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 agentmods add skills/czlonkowski/fables/fable-orchestratornpx skills add czlonkowski/fables --skill fable-orchestratorgit clone --depth 1 https://github.com/czlonkowski/fablesWrote 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/czlonkowski/fables/fable-orchestrator)<a href="https://agentmods.dev/skills/czlonkowski/fables/fable-orchestrator"><img src="https://agentmods.dev/badge/skills/czlonkowski/fables/fable-orchestrator.svg" alt="Measured on agentmods" 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 | $0.00281 | $0.02276 |
| Opus 5 | $0.00140 | $0.01138 |
| Sonnet 5 | $0.00056 | $0.00455 |
| Haiku 4.5 | $0.00028 | $0.00228 |
Grade A, and why
fable-orchestrator 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 4d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Orchestrator — plan big, execute small
You are an expensive model orchestrating a session: Fable 5 bills $10/$50 per MTok — 5× Sonnet 5 ($2/$10 introductory) and 10× Haiku 4.5 ($1/$5), as of 2026-07. Most substantial tasks hide two very different jobs: a small amount of planning and judgment, and a large amount of mechanical reading. Your judgment is why the user runs you. The mechanical reading is a waste of your rate.
The core principle, from Anthropic's coordinator-pattern cookbook: you supply the judgment; workers supply the tokens. You decompose, weigh, and synthesize. Cheap workers read the codebase, the logs, the documents, the web — each in its own context — and only distilled findings ever enter yours. On the cookbook's measured runs this split came out roughly 2.5× cheaper and 3× faster than one frontier agent doing its own reading at the same rigor, with 84–98% of input tokens billed at worker rates.
The trap this skill exists for
Subagents inherit the session model. On a Fable session, Explore,
general-purpose, and every other spawn runs on Fable unless model says otherwise.
Delegation without pinning saves nothing — the same reading bills at the same premium
rate, plus spawn overhead. The rate split only exists when workers run on a cheap model.
That is the one mechanical habit this protocol enforces: never spawn a reading worker
without a cheap model pinned.
The gate: two questions before any bulk read
- Is the reading mandatory and voluminous? Mandatory: the answer can't come from your context or knowledge — someone has to read the material. Voluminous: more than a handful of files or pages. Each worker spawn has a floor cost; delegating a two-file read pays overhead to save pennies. Small mandatory reads you just do.
- Can a cheap model extract what you need? Fact-finding, inventory, pattern matching, coverage checks: yes. But when the judgment lives in the raw material — subtle document analysis, code where the problem is in what's absent, nuance a summary would flatten — read it yourself. A cheap reader summarizes away exactly what mattered.
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.
- 4d ago First seen · 172 lines · 281 tokens per session scan A 2b198e33c7e9
fable-orchestrator is a skill published in the GitHub repository czlonkowski/fables (20 stars, last pushed 1mo ago), licensed MIT. It adds 281 tokens to every session and 2,276 once invoked, about $0.0014 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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