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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 execute. Load whenever spawn

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Price unconfirmed★ 68 GitHub starsRegistry updated · Sep 27, 2026agent-skill

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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

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.
No mid-tiers. Pick a small number of delegate tiers and stick to them. In this stack: Opus (unlimited) and Codex gpt-5.x (via codex exec). Mixing in more tiers makes routing decisions unauditable.
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-5.x, xhigh reasoning effort) — the default for most high-level tasks, including substantial implementation lanes. It is an obsessive instruction follower: nearly 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 xhigh lanes with a complete spec. Core evaluator seams, security-critical strengthen-never-relax changes, correctness-sensitive paths, the largest surfaces.
Gate-reviewer lanes stay on the highest Codex preset 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.

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File metadata

name: fable-orchestration
description: "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 execute. Load whenever spawning sub-agents (Agent tool, Workflow agent() calls, codex fleets) or planning any delegation."

View original text

---
name: fable-orchestration
description: "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 execute. Load whenever spawning sub-agents (Agent tool, Workflow agent() calls, codex fleets) or planning any delegation."
---

# 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-5.x** (via `codex exec`).
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-5.x`, xhigh reasoning effort) — the default for most high-level
tasks**, including substantial implementation lanes. It is an obsessive
instruction follower: nearly 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 xhigh lanes with a complete spec.** Core
evaluator seams, security-critical strengthen-never-relax changes,
correctness-sensitive paths, the largest surfaces.
- **Gate-reviewer lanes stay on the highest Codex preset** 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.

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GitHub adoption: 68 GitHub stars
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Source & usage notes

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Source repository: avenoxai/avenoxskills
License: MIT
Version: 1.0.0
Last GitHub push: Sep 25, 2026
Registry updated: Sep 27, 2026
Instruction path: skills/fable-orchestration/SKILL.md @ 5d0ee6a3e8c3

Version reported in registry metadata; check source releases before relying on it.

Quality

60/100

Promising

Trust

62/100

Sandbox only

Audit

74/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 68 GitHub stars
Stars/forks activity: 68 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Creator: avenoxai
Source: avenoxai/avenoxskills
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fable-orchestration

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Price unconfirmed★ 68 GitHub starsRegistry updated · Sep 27, 2026agent-skill

Overview

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

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

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.
No mid-tiers. Pick a small number of delegate tiers and stick to them. In this stack: Opus (unlimited) and Codex gpt-5.x (via codex exec). Mixing in more tiers makes routing decisions unauditable.
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-5.x, xhigh reasoning effort) — the default for most high-level tasks, including substantial implementation lanes. It is an obsessive instruction follower: nearly 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.

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 xhigh lanes with a complete spec. Core evaluator seams, security-critical strengthen-never-relax changes, correctness-sensitive paths, the largest surfaces.
Gate-reviewer lanes stay on the highest Codex preset 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.

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File metadata

name: fable-orchestration
description: "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 execute. Load whenever spawning sub-agents (Agent tool, Workflow agent() calls, codex fleets) or planning any delegation."

View original text

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

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

## Choosing the delegate

## The difficulty axis

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

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

Review the source

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Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
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Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 68 GitHub stars
Stars/forks activity: 68 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source & usage notes

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Source repository: avenoxai/avenoxskills
License: MIT
Version: 1.0.0
Last GitHub push: Sep 25, 2026
Registry updated: Sep 27, 2026
Instruction path: skills/fable-orchestration/SKILL.md @ 5d0ee6a3e8c3

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Quality

60/100

Promising

Trust

62/100

Sandbox only

Audit

74/100

Needs review

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Permission surface may require sandboxing
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 68 GitHub stars
Stars/forks activity: 68 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Overview

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

The hard rules

Choosing the delegate

The difficulty axis

Exceptions

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal

Overview

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

The hard rules

Choosing the delegate

The difficulty axis

Exceptions

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal