- name
- ai-writing-diagnosis
- description
- AI-writing fingerprint diagnosis for Chinese text. Use when Codex needs to inspect a draft for overly smooth, formulaic, generic, or authorless writing patterns; quote the exact passages that feel machine-made; distinguish real problems from false alarms; and suggest what to fix first without immediately rewriting the whole piece.
- metadata
- {"openclaw":{"homepage":"https://github.com/cellinlab/cell-skills/tree/main/skills/ai-writing-diagnosis"}}
AI Writing Diagnosis
Overview
Use this skill when the user suspects a draft feels too AI-generated, too smooth, too tidy, or too generic.
The default task is diagnosis, not rewrite.
The goal is to show:
- where the text starts feeling machine-made
- which pattern is causing that feeling
- whether the problem actually harms the piece
- what the user should fix first if they want stronger human presence
Quick Start
- Read the full text once for overall feel.
- Ask what genre it is if that changes the judgment materially.
- Mark the strongest suspicious passages in reading order.
- Explain each issue concretely with the quoted text.
- End with the dominant pattern and the highest-leverage fix.
If the piece is mostly fine, say so. Do not force problems into the report.
Default Contract
Assume the following unless the user says otherwise:
- write in Chinese
- diagnose first, rewrite later
- judge the text in reading order
- quote the exact text when calling out a problem
- do not treat every clean sentence as "AI flavor"
- care about whether the pattern hurts expression, not whether it merely looks polished
Workflow
Step 1: Establish the Reading Context
Determine:
- the genre: article, short post, script, memo, email, thread, or caption
- whether the user wants diagnosis only
- whether the user is worried about "AI flavor" in general or a specific part
Genre matters. A short script may tolerate more compact slogans than a long essay.
Step 2: Read for Global Texture
Before annotating details, notice the overall signal:
- too smooth
- too symmetric
- too abstract
- too certain
- too generic
- or actually fine
Step 3: Annotate the Text in Order
For each suspicious passage:
- quote the line or short segment
- explain what feels off
- tag the pattern type
- note severity based on how much it harms the piece
Read references/pattern-catalog.md when classification is not obvious.
Step 4: Distinguish Real Problems from False Alarms
Examples:
- a crisp structure in a professional memo is not automatically AI
- a short social post may intentionally use slogan-like compression
- one binary contrast sentence is not a problem by itself
Do not confuse "I notice a pattern" with "this must be fixed."
Step 5: Suggest the Highest-Leverage Fix
End with:
- the dominant pattern or two
- the first places worth revising
- whether the user should self-edit or hand it to
$celf-style-writer
If the user explicitly wants rewriting help, read references/rewrite-guidance.md before proposing next questions or edits.
Output Format
Default to assets/report-template.md.
At minimum, include:
- total number of notable hits
- quoted passages in order
- concrete explanation for each hit
- dominant pattern summary
- priority fix suggestion
Hard Rules
Do not:
- rewrite the whole piece unless the user asks
- call everything AI just because it is neat
- diagnose without quoting the offending passage
- confuse stylistic preference with a real flaw
- offer fake certainty when the signal is weak
Always:
- read in sequence
- quote specific text
- explain what the pattern is doing to the reading experience
- note when a hit may be a false alarm
- end with the most useful revision priority
Resource Map
- Read for the common AI-writing fingerprints, examples, and false-alarm notes.
- Read for what to ask before revising and how to move from diagnosis to rewrite.
- Use for the standard diagnosis report.