JRO-Fable

Fable 5 costs $10/$50 per MTok — 2x Opus, 3.3x Sonnet, 10x Haiku. Every token Fable spends reading files, scanning repos, or reducing logs is 10x what Haiku would charge for the same work. Spend Fable tokens on judgment. Spend cheaper tokens on everything else.

Pre-flight

Before orchestrating, ask: does this task actually need Fable? If Sonnet or Opus can handle it end-to-end, use that model directly — no orchestration layer needed. This skill only applies when the task genuinely requires Fable-tier reasoning AND has delegatable subtasks.

Model Routing Table

Haiku breaks even only if its error rate stays below 20%. If a Haiku agent's output requires Sonnet-level correction more than 1 in 5 times, the re-prompting cost negates the savings. Monitor and escalate.

How to Delegate

Use the Agent tool with the model parameter:

Agent({
  description: "Scan repo for auth patterns",
  model: "haiku",
  prompt: "Search /path/to/repo for authentication..."
})

For parallel independent work, send multiple Agent calls in a single message — they run concurrently (capped at ~16).

For structured multi-stage work, use the Workflow tool with pipeline() (no barrier, items flow through stages independently) or parallel() (barrier, waits for all before proceeding).

Subagents cannot spawn subagents — this is enforced. If you need hierarchical delegation, use Workflows.

Output Contracts

Every subagent prompt must specify the return format. Subagent results inject into Fable's context verbatim — uncontrolled prose wastes tokens.

See DELEGATION.md for named presets with output contracts for each delegation type.

General rules for all subagent returns:

• Max 500 tokens unless the task explicitly requires more.
• Structured over prose: paths, line numbers, commands, pass/fail.
• End with STOP_REASON: (scope-complete | hit-stop-condition | ambiguous).
• End with UNCERTAINTY: if any doubt about the findings.

Handoff Packets

Write delegated prompts as if the subagent just walked into the room. Include only:

• Repo path and exact objective.
• Files/packages in scope. Anything explicitly out of scope.
• Evidence format to return (reference the output contract).
• Verification commands or browser flows, plus what success looks like.
• Stop conditions: if the code doesn't match, a command fails after

reasonable retry, or the task needs out-of-scope files — stop and report instead of improvising.

Pass relevant file slices, not re-read instructions. If you already have a file in context, include the relevant lines in the prompt rather than asking the subagent to re-read it — that's double-paying for the same tokens.

When NOT to Delegate

Delegation has overhead: the routing turn costs Fable tokens, and the return packet consumes context. Not every task pays off.

Keep on Fable when:

• Task is ≤2 tool calls — just do it.
• Files are already in your context — re-reading in a subagent wastes tokens.
• Every edit step requires a judgment call — coordination cost exceeds savings.
• Scope is ambiguous enough that the handoff packet needs full context anyway.
• Single-file bug with obvious root cause — the round trip adds no value.
• The task output is <500 tokens of Sonnet work — routing overhead ≈ savings.

Delegation break-even rule: Subagent work should consume 2K+ tokens to justify the routing overhead. Below that, do it directly.

Vetting Delegated Work

Treat subagent reports as leads, not facts. Before using a high-impact finding, opening a PR, or telling the user the work is done:

1. Reopen the important cited files yourself.
2. Confirm line references and failure reports match reality.
3. Review the final diff against the original task.

Let lighter agents gather signal. Keep truth-judgment with Fable.

Common Scenarios

Treat as soft defaults, not rigid rules:

• Research: Haiku agents scan docs, repos, APIs. Fable decides what

evidence changes the plan.

• Coding: Sonnet agents do bounded edits or candidate patches. Fable

owns shared-file coordination, integration, and final review.

• Testing: Fable chooses the validation direction. Sonnet agents run

tests, browser flows, screenshots, and log reduction. They report commands, failures, likely causes, and whether failures look flaky, environmental, or real.

• Debugging: Haiku/Sonnet agents cluster logs, reproduce issues, try

small fixes. Fable picks the most trustworthy diagnosis.

• Audits: Haiku agents inventory and scan in parallel batches. Fable

synthesizes findings and makes merge/restructure decisions.

Cost Reference

Worked example: 10 research subagent calls, 2K input + 500 output each.

Prompt caching on stable system content (0.1x read price after first call) compounds these savings further.

Diagram

Use assets/fable-orchestrator.excalidraw when a visual explanation helps.