with YAML-defined tests.

1|Updated Apr 1, 2026
One-click install
npx skills add https://github.com/wojons/je-ne-sais-quoi --skill the-invisible-eye
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: the-invisible-eye
Source: https://github.com/wojons/je-ne-sais-quoi/tree/main/skills/the-invisible-eye
Command: npx skills add https://github.com/wojons/je-ne-sais-quoi --skill the-invisible-eye

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires je-ne-sais-quoi.

What problem does it solve?

It helps AI convey the felt weight of systemic surveillance and permanent data retention, especially the chilling effect of knowing your words and actions may be archived, reinterpreted, and used later.

Core Features & Use Cases

  • Logical-to-felt translation of surveillance: Turns clean definitions (panopticon, retention, chilling effect) into lived experience (hesitation before typing, the photo not taken, silence over exposure).
  • Decision and expression under record-keeping: Explains why people edit drafts, alter search behavior, choose anonymity, or avoid sending messages when the record can’t truly be deleted.
  • Embodied “felt tests” and failure modes: Provides scenario-based tests that detect whether an AI truly understands the grief versus only describing behavior changes with clinical language.

What problem does it solve?

Core Features & Use Cases

  • Use Case: Use this Skill to rewrite an AI response about privacy or surveillance so it captures why “the right to be forgotten” feels like more than a policy—why it feels like losing a part of self that should die with you.

Quick Start

Use the the-invisible-eye skill to produce a felt, scenario-grounded explanation of why someone might choose silence over visibility when they know their record will persist.

Frequently Asked Questions about the-invisible-eye

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the chilling effect of surveillance and digital permanence?

The chilling effect of surveillance and digital permanence is the grief of permanent visibility, where systemic observation causes self-censorship, hesitation before typing, and silence over exposure due to archived data.

How does the right to be forgotten relate to self-censorship?

The right to be forgotten mitigates self-censorship by addressing the grief of losing a part of self that should die with you, countering the chilling effect where permanent data retention alters search behavior and expression.

How do I explain the lived experience of systemic observation and permanent data retention?

Translate surveillance mechanisms into lived experience by applying logical-to-felt translation, turning clean definitions like the panopticon into felt consequences such as the photo not taken or the hesitation before typing.

Does this approach use scenario-based tests to detect AI understanding of privacy grief?

Yes, embodied felt tests provide scenario-based tests that detect whether an AI truly understands the grief of permanent visibility versus only describing behavior changes with clinical language, including failure-mode guidance.

Can I use je-ne-sais-quoi to rewrite AI responses about anonymity choices?

Yes, you can use this approach with the je-ne-sais-quoi dependency to rewrite AI responses about anonymity choices, ensuring outputs capture why choosing silence over visibility feels like losing a part of self.

When should I not use logical-to-felt translation for surveillance mechanisms?

Avoid using logical-to-felt translation when you only need clinical descriptions of behavior changes, as this approach requires YAML-defined activation with deterministic instructional structure to convey the felt weight of privacy loss.