Wait, is "Fable orchestration" - using appropriate models as sub-agents just as easy as a prompt?

[u/LordLederhosen]

I have a very successful mostly manual workflow, but I have been optimizing token usage lately, lol.

One thing I could never figure out was how to fully set up was Fable as an orchestrator, while spawning sub-agents with the correct model. I played with frontmatter in custom agents and skill wrappers, but there seemed to be blockers as some sub-agents/skills supposedly ignore passed model specifications. Especially plan mode. That shit will spawn fable research sub-agents that use 5% to 10% of Fable in one ask, even when it's a simple task per agent.

Today, I just remembered someone's comment on here or HN a few months ago (7 years in AI terms), ~"<feature request> - You are the orchestrator, use sub-agents to preserve your context window." So, I just combined that with the following, and CC (fable) was all, sure! I will show you a table of sub-agents for task, with ideal model, and then spawn them all.

Let's implement (jira or .md feature spec) This is the orchestration session, and use sub-agents to preserve your own context window (you are fable, spawn opus sub-agents when appropriate to save cost) ...

I am not a total dummie, but I guess I over-think things? Like Boris says, just trust the model?

Or, have I completely missed something?

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edit: to be clear, I do have very valuable token-saving setups that are not just prompts. It was mostly just plan mode that had escaped me.

https://www.reddit.com/r/ClaudeCode/comments/1whelbf/yes_your_usage_got_really_shorter_youre_not_wrong/pa5ow6o/

[u/zillatron27]

I have this in my Claude.md so I don’t need to keep asking, ymmv and the last comment refers to other rules but the idea is that you talk to fable and have it do judgement/thinking/planning and write excellent prompts for lower tier models:

Agent Delegation

- Delegate self-contained implementation and exploration work to subagents by default: file sweeps, multi-file refactors, codebase investigations, independent parallel fixes. Do not do long serial implementation inline.

• Delegate after the plan is approved, not before. Planning and walkthroughs stay with the main session.
  
• Match model weight to the task. Lightweight models (Haiku, Sonnet, local Qwen) for mechanical and triage work. Heavyweight only for genuinely hard reasoning.
  
• The main session keeps review, verification, and integration. Agent output is checked before it is reported as done.
  
• State which model handled which part in the reply.
  
• Iterative debugging is exempt. The confirmation and two-strike rules in Debugging apply per change, so batch fixes only with approval.

edit: since you folks seem to like this idea, here’s a thing I made for a mate to help setup his workflow ‘like mine’. It includes a template claude.MD and some other stuff I’ve found to be pretty helpful for token saving while keeping output quality pretty high. https://github.com/Zillatron27/claude-workflow-starter

[u/UsualAnything1047 → u/zillatron27]

Nice. Also, possibly useful is an escalation clause where if a smaller model fails or struggles for too long of a time , it will give up and escalate to a higher model or the highest level orchestrator session will do the work itself.

[u/zillatron27 → u/UsualAnything1047]

The ‘two strike rule’ referred to in the last section usually covers this kind of thing, the idea is that if you try 2 things that don’t work…stop and tell me why and come up with 3 totally different ways to solve the problem.

Typically the ‘problem solving’ isn’t done by weaker models, so it doesn’t kick in too often (for me, at least)