I do this every day, because Codex writes my requirements and Claude implements them. Just ask it for whatever you think the next model will need, tell it to be verbose if you like, and even have a second ChatGPT check it if you are worried. You can even give it a format, going as far as providing a specification or template if you do it frequently. Stick that template in both ChatGPT and claude projects so one can write it and the other can read it.
Edit: I shouldn’t admit this, but I even have an ontology defined - RDF and all - for some of my LLM tasks. Its classes contain examples, and so is like a few-shot instruction, and it’s working scarily well for structuring tasks.
Holy shit. That’s scarily clever. Do you trigger it at a certain max token spend ratio? And do you think it generalizes to pass all kinds of context or its tailored for structured tasks?
It is generalizable given a defined ontology, even better if your life experience can be represented using my ontology.
I had a large context model analyze the last ten years of my notes and build an ontology, it took a lot of iterating. Examples; A software project may have Decision, Risk etc entities; Life may consist of Activities, Goals, Concerns, Problems, etc; the World has knowledge/facts (Topic Taxonomies like Wikipedia) etc. These are all joinable given the relationships are intact.
The agent put everything into a huge RDF ontology - a world model. I worked with the agent to re-frame that large ontology, to I can build a skill appropriate for a small context model to serve as an expert on the ontology, it owns it for all intents and purposes.
I then worked with an agent to define use cases using my notes, real world things I do and have done: research, project management, goal setting, “hey I found a cool project online, it would be useful for my X project which is on the back burner but I don’t want to forget about it” you get the idea. I used those use cases to build out a few skills.
These serve as the actual ontology-aligned data layer, and so have access to URIs pointing to Goal entries in Obsidian, Projects in ClickUp, my calendar etc etc. And so it knows what my most salient Concerns are, and which other entities are associated with them - projects, goals, documents etc, and utilize various MCP tools for external systems. It also creates “context packs” using its view across systems, and so I can have it export structured markdown for any arbitrary entities and their direct and transitive relationships, to facilitate some other agent performing targeted work.
This enables me to:
1. Build targeted software tools for life and work management, I bring the skills into that project which own the data layer OR export context in the form of agent-ready markdown
2. Give skills to my Claws (which aren’t Claws at all, it can be any arbitrary skill supporting agent harness like Goose or Hermes) and LLM apps like Claude Desktop.
Edit: I shouldn’t admit this, but I even have an ontology defined - RDF and all - for some of my LLM tasks. Its classes contain examples, and so is like a few-shot instruction, and it’s working scarily well for structuring tasks.