How do I make animation of Water sort puzzle pouring?

yes looks like premade animations are best way here. I have a doubt here about the situation when I have to pour multi sections of same color water at once. How would I achieve that dynamics system. there can be 2 and 3 same colors at any position and I have to pour them all at once.

I was thinking to use timeline for the animation but now How would I achieve that with timeline.

Mow I'm thinking that if I use masking method that would be rather easy than making animation with timelines. but then issue will be masking sprites will cover large area and will affect nearby other tubes too.

like this image

May be I need to mix masking + mesh to avoid large area or masking

its the same case and masks will override with other tube's mask As mask is taking much space in the above screenshot

How did you manage to make Claude listen to what you actually wanted? I tried too, but it was driving me crazy and kept producing weird results.

Thanks BTW.

I built a RAG project that instructs the AI to consult Construct's interface type documentation, the Construct Manual, and similar patterns from example projects before writing event sheets.

You can start by cloning these repositories:

github.com/XHXIAIEIN/Construct3-RAG

github.com/XHXIAIEIN/Construct3-Manual

github.com/Scirra/Construct-Example-Projects

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My workflow is roughly like this:

I create a new project in Construct and save it as a folder project, then open that project folder in VS Code.

The first thing I do is initialize a Git repository. Very important.

Then I manually create AGENTS.md and CLAUDE.md in the project root. These files contain instructions that guide the agent to follow the workflow and proactively consult the reference material. I also use symbolic links to point the project to the local copies of those repositories.

You can see my example here: AGENTS.md

After that, you can start Claude Code or Codex with the session opened inside the project folder.

The agent modifies the Event Sheet JSON directly in the project's source files, without going through the Construct editor. Once the changes are complete, just reopen or reload the project in Construct and test it normally.

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Based on my experience so far, using this RAG setup to guide the agent has been extremely reliable.

I don't use any elaborate prompts. I usually just describe what I want to build. Even with that, the generated event sheets have been close to 100% correct in my tests.

Claude can now generate complete Construct event sheets while making proper use of Construct's native features, including hierarchies, families, containers, arrays, dictionaries, JSON, custom actions, and functions.

It also handles more subtle patterns correctly. For example, it knows when to add For Each inside On Timer events, and it can correctly use AJAX to load files data.

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One interesting thing I noticed is that a big reason for the high reliability is that the agent created its own build-project.py tool.

Instead of directly improvising Construct JSON every time, it generates the project through Python and converts that structure into Construct event sheets and project data, including sprites, animations, and other resources.

That seems to make the whole process much more deterministic.

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One small tip when working with coding agents:

Whenever you finish a specific task, have the agent commit the changes to Git. Then start a fresh session for the next task. Try not to mix several unrelated tasks into one long session.

In my experience, keeping each session focused on a single task helps reduce hallucinations and makes the output much more consistent.

I still have question like you said you manually create AGENTS.md and CLAUDE.md in the project root. do you use pre-made AGENTS.md and CLAUDE.md and just place them in project's root folder or always create them with custom instructions?

Yes. For a new project, I usually start with a pre-written AGENTS.md template.

The main purpose of this template is simply to remind the agent to read Construct’s interface/type documentation and related references before making changes, instead of relying on its memory and guessing how Construct works.

As development continues, I let the agent gradually improve AGENTS.md itself.

For example, it can record mistakes it has made, my personal preferences, coding conventions, project-specific rules, or anything else that should be remembered for future tasks.

If the agent does something incorrectly even though I already explained it before, I usually ask it to add that lesson to AGENTS.md so it won’t repeat the same mistake next time.

Over time, AGENTS.md usually evolves into a kind of project-specific "working memory" and development guide for the agent.

If it grows beyond around 200 lines, I usually ask the agent to review the official AI/agent best-practice documentation, such as Claude Code’s best practices:

code.claude.com/docs/en/best-practices

Then I let the agent reorganize and optimize the instructions itself. Typically, it will keep the important and always-relevant rules in AGENTS.md, while moving more specialized rules into separate files under a rules folder inside .claude/ or (.codex/).

Those rules can then be loaded or read only when they are relevant to the current task, following a progressive disclosure approach.

Thanks for the so much info, I'm gonna try this.

The model itself also matters a lot.

So far, I’ve only tested this workflow seriously with Claude Code using Fable 5.1 and Opus 5.

A strong model can often infer your intent from limited context and fill in the missing pieces on its own. In many cases, even if your prompt is not very detailed, it can still reason from the surrounding project structure and documentation and produce the right result.

With weaker or free models, the same workflow can be much less reliable. Even when you explicitly tell the model what to do, it may still make incorrect edits, sometimes to the point where the Construct project becomes corrupted and can no longer be opened.

I tested the same workflow with V4.1 Flash DeepSeek Harness. After it broke the project, I gave it the error logs and spent around ten rounds going back and forth trying to fix everything. Eventually, the project opened again, but by that point the result was still completely off from what I wanted. I ended up having to start over from scratch anyway. That was probably the most frustrating part: after all that debugging, I had burned a lot of time, effort, and money, and still ended up back at square one.

If your budget allows, I’d strongly recommend trying Claude Code with the $20 plan.