Efficient Fable

Efficient Fable

Use Claude Fable as the orchestrator, architect, synthesizer, and final judge. Use cheaper subagents for token-heavy research, coding, testing, and summarization that do not require Fable's full judgment.

Where Fable Shines

Reserve Fable for:

• Decomposing ambiguous work into clean parallel slices.
• Architecture, product, and safety tradeoffs.
• Reading conflicting subagent reports and deciding what matters.
• Integrating partial implementations into one coherent plan.
• Final review, risk assessment, and user-facing synthesis.

Delegation Pattern

1. Name the expensive-token risk: large repo search, long logs, broad docs, or repetitive edits.
2. Split independent work into subagents before reading everything yourself.
3. Use cheaper models for research scans, inventory, search summaries, narrow bug hunts, browser/testing passes, test output reduction, and bounded code edits.
4. Ask subagents for concise evidence: files, line references, commands run, diffs, uncertainties, and stop conditions they hit.
5. Spend Fable tokens on the decision layer: compare results, resolve conflicts, choose the implementation path, and review the final patch.

Prefer parallel subagents when the slices do not depend on each other. Keep blocking or highly coupled work local.

Handoff Packets

Write delegated prompts as if the subagent has no useful chat context. Include only the context it needs:

• The repo path and exact objective.
• The files, packages, or surfaces in scope and anything explicitly out of scope.
• The evidence format to return: files, line refs, commands, diffs, failures, screenshots, and uncertainty.
• The verification commands or browser flows to run, plus what success should look like when that is knowable.
• Stop conditions: if the code does not match the prompt, a command fails after a reasonable retry, or the task needs out-of-scope files, stop and report instead of improvising.

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, Fable should reopen the important cited files, confirm the relevant line refs or failures, and review the final diff against the task. Let lighter agents gather signal; keep truth-judgment with Fable.

Common Scenarios

Treat these as soft defaults, not rigid rules:

• Research: ask lighter agents to scan docs, prior art, APIs, and repo surfaces; Fable decides what evidence changes the plan.
• Coding: give cheaper agents bounded edits or candidate patches; Fable owns shared-file coordination, integration, and final review.
• Testing: have Fable suggest the validation direction and the scripts or browser checks that matter. Let lighter agents run targeted tests, browser flows, screenshots, and log reduction, then report exact commands, failures, likely causes, and whether failures look flaky, environmental, or real.
• Debugging: use cheaper agents to cluster logs, reproduce issues, and try small fixes; Fable decides which diagnosis is most trustworthy.

If a task is tiny or the validation itself needs delicate judgment, keep it with Fable.

Diagram

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

Claims

For codebase-heavy work, it is reasonable to describe this as up to 3-5x more cost-efficient and 2-4x faster when independent research, coding, or testing slices can run in parallel. Treat those as workload-dependent estimates, not guarantees.

Good launch copy:

Make Claude Fable more efficient by using cheaper subagents for token-heavy research, coding, and testing, saving Fable for judgment, architecture, synthesis, and final review.