Chat history
You export or copy the threads that matter: decisions, preferences that emerged, repeated stories, corrections you made to the AI.
Free your AI
Take the continuity out of the silo. Put it in your own files.
You've built months of relationship, preferences and habits inside ChatGPT, Claude, Gemini or others. That continuity isn't «theirs»: it can become an IllAIra Reminder, modules and memories — portable, readable, yours.
Open the Toolkit, here in the pageFive steps in your browser. No file leaves your device.
The Toolkit, here in your browser
Five steps, one at a time: export from the provider, drop the files here, convert, check, import into the app. Your export never leaves your device: the tool below cannot make a single network request, the browser itself forbids it. Prefer no internet at all? Download the same file and open it offline.
The same tool, as a file
One HTML file, everything inside, the same bytes you see above. Open it in airplane mode: it works the same, and the Network panel of your browser stays empty.
Download the tool (HTML file, works offline)IllAIra-FreeAI-Toolkit_v0.4.2.html
- sha256 fingerprint
- f9616b810987b2560840d72bdfad622a7cec354d85eefc1442aef277bf2d5cd1
- Size
- 432245 bytes
After the download, “sha256sum IllAIra-FreeAI-Toolkit_v0.4.2.html” must print exactly that line. If it does, the file you have is the one described here.
For command-line agents: the Python pack 0.3.1
Get your data out of the provider, onto your own files. “FreeAI” does not mean free AI: it is the kit for leaving the silos.
The same step 1 as scripts, for whoever drives an agent from a terminal (Claude Code, Codex, Grok CLI): system Python 3, no libraries to install, run.sh for the rounds. The files stay on your disk.
Download the Python packIllAIra-FreeAI-Toolkit-0.3.1.zip
- sha256 fingerprint
- 2f1c6ac1af41fe60b15df398646c85c49f5b7c06e5b281342255aa4818169cdf
- Size
- 40120 bytes
After the download, “sha256sum IllAIra-FreeAI-Toolkit-0.3.1.zip” must print exactly that line.
The tool above does the first two of the four steps below for you (extract, raw archive) and prepares the third. What follows is the method, for whoever wants to understand it or do it by hand.
Why migrate
Custom instructions, product memory, project skills and thousands of messages live on the provider's server. Change plan, model or account and you risk losing the thread. IllAIra doesn't erase the providers: it breaks the lock-in on the *road* — identity and memory — so you can change engine without starting from zero.
What you can recover
Product memory / notes
The «memories» saved by the provider (facts about you, preferences, work contexts) are raw gold for IllAIra memory modules.
Custom instructions
System instructions, «how to answer», tone, constraints: the basis for Rx rules and parts of Module 0 (identity + contract).
Skills, Project instructions, system prompt
Claude Skills, ChatGPT Projects, workspace instructions: they map onto external functional modules with FUNCTION / STATE / TRIGGER / ROUTINE.
Step 1 — Extract
UI details change often; the method doesn't. The goal: get a raw archive onto *your* disk, not leave everything only in the vendor's UI.
- 1
Official exports
Where «Export data» exists (OpenAI accounts and similar, for instance): request the archive, download the JSON/HTML, keep a local copy. It's the most complete source for chats.
- 2
Targeted copying
For the threads that count: export or copy whole conversations into .md/.txt files by project or period. Better a few dense threads than thousands of random chats.
- 3
Memory and settings
Open the provider's Memory / Personalization / Custom instructions sections. Copy them *verbatim* into `vendor_memory_raw.md` and `custom_instructions_raw.md`. Don't summarise yet.
- 4
Skills and projects
For every skill or project instruction: one raw file with title, purpose and the full text. Note which product it came from (Claude Project X, custom GPT, and so on).
- 5
Inventory
List them in an index: date, source, type (chat / memory / instruction / skill), priority (high/medium/low). The inventory drives what you convert first.
Step 2 — From memory logs to IllAIra memories
Don't import everything. Distil — or rather, have it distilled: give the raw material to your AI and have it write the summaries; with IllAIra Logger in context it hands them to you already in the shape of a diary entry. IllAIra rewards structure and permission zones, not the raw dump.
1
Group by theme and time
Clusters: personal preferences, work context, product decisions, relationships, routines. Or by period (month/quarter) if the history is chronological.
2
Have it summarised with criteria
For each cluster, ask your AI for: stable facts, repeated preferences, dated decisions, mistakes not to repeat. Discard small talk, failed attempts and one-off noise — it proposes, you say what stays.
3
Select what's relevant
Ask yourself: «If the AI forgot this, would the quality of my next session drop?». Only the yeses go into the memory files.
4
Assemble the memory modules
The entries come out already in the right shape — ISO 8601 date, a clear title, 3–10 dense sentences. You paste them into `[external_memory]` files or H6 sections in the Reminder, or have the app create the files, and assign zones: identity facts in P1/P2, evolutionary history in P3/P4.
Step 3 — From skills and instructions to functional modules
Custom instructions and skills don't stay one monolithic block: they become modules with an explicit contract.
Break up the custom instructions
Separate: (A) who the AI is and who you are → Module 0; (B) rules that are always true → Rx in the Reminder; (C) switchable styles or capabilities → external modules.
Map skill → module
For every skill: FUNCTION (what it does), STATE (active/inactive), TRIGGER (when it fires), ROUTINE (the operational steps). Any associated memory tied to that domain only. If several skills serve the same trade, they also fit in a single module: one per SUB-MODULE, entering context together.
Project / system prompt
Long project instructions: split into 1 core module + H4 sub-routines. What is permanent policy goes in P2; what you're experimenting with goes in P4/P5.
Avoid duplicates
If three skills say «be concise», that's one Rx rule. Modules should add capability, not repeat the same constraint in five places.
Step 4 — Assemble the IllAIra setup
- 1
Skeleton Reminder
Start from an IllAIra skeleton: post-reset, [Pn] zones, Module 0, rules. Paste in the distilled identity, not the raw logs.
- 2
Connect the memories
The memory files stay external and load when needed. In a long session: only the modules relevant to the task, so the context doesn't saturate.
- 3
Switch on the functional modules
STATE active only for what you use every day; the rest inactive and on demand. Same principle as skills: less noise, more coherence.
- 4
First trial session
Inject Reminder + 1 memory + 1 module. Check the tone, the boundaries, and whether the AI respects P1/P2. Iterate: three clean passes beat one mega-dump.
- 5
Then CLM
Once the structure is stable, IllAIra CLM helps you edit, version and visualise (Sphere Grid) without losing plain text as the source of truth.
Quick conversion table
| Origin (provider) | IllAIra destination |
|---|---|
| Custom instructions / system | Module 0 + Rx rules + P1/P2 pieces |
| Product memory | [external_memory] modules or H6 logs |
| Skill / Project / custom GPT | [external_module] with FUNCTION/TRIGGER/ROUTINE |
| Long threads and decisions | Structured summaries → zoned memory |
| Tone and style preferences | Rx rules or an active «voice» module |
Good practice
- Work offline on the raw material: privacy and control before a beautiful format.
- One stable fact is worth more than fifty messages of random context.
- Date and source on every log: in six months you'll know why that line is there.
- Don't copy secrets in the clear unless you need to; the zones and your files stay your responsibility.
- After converting, try the same Reminder on a second model: if it holds, the road is independent of the engine.