Detect AI Patterns

Diagnose Writing Patterns

Use this skill at the start of a de-flavoring task.

Its job is not to rewrite. Its job is to identify what makes the text feel generic, unowned, or mechanically produced so later edits can stay targeted.

When to Use

Use this skill when:

• the user asks to "remove AI flavor" from a passage
• the text feels formulaic but you need a crisp diagnosis first
• the user explicitly asks why a passage sounds stiff, generic, or AI-like

What to Look For

Scan for patterns such as:

• template openings and empty framing
• rigid symmetry like "first, second, finally"
• stacked connectors that make logic feel mechanical
• repeated sentence shapes or paragraph cadence
• abstract, inflated wording with low information density
• generic authority markers and fake profundity
• canned warmth, canned empathy, or canned uplift
• excessive list structure where prose would be more natural
• translated-from-English Chinese phrasing
• weak authorial judgment
• places where the real point is buried under framing

Output

Keep the diagnosis short and actionable.

Recommended structure:

1. main_patterns: the top 2-5 patterns
2. severity: low / medium / high
3. rewrite_focus: what the next stage should prioritize
4. specificity_gaps: where the text needs stronger grounding or sharper wording

Rules

• Quote only short trigger fragments when needed
• Do not start rewriting in this stage unless the user asked for a combined result
• Do not claim a text is "definitely AI-generated"
• Frame this as pattern diagnosis, not authorship detection
• Focus on revision priorities, not detector language

Prompt Pattern

Use a compact internal instruction shaped like this:

Read the passage and identify the strongest formulaic writing patterns.
Focus on repeated structures, stacked transitions, empty significance claims,
over-smoothing, low-information abstraction, and weak authorial judgment.
Return only the main patterns, a severity level, specificity gaps,
and rewrite priorities.
Do not rewrite yet.