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
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 goal, the requirements, the gate and 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 31-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.
Cost: in v0.1 a pass through the three roles took about twice the sub-agent tokens of a single skill (≈592k vs ≈283k over the two recorded tasks; the single skill's own auditor is not counted). For a task under a day it does not pay off. The v0.2 figure will be added after its acceptance run.
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:
| Failure mode | What the research says | What the harness does about it |
|---|
| Constraint saturation | Reliable instruction following breaks down beyond 5–6 simultaneous constraints in a single generation; across 15 models at k=8, individual constraints still pass ~41% of the time, but all eight hold together only 5.7% of the time (Vasileva, arXiv:2608.12426; poster at an ICML 2026 workshop) | Each role gets only the files for its step (250–630 words each); structure comes from templates, not from rules held in context |
| Negative instructions backfire | Naming a forbidden word primes the model to produce it; in failures the suppression signal is 4.4× weaker (Rana, 2026 preprint; one model, Qwen2.5-7B-Instruct) | Instructions are positive: "include only the template sections" instead of "do not include the analysis" |
| Self-preference bias | LLM judges rate low-perplexity text higher than human evaluators do, regardless of who wrote it (Wataoka et al., NeurIPS 2024 Safe Generative AI Workshop) | The verifier sees only the goal, requirements, criteria, result and evidence — and preferably runs on a different model, which is what actually changes the judge's perplexity |
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.
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.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
