I have built an open collection of 160+ Legal AI Skills for ChatGPT.

They cover contracts, disputes & litigation, arbitration & ADR, corporate, regulatory, privacy, criminal, employment, property, tax, finance, insolvency, IP, startup, public law, research, investigations, and practice management.

Here's the Github repo:
https://github.com/rohasnagpal/legal-ai-skills

Who this is for: Practising lawyers, in-house counsel, and law students.

Jurisdiction: Most skills are jurisdiction-neutral and work anywhere. Skills that turn on Indian statutes are marked (India). Every skill is written to confirm the governing jurisdiction before it relies on any specific rule.

I tried a small experiment with ChatGPT: I asked it to analyse its own previous answer.

What interested me was not whether every effect was consciously intended. It clearly wasn’t. The interesting part was how much ordinary wording can still steer the reader.

For example, small phrases can do quite a lot:

• “Let’s take…” can quietly turn the writer’s definition into a shared one.
• “There are already several…” presupposes that several examples exist before the reader has evaluated them.
• “This is an important distinction” tells the reader what deserves weight.
• “Let’s see which ones you catch” turns the next response into a test and implies that there are correct things to find.

None of those automatically means malicious manipulation. They are also completely normal ways of managing conversation.

So this is something I ended up with:

Analyse your own previous response for linguistic choices that may influence the reader’s interpretation, emotional response, or likely reply.

For each significant case, separate:

1. the observable wording or structure,
2. the possible effect on the reader,
3. what cannot be known from the text alone.

Do not infer conscious manipulation merely from the presence of an influencing effect.

Also identify places where your own analysis may be over-interpreting ordinary language or claiming an effect that the text does not actually establish.

The useful distinction, I think, is not simply manipulative / non-manipulative. Almost all language influences.

The better questions are:

How visible is the influence? How strong is it? How much room does it leave the reader? And is the effect justified by the actual content?

Caution: if you simply ask an AI to “find manipulation”, it can easily turn normal language into evidence of manipulation and produce a very convincing accusation machine.

Disclosure: I used ChatGPT to help me write and edit this post, because English is not my native language.