Efficient Frontier logo

Efficient Frontier

OrganizationPopular
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.

Overview

PublisherBuilderIO
Repositoryskills
Skill nameefficient-frontier
Stars
4.3K
Forks
211
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BuilderIO on GitHub. Read the source before you install it.

Installation

Install the Efficient Frontier AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BuilderIO/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/efficient-frontier .claude/skills/efficient-frontier
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Efficient Frontier in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Efficient Frontier on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Efficient Frontier is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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."

Frequently asked questions

What does the Efficient Frontier AI skill do?

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.

Why use Efficient Frontier on TypingMind?

Because you install it once and use it with any model. Efficient Frontier is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Efficient Frontier in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BuilderIO/skills/tree/main/skills/efficient-frontier. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Efficient Frontier?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Efficient Frontier?

As many as you like. As long as a model supports skills, you can use Efficient Frontier with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Efficient Frontier AI skill free?

Yes. It is published on GitHub by BuilderIO under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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