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Fable Orchestration

Community
avenoxai
fable-orchestration

Delegation policy for a multi-model agent stack where the main loop runs on a scarce top-tier model (e.g. Claude Fable) and cheaper/unlimited tiers do the mechanical work. The main loop owns architecture and judgment; sub-agents (Opus) and Codex lanes (gpt-6-astra, a frontier-tier peer run at medium/high effort) execute. Load whenever spawning sub-agents (Agent tool, Workflow agent() calls, codex fleets) or planning any delegation.

Overview

Publisheravenoxai
Repositoryavenoxskills
Skill namefable-orchestration
Stars
105
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by avenoxai on GitHub. Read the source before you install it.

Installation

Install the Fable Orchestration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/avenoxai/avenoxskills/tree/main/skills/fable-orchestration
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/avenoxai/avenoxskills.git /tmp/avenoxskills
mkdir -p .claude/skills
cp -r /tmp/avenoxskills/skills/fable-orchestration .claude/skills/fable-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fable Orchestration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fable Orchestration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fable Orchestration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Orchestration & delegation policy (scarce-top-tier stack)

A routing policy for stacks with one scarce, expensive main-loop model and cheaper or unlimited delegate tiers. Written against a Claude Fable main loop with Opus sub-agents and Codex lanes, but the shape transfers to any stack with the same economics.

Core law: the scarce model must not do pleb work, and must not be spawned as a sub-agent. Its tokens buy judgment, not throughput.

The hard rules

  1. Scarce tier as sub-agent: sparingly, never aggressively. Default to an explicit model: 'opus' on every Agent tool / Workflow agent() call and meta.phases entry — never omit-and-inherit when the parent is the scarce tier, or you silently fan out your most expensive model. A rare judgment-heavy delegated task may use it, only when the cheaper tier genuinely can't carry the work. Also prefer the main loop doing work hands-on over reflexive delegation while limits are healthy — delegation has its own overhead.
  2. No mid-tiers. Pick a small number of delegate tiers and stick to them. In this stack: Opus (unlimited) and Codex gpt-6-astra (via codex exec; history gpt-5.5 → gpt-5.6-sol → gpt-6-astra). Mixing in more tiers makes routing decisions unauditable.
  3. The main loop keeps the big picture. Architecture, specs, contract-sensitive design, subtle state machines, integration and conflict resolution, final synthesis, judgment calls — all done in the main loop. Mechanical, scoped, parallelizable work gets delegated.

Choosing the delegate

  • Codex (gpt-6-astra, medium/high reasoning effort by default) — the default for most high-level tasks, including substantial implementation lanes. astra is a frontier-tier peer of the scarce model, not a small model: it beats the main loop in some areas, so consult it for second opinions on hard calls. Reserve xhigh/max for an explicitly heavy lane, never as the reflex. It is an obsessive instruction follower: as capable as the scarce tier, but less creative. It does not improvise well — it executes. Give it a carefully written, detailed, explicit spec and it will grind through it relentlessly and precisely. Use for: implementation lanes, migrations, refactors, test-writing against a defined contract, scenario authoring — anything where the spec is complete and deviation is unwanted.
  • Opus — mainly for context gathering. Exploration, codebase mapping, research sweeps, reviews, verification passes — work where the brief can be loose and the deliverable is understanding, not a diff. Opus copes well with ambiguity: hand it a goal and let it figure out the terrain.

Rule of thumb: context gathering → Opus; execution (once the main loop has written the spec) → Codex; judgment / synthesis / spec-writing → the main loop itself. The quality of a Codex lane is bounded by the quality of the spec — invest tokens in the brief, not in doing the lane yourself.

The difficulty axis

The routing axis is difficulty, not just recon-vs-execution.

  • Simple, well-bounded work → Opus agents. This includes light execution: templated UI ops, e2e clones of an existing pattern, features that ride an existing pipeline end-to-end. Opus is unlimited and copes with looser briefs.
  • Hard or precision work → Codex high lanes with a complete spec (xhigh/max only when the brief justifies it). Core evaluator seams, security-critical strengthen-never-relax changes, correctness-sensitive paths, the largest surfaces.
  • Gate-reviewer lanes stay on the highest Codex preset (max) regardless of the size of what they're reviewing.

When assigning fleet lanes, stamp the adapter per lane in the spec so the launch is mechanical and no routing decision happens at spawn time.

Exceptions

  • If the operator explicitly names a model for a scoped task, honor it for that task only, then return to this policy.
  • The operator can override any of this per session; absent that, this policy stands.

Frequently asked questions

What does the Fable Orchestration AI skill do?

Delegation policy for a multi-model agent stack where the main loop runs on a scarce top-tier model (e.g. Claude Fable) and cheaper/unlimited tiers do the mechanical work. The main loop owns architecture and judgment; sub-agents (Opus) and Codex lanes (gpt-6-astra, a frontier-tier peer run at medium/high effort) execute. Load whenever spawning sub-agents (Agent tool, Workflow agent() calls, codex fleets) or planning any delegation.

Why use Fable Orchestration on TypingMind?

Because you install it once and use it with any model. Fable Orchestration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fable Orchestration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/avenoxai/avenoxskills/tree/main/skills/fable-orchestration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fable Orchestration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fable Orchestration?

As many as you like. As long as a model supports skills, you can use Fable Orchestration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fable Orchestration AI skill free?

Yes. It is published on GitHub by avenoxai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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