Screen Time Auditor

Help users audit and analyze screen habits by device, app, and time to identify triggers and create a realistic one-week reduction plan.

Security Scan

The skill's code and instructions match its stated purpose (a manual screen-time audit); it does not request credentials, external installs, or network access and appears internally consistent.

Purpose & Capability

Name/description (manual screen-time audit) align with the code and SKILL.md. The handler only processes user-provided text/dicts to detect devices, apps, time bands and produce a reduction plan. There are no unrelated requirements (no cloud creds, no unrelated binaries).

Instruction Scope

SKILL.md instructs a manual audit and the handler implements that workflow. The code only reads the skill's own SKILL.md, the provided inputs, and local helper logic—there are no instructions to read arbitrary user files, shell history, or transmit data externally.

Install Mechanism

This is effectively instruction-first with small local Python files and no install spec. Nothing is downloaded from external URLs and no archives are extracted. Risk from installation is minimal.

Credentials

The skill declares no required environment variables, credentials, or config paths and the code does not access environment secrets. Requested privileges are proportionate (none).

Persistence & Privilege

always:false and user-invocable defaults are used. The skill does not modify other skills or system-wide settings and does not request permanent presence.

Assessment

This skill is small, local, and coherent with its description: it performs a manual audit based on user input and does not access external services or secrets. If you care about provenance, note the package source/homepage is unknown — review the handler.py yourself before installing if you want extra assurance. If you prefer to prevent autonomous invocation, you can disable model invocation for this skill in your agent settings, although there are no other red flags here.

Screen Time Auditor / 屏幕时间审计师

Use this skill when a user knows screen time feels too high but cannot yet see where the time goes, why it happens, or how to reduce it without a rebound.

What it helps with

• Estimating screen time by device, app, and time band
• Separating functional use from unconscious drift and emotional escape
• Mapping trigger loops such as boredom, transitions, fatigue, procrastination, or social comparison
• Identifying the worst leakage zones, such as late-night scrolling or fragmented checking
• Recommending friction changes, replacement rituals, and protected phone-free windows
• Converting the audit into one realistic week of experiments

Workflow

1. Ask the user to estimate or manually review screen time by device, app, and time band.
2. Separate functional screen use from drift and emotional escape.
3. Map the trigger loops behind the behavior.
4. Identify the worst leakage zones.
5. Recommend friction changes, replacement rituals, and phone-free windows.
6. Turn the audit into a one-week experiment.

Output format

# Screen Time Audit
## Current Pattern
- Main devices:
- Main drain apps or behaviors:
- Worst time bands:

## Trigger Map
- Trigger:
- Typical behavior:
- What it gives me:
- Better substitute:

## Reduction Plan
- Friction to add:
- Phone-free zone:
- Replacement action:
- Weekly target:

Quality bar

• Move beyond shame into a specific pattern diagnosis.
• Distinguish useful use from compulsive drift.
• Include at least one friction change and one replacement behavior.
• Target one or two problem zones first instead of demanding perfection.

Limits

• Some users need screens for work, caregiving, or study, so total reduction is not the right metric.
• Over-restriction can backfire if boredom or emotional need is ignored.
• Shared household devices can reduce data accuracy.
• Manual audit only, with no telemetry or app-blocker integration.