Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add avenoxai/avenoxskills --skill fable-orchestrationgit clone --depth 1 https://github.com/avenoxai/avenoxskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/avenoxai/avenoxskills/fable-orchestration)<a href="https://agentmods.dev/skills/avenoxai/avenoxskills/fable-orchestration"><img src="https://agentmods.dev/badge/skills/avenoxai/avenoxskills/fable-orchestration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/avenoxai/avenoxskills/fable-orchestration"><img src="https://agentmods.dev/badge/skills/avenoxai/avenoxskills/fable-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00089 | $0.00954 |
| Opus 5 | $0.00044 | $0.00477 |
| Sonnet 5 | $0.00018 | $0.00191 |
| Haiku 4.5 | $0.00009 | $0.00095 |
Grade A, and why
fable-orchestration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 / Workflowagent()call andmeta.phasesentry — 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 76 lines · 89 tokens per session scan A 3bb69bde3d8d
fable-orchestration is a skill published in the GitHub repository avenoxai/avenoxskills (49 stars, last pushed 1mo ago), licensed MIT. It adds 89 tokens to every session and 954 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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