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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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价格未确认★ 68 GitHub Stars目录更新于 · 2026年9月27日agent-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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文件元数据

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."

查看原始文本

---
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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安装前审查: 避免自动安装

许可证: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
缺少 AI 审查批准
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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仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: avenoxai/avenoxskills
许可证: MIT
版本: 1.0.0
最近 GitHub 推送: 2026年9月25日
目录更新于: 2026年9月27日
技能指令路径: skills/fable-orchestration/SKILL.md @ 5d0ee6a3e8c3

版本来自目录元数据，使用前请核实来源发布记录。

质量

60/100

有潜力

信任

62/100

仅限沙盒

审计

74/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
缺少 AI 审查批准
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

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

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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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此列表来自公开来源，维护者认领获批前不会标记为官方。

创作者: avenoxai
来源: avenoxai/avenoxskills
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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这条 Registry 收录列表归属于 avenoxai，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/avenoxai-fable-orchestration?metric=listed&label=Listed)](https://www.openagentskill.com/skills/avenoxai-fable-orchestration?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/avenoxai-fable-orchestration?metric=trust&label=Trust)](https://www.openagentskill.com/skills/avenoxai-fable-orchestration?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/avenoxai-fable-orchestration?metric=audit&label=Audit)](https://www.openagentskill.com/skills/avenoxai-fable-orchestration/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/avenoxai-fable-orchestration?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/avenoxai-fable-orchestration?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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