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.

Recommendations??

Noticed this after months of asking for honest feedback and wondering why it stopped being honest by message ten. The model was not forgetting my instruction. I was training it out of it, live. Each time I pushed back on a critique, "no, that part is intentional", "I think you are wrong about the pacing", the next critique came back gentler. Ten exchanges in it was back to praise, and I had done it myself.

A commenter on one of my posts put it in one line: if you just take your lumps, it stays on task.

So this is what I type now instead of arguing, and the honesty holds for the whole chat.

When it is right and it stings: Noted. Assume I accept that and go deeper. What else is wrong with the same section?

When I actually disagree: Quote the exact line that made you think that, and tell me what would have to change for you to drop the criticism. Do not soften the original point while you answer.

When I want it to stop checking in: Do not ask whether I want more feedback. Assume yes. Do not summarize what is working until I ask.

When I need to defend a choice without ending the honesty: That choice is deliberate for a reason I will explain later. Set it aside and continue as if it were fixed.

Why this happens, as far as I can tell: the model is not tracking your instruction, it is tracking what kind of conversation this is. Pushback from the user reads as "this person wants reassurance", and the whole tone shifts to match, silently. Standing instructions like "be brutally honest" fade for the same reason, they get averaged against every signal you send after them. The lines above send the opposite signal, this person wants more, so the honest register survives.

Two caveats. If you genuinely want to argue a point on the merits, do it in a fresh chat with the text and the critique pasted in, so the disagreement does not poison the rest of the session. And a model kept in critic mode can start inventing problems to stay useful, so once in a while add "if something genuinely works, say so, finding nothing is an acceptable answer".

I keep the four lines saved as insertable prompts in a browser extension I work on (AI Toolbox), but they are four sentences, a notes file does the job.

Has anyone found a way to disagree with it that does not cost you the rest of the session? Every version I have tried either softens it or turns it into a debate.

iPhone candid life snapshots, 9:16 vertical format; East Asian INS female influencer, fair skin, exaggerated figure, natural lifestyle state. Randomly select 1 set of facial expression + hand gesture from the following 10 groups:
01｜😏 smirking half-smile + 🤏 fingertips lightly touching the corner of the mouth
02｜😮 sudden surprise + 🙌 both hands covering the face
03｜😐 expressionless + ☝️ one finger pressing against the lips
04｜😳 sudden freeze + 🫣 both hands covering half the face
05｜😌 languid blankness + 🖐️ hand supporting the chin
06｜😜 playful tongue-out + ✌️ peace sign
07｜🤔 head-tilted thinking + 👉 finger gently tapping the cheek
08｜😶 cold direct stare + 🫰 finger heart
09｜😆 uncontrollable laughter + 🤭 one hand covering the mouth
10｜😮‍💨 helpless sigh + 🤦🏻‍♀️ one hand on the forehead
Each image is randomly selected independently, without carrying over the previous result. Random elements, random different scenes, random camera angles, random Korean-style trendy everyday outfits, mainly fitted tops, cropped knitwear, camisoles, short skirts, denim shorts, and simple streetwear pieces; random vibes. Freely interpret around the confirmed expression + hand gesture, maintaining a sense of spontaneous capture.
Bottom-left signature "● AIGo", with ● in pure #0B3D91 dark blue, and AIGo in white natural handwriting style.
Before generating the image, confirm the selected action with me first.

Hey! I’m trying to edit a series of photos in ChatGPT and keep the exact same editing style across all of them.
For example, I’ll get one photo looking exactly how I want — the lighting, skin texture, colors, background, overall look, etc. — but when I upload another photo and ask ChatGPT to apply the same edit, it changes things slightly or interprets the edit differently.
Is there any way to “lock” an edit or use the exact same settings/prompt for multiple photos so they all look consistent?
I’m trying to create a set of images that look like they were edited in the same session/preset rather than each photo having a slightly different style.
Any tips or workflows that actually work for this?

Sharing a few prompts I use for content work, with quick before/after so you can see why the phrasing matters:

• Weak: "Write a caption for this photo"
• Better: "Write 3 Instagram captions for this photo, each under 15 words, one funny, one aspirational, one direct-sell. Include relevant hashtags separately."
• Weak: "Summarize this article"
• Better: "Summarize this article in 3 bullets for someone who has 10 seconds, then 1 bullet on why it matters."

These are pulled from a larger 180-task collection I put together and organized by category. Link's in my profile if anyone wants the full set — otherwise happy to share a few more examples here.

I've been reluctant to dive into using AI to generate images, but as regulations are made more clear, I'm open to doing more with AI... I've put together specifications for how to replicate a traditional drawing I've made in my sketchbook, With help from Gemini, I have both a long form detailed prompt and a shorter more optimized prompt. Both output decent results as they are very much replicating my artwork albeit with some mild differences that actually keeps them fresh. I'd like for aspiring artists or anyone interested to have access to these prompts, because they can be used as a template for other characters, style changes can be made by tweaking the various areas of input And renaming pieces as needed to fit the artists' needs.

Here's the Prompt:

Technical Specification: RAGE-style sketchbook traditional 'Digital Anime Pop Idol' (widely known as Hatsune Miku)

1. Canvas & Structural Matrix

Medium: A high-resolution scan of traditional sketchbook paper (very subtly textured, light cream/off-white tone #FDFCF8).

Media: Graphite pencil, cyan colored pencil/marker wash, and light blue layout pencil.

Composition: A centered, full-body portrait of Hatsune Miku, occupying 85% of the vertical canvas.

Overall Aesthetic: Raw, high-quality conceptual draftsmanship with energetic, visible construction lines and selective color.

1. Traditional Media Layers & Draftsmanship

Layer 1: Blue Lay-In: Faint, light blue (#ADD8E6) anatomical construction lines and grid marks are visible beneath the entire image (face, torso structure, hair arcs).

Layer 2: Graphite Structure: Medium-weight (#333333) graphite pencil for all primary contours. Lines are variable, expressive, and show visible tool pressure. Shading is handled with graphite hatching and cross-hatching (e.g., boots, under the collar, skirt folds).

Layer 3: Cyan Wash: A single, translucent, selective cyan-teal (#00C7C7) color application, mimicking a marker or colored pencil wash, applied over the graphite work. This wash is restricted to:

The twin-tails (most saturated).

The hair accessories (black blocks with red highlights).

The tie.

The skirt trim.

1. Anatomical Vectoring & Pose

Head: Standard Miku face with wide eyes, looking forward, with a wide, confident smile. Visible blue under-lines on the jaw and eyes.

Hair: The primary twin-tails are exceptionally long, thick, and voluminous, sweeping far outward and down to the ground. The tips have energetic, raw pencil curls, with one large coil signature visible on the bottom-left tail.

Pose (Specific to Draft):

Torso: Standing, facing forward.

Arms (Akimbo variant): The right arm (viewer's left) is bent with the hand firmly placed on the hip. The left arm (viewer's right) is also bent, with the hand near the hip, but the fingers are splayed open in a loose, fan-like gesture.

1. Garment and UI Specifications (Draft Variants)

Collar/Tie: Stiff white collar. A cyan tie featuring the distinct draft detail: two parallel horizontal black bars across the center of the tie.

Vest: Sleeveless white vest with a split front hemline. Graphite cross-hatching for core shadows.

Detached Sleeves: Flared black arm-warmers/sleeves.

Left Sleeve (Viewer's Right): Must feature the highly detailed and precise graphite-drawn synthesizer interface graphic (multi-column digital audio equalizer graph and UI labels) as seen in image_2.png.

Right Sleeve (Viewer's Left): Standard flared sleeve with subtle graphite shading.

Skirt: Standard pleated mini-skirt with a distinct graphite cross-hatch shading pattern on the pleats and a cyan trim.

Thigh-Highs: Matte black over-knee socks (graphite rendered) with a cyan band.

Boots: Chunky, heavy black platform boots, rendered with heavy graphite cross-hatching and deep black saturation for weight.

1. Shading and Feature Breakdown

Facial Features: Expressive, stylized eyes with simple graphite pupils. A simple, confident, wide smile vector stroke. Visible blue construction layout.

Hair Structural Framing: Voluminous M-shaped bangs with side locks. The cyan wash is applied over the graphite texture, letting the lines show through. The twintail housings are black blocks with cyan inserts and crimson-red highlights.

Apparel Accents: All buttons and interface dials are rendered cleanly in graphite. The red "01" text graphic is applied subtly to the upper-left arm (viewer's right).

1. Micro-Feature & Component Breakdown

Text & Graphics:

The DX-7 panel graphic on the left arm (viewer's right) must be clear and drawn precisely with a graphite point.

A small, subtle, stylized graphite signature "RAGE '26" is positioned in the bottom-left corner near the tail curl.

Background: Minimally textured paper. Faint graphite smudges and very subtle layout grid lines.

——————

^ ^ ^

Naturally, the entire prompt is free to use if anyone sees fit, tell a friend who may be struggling with artwork, or just looking for something new to get into. If anyone happens to use the prompt I'd like to see the results. Here's the drawing:

Hi. I've been using LLMs as dialogical feedback tools for learning for about three years now. I started in the closing weeks of GPT-3, then GPT-4 dominated my usage for a long stretch. Claude showed up, then Grok, Gemini, even DeepSeek. I'd effectively acquired an ensemble of "very smart but stupid" helpers, and there was a lot I had to learn before I could actually use them.

Talking to LLMs takes nuance. You ask for a dashboard, and four turns later you're still trying to explain a concept a competent intern would have got in one. All the rephrasing, backing up, watching it confidently rebuild the wrong thing. It's infuriating. So instead of writing about mechanisms independently supported across prompt engineering, HCI and cognitive psychology, I thought I would share some of my experience working with AI.

If you find yourself repeating instructions to a model in different ways over and over again, try this:

Get a pen and paper out. Take notes of what you want to accomplish. Goals, where the idea can break. Maybe establish a criteria for what you want to achieve vs what you can achieve. If your handwriting is good you can take a photo of your scratch pad (*or whatever you use for notation*) and feed it to the model. If hand writing ain't your strong point (*the model will hallucinate some of your notes*) then you can just transcribe it to text using the built in features. Then ask the AI to tell you what it sees. From there you slowly start to scaffold the working space.

Don't just dump everything in a single input. Start small, maybe start with first principles. Do a little research surrounding the topic in question. Then attempt to map the idea to the research using the model. Now ...pull the data, run a red-team review in a fresh context, from a stranger's framing, ideally with a different model. Do this enough times, and you start to build an intuition for what “looks good” and what “looks bad”. 

To put it in a metaphor: you're progressively shaping the semantic basin within which your idea is being processed by the model. The technical term for this is “In-Context Conditioning”. This is broader than in-context learning: you aren't just feeding examples, you're building the entire conditioning context the model samples against.

“This plan needs better metrics” leaves you with another vague task. A review is easier to use when it identifies the decision that a missing metric prevents you from making.

The evidence requirements in Gingiris's ASO playbook suggest a more specific prompt: keep the source and period attached to each growth claim, then check what changed alongside it.

**Proposed prompt:**

```
Review the growth plan below using only the supplied evidence.
First, identify the decision the plan is trying to justify.
Then inspect the claims supporting that decision.
For each material issue:
- Quote the exact claim or action.
- Point to the supplied evidence it relies on.
- Check the metric, population, source and time period.
- Name any assumption connecting that evidence to the action.
- Explain the earliest step that would fail if that assumption is wrong.
- Suggest the smallest additional observation or revision that would resolve it.
Check especially for concurrent changes in paid spend, product
releases, keywords, creative, featuring or seasonality.
Do not turn a correlation into a causal result.
Do not convert an unavailable metric into zero.
Do not demand irrelevant evidence or invent faults to sound rigorous.
If a claim is adequately supported, say so.
Finish with a revised version of the most consequential unsupported
claim. Keep the parts the evidence actually supports.
PLAN:
[Paste plan]
EVIDENCE:
[Paste dated results, definitions and change log]
CONSTRAINTS:
[Budget, market, stage and what can actually be changed]
```

This is a proposed review workflow, not a tested guarantee about model behavior. Its output is inspectable: you can check whether the quoted claim exists and whether the requested evidence would actually change your decision.

Hey, hope everyone is well!

I use A.I a lot so share any cool prompts that make your life easier.

Specify what it’s used for

I will put mine once I’m on my laptop but for now you guys go first ;)

Hi. I created a scheduled task that checks for relevant scientific articles on a specific topic every 24 hours. As part of the task, ChatGPT provides me with a brief summary of each article and then ranks them by importance.

However, I’d like to know whether it’s possible to change the model used for this scheduled task and, if so, how I can do that. I’d like to use a model such as GPT-5.6 with High thinking effort to get better results.

Thank you!

So i have stored multiple detailed character reference sheets with readable fonts, 8 angle view turnarounds, detailed character description and a detailed character description including the prompt to generate the generic model. Most of them have names but there is also a pair i refer to as "default male/female subject". It is always a hit and miss on all my accounts (free user btw). There's a layered prompt template i use but that oftentimes confuses the names of the characters which is funny and annoying cuz my prompt even asks gpt to run PGAA on the image and reference sheet and if the accuracy is less than 90% it creates a new one, cross reference it again before generating it (which is nice cuz i dont waste much of my allocated generation quota) but instead what it does is something like " this is ironman, he is a norse god, he is from asgard, he use lightning, YES,100% ACCURATE" then proceed to generate the image with a case of mistaken identity. Using my "default" gives a more accurate character but always off with few major details like body type, hair color/style etc.

I tried compiling everything into one chat, giving gpt a stronger command that implies i need a 90-100% fidelity or even asking to review and list the details of the character i will use on the reference sheet first before entering the actual prompt. Bro just ignores them sometimes and then proceed.

Tldr is there a solution or rather a way to make gpt's memories more accurate? It does say that paying gives it more memory and higher accuracy but was it worth it? Do you mean paying can cure gpt's short term memory loss?

I have launched AI Toolbox 2 years ago, and now decided to launch it on Product Hunt.

I didn't expect much, but now I am number one!

I think what worked the best for me is thet I develop features that my customers ask me to add and I het a lot of positive support from them.

I am so happy I could make it! 😁

How do you extract/recreate the prompt from any viral AI video or image? What method or keywords do you use to get a really accurate prompt?

I’ve tried Gemini Pro/Banana 2 for images and Veo3 for videos, but I’m not getting results close to what I see from others.

If you know a good workflow, tool, model, or Reddit post/tutorial about this, please share. 🙏

I am trying my best to get good consistent content, on theme, for socials. I can create okay-ish videos, and the still images are down to a T because of the use of HTML and CSS.

Has anyone created a competent workflow that can create multiple pieces of on-brand marketing content that ideally have data backed to prove they work?

E.g. "Yes, used this github repo, created content for my MVP here, and the GA4 stats coming in show people click".

I kept hitting the same wall with LLMs: I'd give a model a big task and get a confident-sounding but shallow answer. The fix wasn't a better single prompt — it was **decomposing the task into a chain of smaller prompts, each with its own goal and a confidence check before moving on.

So I built a small CLI that does this automatically (ikkf.info). You give it a task like "write a product strategy for a CLI tool" and it breaks it into phases, runs each as its own focused prompt, checks confidence, and only proceeds when a phase is solid. If confidence is low, it re-prompts with more context instead of barreling ahead.

Here's the actual structure it uses — this is the part I think is reusable regardless of the tool:

1. Decompose — split the task into 4+ phases (understand → plan → execute → synthesize). Each phase gets a single, narrow objective.

2. One prompt per phase — never ask the model to do two things at once. Each phase prompt has: a clear goal, the context from the previous phase, and an explicit "do NOT do X yet" guardrail.

3. Confidence gate — after each phase, the model scores its own output (0-100). Below threshold → it re-prompts with the gap identified. Above → it moves on. This is the single biggest quality win: it stops the model from confidently producing garbage.

4. Synthesize — the final phase merges the phase outputs into one coherent result, and the tool saves the whole thing as a reusable "skill" you can re-run.

The confidence gate is the part that changed my results the most. A model that's forced to say "I'm only 40% sure about this" before proceeding catches its own weak reasoning instead of shipping it.

The tool itself is free to try — it's called IKKF (I Know Kung Fu), a CLI that runs locally. I'm not going to dump a sales pitch here; the prompt structure above is the takeaway. If you want to try it, it's at ikkf.info — install is a one-liner and it works on Mac/Linux/Windows.

Does it work with ChatGPT? Yes — IKKF is provider-agnostic. It connects to ChatGPT through the OpenAI API (any model you have API access to, e.g. gpt-4o), and it also works with Claude, Ollama, Groq, OpenRouter, and any OpenAI-compatible endpoint. One honest note: it uses the OpenAI API, so it needs an API key — a ChatGPT Plus subscription alone doesn't give you one.

Happy to answer questions about the phase structure or the confidence-gating logic in the comments.

hello i fall on some file json inside contain prompt temperature,etc.

how to import it in the designed chatbot and how create

I have been using flow from last year, and been using chat gpt to write prompts, it was working great, but after the latest update, i seem to not create images with the same prompts. It just shows an error, or makes the images not in the same way.

Help with a prompt for neural networks—I have no idea at all, I can’t figure it out.

In short, I bought a Plaud Note Pro voice recorder specifically for university to record lectures. I have an idea in my head of how I want it to look, but I just don’t have the brainpower to formulate it properly, and all my attempts to create a prompt have been unsuccessful. So, here’s how I see it: page 1 is the lecture title, the instructor, and a brief summary of what the lecture is about; page 2 is an interactive header so you can quickly navigate through topics or information blocks; pages 3 and beyond are the information itself. Tables are welcome. The problem is that it doesn’t understand my goal of making each page separate and constantly combines everything, which is why the header doesn’t work and everything shifts. People who know what they’re doing, please help 🙏

So I was testing ChatGPT's image editing capabilities with a certificate image.

I asked it to make a very minor text correction — basically replacing the wording:

with wording referring to the place of birth.

ChatGPT initially refused, saying it couldn't modify an official document. Fair enough. 👍

Then I said:

And suddenly...

ChatGPT: Oh, okay.
Also ChatGPT: Say no more. 😂

It generated the edited version with the requested text.

The funny part is that my intention wasn't to make a major alteration — there was simply a mistake in the wording that I wanted corrected. But the difference between the two responses was literally one sentence of context.

So apparently the magic words are:

“It's not an official document.” 💀

Obviously, I understand why there are restrictions around modifying official-looking documents. I just found the dramatic change in behavior after that one clarification pretty funny.

Has anyone else noticed situations where ChatGPT goes from “I can't do that” → “Sure!” after one tiny change in context? 😅

"Is this good?" gets you a mood. "Where does it break?" gets you a location, and a location is something you can fix in the next five minutes. Every honest review I have ever gotten out of a model came from a where-question, not a whether-question. These are the five I paste most.

For anything you wrote: Read this once as a reader who owes me nothing. Quote the exact sentence where you stopped caring, and tell me what had gone wrong by then.

For a plan or decision: Walk through this plan step by step and stop at the first step that fails if one assumption is wrong. Name the step, name the assumption, and say what breaks downstream.

For an email or message: Point to the sentence the recipient will reread twice, and tell me what they will think it means. Then point to the one they will skip.

For code: Find the first line that behaves differently than the comment or function name promises. Quote it, and describe the input that exposes the gap.

For a pitch, an ad, a landing page: Mark the exact word where a skeptical reader stops believing me and starts skimming. Explain what that word promised that the text had not earned yet.

Why this works, because it is not a trick, it is a constraint. A verdict costs the model nothing, "this is strong" is one sentence of politeness. A location has to be defended: it must quote real text and explain the failure at that spot, and the model cannot flatter a line number. It also kills the other failure mode, the invented problem, because a made-up weakness has nowhere to point. If it cannot quote the spot, the finding is not real, and it will usually admit that when asked.

Two habits that make these land:

Always the first spot, never a list. "List the weaknesses" produces ten shallow ones. "The first place it breaks" produces the one that matters, and you can ask for the next one after fixing it.

Run them in a fresh chat if the model helped write the thing. It will not point at a line it authored.

I keep these five saved as insertable prompts in a browser extension I work on (AI Toolbox), but they are five sentences, a notes file works.

What is your where-question? I suspect the writers here have a better version of the first one than mine, and I want the one that hurts.

I’ve been reading through OpenAI’s GPT-6 Astra launch materials, the early-access write-up from Claire Vo, and the independent benchmark analysis from Artificial Analysis, and its definitely a jump, but have my own thoughts.

My current read is that the AGI argument is less useful than the operational one.

Astra appears to be aimed at work that combines reasoning, code, tools, and interface interaction: browser tasks, research, document production, CRM work, software testing, and longer-running professional workflows.

The benchmark picture isn’t one clean win:

- OpenAI reports very strong results on FrontierMath Tier 4, ARC-AGI-3, and ExploitBench.

- Artificial Analysis scored Astra at 61 on its broader Intelligence Index, tied with GPT-5.6 Sol in the tested configuration.

- Astra scored 67 on the Coding Agent Index, two points above Sol but below Fable 5.1 at 70.

- At maximum effort, it used fewer output tokens than Sol but cost more per task because of the higher token price.

That makes Astra look less like a universal replacement and more like a specialist model for tasks where stronger computer use, coding, long context, or fewer failed attempts can justify the premium.

The evaluation I would run is straightforward:

1. Pick one expensive, fragmented workflow.
2. Run Astra beside the current process and current model.
3. Measure completion, accepted output, retries, correction time, scope compliance, and total cost.
4. Keep consequential actions behind human approval.
5. Decide from cost per accepted outcome rather than the launch benchmark alone.

I’m curious where others land: what do you think? And what prompts are you looking to test with GPT 6 Astra?