The most interesting Fable tip I've heard so far is to let the model use its own judgement as much as possible
I told it "For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent" and it seems to be saving a lot of tokens
Users praised Simon Willison's Fable prompt tip for cutting token usage because letting the model delegate to cheaper sub-models delivered major bill reductions and strong results.
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More notes on my blog: https://simonwillison.net/2026/Jul/3/judgement/
The most interesting Fable tip I've heard so far is to let the model use its own judgement as much as possible
I told it "For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent" and it seems to be saving a lot of tokens
@_markfenner @simonw Yeah, same. Wonder if we can also tell it to use other cheaper models during its task and just act as the orchestrator.
@simonw Yeah I really like the subagent implementation piece of the superpowers plugin. It does a really nice job delegating to cheaper models dynamically. So I start a plan with Fable and let it do its thing. You have to try that plug-in if you've not
@simonw I built a tool around the same idea! I want fable to delegate workflows to Codex (which has much higher usage limits)
https://github.com/YuanpingSong/ultracodex
@simonw Its amazing, you can also say "look for our previous chats and infer what I would do for this specific problem, use subagent to get info you need", it infer pretty well your pattern of intent and follow suit.
@simonw
@simonw Yep. The better the underlying reasoning, the less scaffolding they need. Also applies to the personal system prompt/instructions. Beyond a certain point, the personal ‘preferences’ tend to reduce performance by adding noise and unnecessary constraint.
@simonw This is the exact mechanic behind a skill I built this week, the plan gets decided once, internally, never printed as a deliberation. Benchmarked it against a viral 'minimal code' skill on its own suite: https://github.com/anshaneja5/scalpel
@singular_prism @simonw Yeap. Just tell Claude to set it up. I give Claude access to all the other CLIs. https://github.com/Jdjohnson/skills/tree/main/downloads/cli-skills
@simonw The most awesome Fable thing to me is free usage. Not sure how I did it, but I glitched the system. Fable medium has been working for 20 hours now without a single % point increase in Fable usage.
I told it no agents, just in case.
Some kind of 2 day old session bug
@simonw Interesting. I’ll give that a try just to test it
@simonw Tried this exact pattern last week — let Claude self-select model tier per subtask instead of hardcoding Opus everywhere. Cut my API bill by ~30% on a multi-step pipeline. The model is genuinely good at knowing when it doesn't need the expensive tier.
@simonw Can we ask it to use codex and openai as well
@simonw we ask the model to make decisions constantly until it makes one we didn't expect, then we yank control back. letting it actually pick the subagent is the harder trust exercise than it sounds.
@simonw Been doing the same, even giving it Codex to delegate to…works great!
@simonw I've adopted having fable captain the work. Fable requests scans, evals, double-checks, authors a plan, and then spins up minions for the impl. It uses ultracode fan-outs wisely.
@simonw you're trying this in claude code?
@simonw Is this going to turn out to be a net positive in the long run?
@simonw I’m doing the same. Having it scope each task with git issue, fire up an independent work tree and assign to appropriate model. It runs several in parallel. Sometimes it’s a rush to review all the results and make decisions before the next batch need to get launched.
@simonw Similarly have it create custom subagents and use those with different effort levels based on the complexity of the task.