be-ambitious-harness

Be-Ambitious Harness — a planner / worker / verifier workflow for ambitious AI tasks

Make AI agents aim higher without losing control: two bars for every task, a cheap gate check before expensive work, and an independent verifier that never sees the worker's reasoning.

English · Русский · 简体中文 · Español

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What it is: a prompt engineering framework, a Claude Code skill, a multi-agent workflow

Most AI prompts ask for a step: fix this bug, make this screen, analyze this CSV. The outcome you wanted — the app stops crashing, users finish checkout, you know why churn rose — stays implicit, so the model delivers the step and stops.

Be-Ambitious Harness is a prompt engineering framework and multi-agent workflow that turns any task into a prompt with:

• Two bars — the baseline result and a more ambitious one, with the extra work and the main uncertainty named.
• A gate check — a cheap, observable test that decides whether the ambitious path is worth starting, with a pass criterion fixed before the test runs and three outcomes: passed, failed, insufficient data.
• Role isolation — a planner writes the prompt, a worker executes it, a verifier grades the result seeing only the requirements, the acceptance criteria and the evidence log. Nobody grades their own work.
• Six scenario playbooks — design/UX, data analysis, code, writing/research, decisions/plans, generic — each with domain-specific checks and traps.
• An eval suite — 8 test tasks and a 30-criteria scorecard, so you can tell when the harness itself breaks. Two of the tasks have been run so far — see Status.

It ships as a Claude Code skill (/be-ambitious-harness) in English and Russian, and works with any agent that can read a folder of markdown files.

Install: git clone https://github.com/alexpiar/be-ambitious-harness.git ~/.claude/skills/be-ambitious-harness, then /be-ambitious-harness in Claude Code. Details and the Russian version — Install.

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Why this exists

On 13 October 2026 Felix Rieseberg, an engineering manager at Anthropic who has led engineering for Claude.ai, Cowork and Claude Code Desktop, published How to be more ambitious: A tutorial for humans with models. The core ideas: consider the outcome rather than the individual task; insist on the instruments the best in the industry use; bring in additional fields; and do not ask the model to judge itself — insist on outside validation. He shared a short prompt: "I want a deeper version of this task, not a bigger one…"

Community skills followed within days: DannyMac180's be-ambitious wraps that prompt, and robonuggets/be-ambitious turns altitude, depth and range into steps and then does the work.

The prompt is excellent and it has a limit: it is one prompt. As soon as you add the rules that make ambition safe — fixed criteria, budget limits, mandatory verification — you run into three failure modes reported in recent LLM research:

Each of these rests on one recent study, and applying them to agent instructions is the harness's own extrapolation. The harness is an attempt to keep "be more ambitious" working within these limits — and its eval suite is there to show where it does not.

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The principles

1. Outcome over step (altitude)

Every analysis starts with what should become possible after this result? A bug fix is a step; "this class of crash does not recur" is an outcome. A slide deck is a step; "leadership catches problems a quarter earlier" is an outcome. The planner names both and builds the prompt around the outcome.

2. Two bars, always

The baseline bar is quality execution within the original scope. The ambitious bar is one of three moves: a higher quality threshold, removing the cause instead of the symptom, or a more useful way to reach the goal. For every ambitious bar the planner states the expected gain, the extra work, and the main uncertainty. If no useful ambitious bar exists, the planner says so — ambition is not inflation.

3. Learning goals for new tasks

Goal-setting research shows that specific, difficult goals beat "do your best" (Locke & Latham, 2002). But where a task first requires acquiring new knowledge or strategies, a specific difficult performance goal can do worse than "do your best", while a specific difficult learning goal does better (Seijts & Latham, 2001; 2005). Critics add that stretch goals narrow focus and invite shortcuts (Ordóñez et al., 2009). So for unfamiliar tasks the ambitious bar is phrased as a learning goal ("find out where the time goes"), with any number attached as a hypothesis ("p95 ≤ 300 ms"), not a promise. Rieseberg's "need 5 s instead of 10 s? Ask for 200 ms" trick lives here — as an experimental target with a feasibility check, so it stops being filtered out as "unfounded".

4. The gate: cheap evidence before expensive work

Before the ambitious branch starts, a gate check answers two questions: is the extra result actually valuable, and is it reachable with the available means? A gate is fully specified only when it has five attributes:

1. a concrete action and the materials it needs,
2. an observable result (a number, a fact, an artifact — not an opinion),
3. a pass criterion fixed before the check runs,
4. a cost limit, substantially smaller than the work it unlocks (a 20-minute test that saves a week is fine, even if the baseline task takes 5 minutes),
5. actions for all three outcomes: passed, failed, insufficient data.

Ambiguity is the third outcome, never the first. The criterion is read, not revised: if it turns out inapplicable, that is "insufficient data", not a reason to move the goalposts. And missing materials are a request to the human before work starts — not a gate outcome.

5. The human owns scope and budget

A gate can show that 200 ms is reachable and that users would notice. Wheth