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BuilderIO/efficient-frontier

BuilderIO

efficient-frontier

Apply the same orchestration as `/efficient-fable` to any high-cost frontier model: delegate research, coding, and testing to cheaper subagents while keeping planning, synthesis, and final review with the expensive model.

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New~842
v1.0Saved Jul 11, 2026

Efficient Frontier

Use the expensive frontier model where its marginal judgment matters. Push repeatable, bounded, or token-heavy work to cheaper/faster subagents.

Workflow

  1. Identify the frontier-only decisions: architecture, prioritization, ambiguity resolution, risk, synthesis, and final review.
  2. Identify delegable work: research scans, repository inventory, search, docs extraction, browser/testing passes, log reduction, test failure clustering, narrow coding, and mechanical edits.
  3. Spawn parallel subagents for independent slices with clear ownership, bounded scope, verification gates, and expected evidence.
  4. Require compact returns: findings, changed files, commands run, residual risk, stop conditions hit, and anything the frontier model must decide.
  5. Integrate and review centrally before presenting the result.

Handoff Packets

Write delegated prompts as self-contained packets. Assume the receiving agent has not seen the conversation. Include the repo path, objective, scope, out-of-scope areas, relevant files or search targets, expected return format, verification commands, and stop conditions.

Useful stop conditions:

  • The live code does not match the assumption in the handoff.
  • A verification command fails twice after a reasonable fix or retry.
  • The work appears to require files outside the assigned scope.
  • The agent cannot produce concrete evidence for its claim.

Review Loop

Treat delegated output as evidence to inspect, not a verdict to forward. Reopen important cited files, skim high-risk diffs, and rerun or spot-check the verification that matters before claiming completion. If delegated agents disagree, resolve the disagreement at the frontier-model layer.

Common Scenarios

Use these as soft suggestions:

  • Research: delegate broad repo scans, docs extraction, and source comparison; the frontier model keeps the judgment about what matters.
  • Coding: delegate bounded patches, refactors, or mechanical edits when file ownership is clear; integrate and review centrally.
  • Testing: let the frontier model choose the validation strategy and scripts, then use cheaper agents to run unit checks, browser flows, screenshots, and log reduction. Ask them to return exact commands, failures, likely causes, and whether the signal looks flaky, environmental, or product-relevant.
  • Debugging: send independent agents after separate theories, logs, or repro paths; keep the final diagnosis with the frontier model.

Guardrails

  • Do not delegate the immediate blocker if your next step depends on it.
  • Do not ask multiple agents to edit the same files at the same time.
  • Do not trust subagent conclusions blindly when the risk is high; inspect the important evidence yourself.
  • Do not claim universal savings. The pattern works best when exploration and implementation, testing, or research can be parallelized.

Default Framing

"I will use the frontier model as the orchestrator and reviewer, and use cheaper subagents for token-heavy research, coding, or testing so the expensive tokens go to judgment, synthesis, and final quality."

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Overall Score

84/100

Grade

B

Good

Safety

88

Quality

83

Clarity

87

Completeness

78

Summary

A meta-skill that teaches frontier models how to orchestrate cost-efficient multi-agent workflows. It divides work into frontier-only decisions (planning, synthesis, final review) and delegable tasks (research, coding, testing) pushed to cheaper subagents, with structured handoff packets, verification gates, and centralized review before output.

Detected Capabilities

agent orchestrationtask delegationmulti-agent coordinationhandoff protocol designverification gate setupwork triaging and classification

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

multi-agent orchestrationcost-optimize llmdelegate coding taskcheaper subagenthandoff protocol

Use Cases

  • Cost-optimize large LLM tasks by parallelizing token-heavy work
  • Orchestrate multi-agent repository exploration and refactoring
  • Delegate testing and validation while keeping architecture decisions centralized
  • Structure handoff protocols for reliable inter-agent communication
  • Implement tiered cost strategy for ambiguous or exploratory work

Quality Notes

  • Clear separation of responsibilities between frontier and delegable work
  • Practical handoff packet structure with concrete fields (scope, stop conditions, verification)
  • Well-reasoned guardrails that prevent common delegation pitfalls
  • Explicit verification loop prevents blind trust in subagent output
  • Soft suggestions for common scenarios (research, coding, testing, debugging) provide concrete guidance
  • Good coverage of when NOT to use the pattern (tiny tasks, same-file edits, immediate blockers)
  • Clean, scannable structure with descriptive section headings
  • References a sister pattern (/efficient-fable) but does not require it
  • Assumes some agent orchestration experience but remains accessible
  • Testing guidance is practical and model-agnostic
Model: claude-haiku-4-5-20251001Analyzed: Jul 11, 2026

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