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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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Precio sin confirmar★ 68 Estrellas de GitHubRegistro actualizado · 27 sept 2026agent-skill

Resumen

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.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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.
Ver fuente ↗
Metadatos del archivo
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."
Ver texto original
---
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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Licencia: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Falta aprobación de revisión por IA
  • 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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Repositorio fuente
avenoxai/avenoxskills
Licencia
MIT
Versión
1.0.0
Último push de GitHub
25 sept 2026
Registro actualizado
27 sept 2026
Ruta de instrucciones
skills/fable-orchestration/SKILL.md @ 5d0ee6a3e8c3

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

60/100

Prometedor

Confianza

62/100

Solo sandbox

Auditoría

74/100

Requiere revisión

Abrir auditoría completaVer informe de evaluación
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Falta aprobación de revisión por IA
  • 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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Más detalles
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      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/avenoxai-fable-orchestration",
    "api": "https://www.openagentskill.com/api/agent/skills/avenoxai-fable-orchestration",
    "audit": "https://www.openagentskill.com/skills/avenoxai-fable-orchestration/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=avenoxai-fable-orchestration&task=Use%20fable-orchestration%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20fable-orchestration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20fable-orchestration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/avenoxai-fable-orchestration/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/avenoxai-fable-orchestration"
  }
}

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Creador
avenoxai
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