TL;DR
Thorsten Meyer AI has published a Built in Public spotlight on Outcome-First Decisions, an AGPL-3.0 open-source skill for AI agents. The skill is presented as a decision filter that turns a fuzzy business bet into a verdict, a one-week proof test and three actions for the same day.
Thorsten Meyer AI has spotlighted Outcome-First Decisions, an AGPL-3.0 open-source skill for AI agents that is meant to turn uncertain business choices into a clear verdict, a proof test that can run within a week and three immediate actions.
The source describes the tool as a decision-support skill, not a standalone app. It is built for use with Claude Code, Codex/OpenAI and Cursor, and the spotlight identifies the current version as v1.1.0.
According to the material, Outcome-First Decisions will not approve a plan unless four inputs are present: a named buyer, one scoreboard number, a this-week proof test and a written kill line. If one is missing, the skill is designed to ask a narrow follow-up rather than generate a longer plan.
The skill returns one of five plain-language verdicts: worth doing, test first, change, defer or drop. The source frames the product around a claim that small tests can prevent teams from spending months on ideas that have not been checked against buyer behavior.
The Friction Is the Feature
Most tools help you do more. This one helps you do less — and proves the “less” is the part that earns. It turns a fuzzy decision into a verdict, a one-week proof test, and three actions for today.
Missing one? It doesn’t cheer you forward — it asks the smallest question that fills the gap. When the evidence is an opinion, the answer is “test first,” not a 12-week plan. That’s $250 to learn the truth instead of three months.
A click is not a customer. A “great idea” is not revenue. The skill reads where your evidence sits and designs the cheapest test that moves you up exactly one rung.
So your next “80%” gets discounted accordingly — and the rungs you habitually skip get flagged. You’re not just deciding; you’re building a calibrated instrument out of your own track record.
- Triggered by runway, missed payroll, a lost biggest customer.
- A one-line verdict and three actions with hour-level deadlines.
- The dollar number below which the business closes.
- Scoring tables and framework talk disappear — busywork in an emergency.
- Every active bet with its evidence rung, capacity cost, and kill date.
- At most two unproven bets at once. No bet without a kill date.
- Killed capacity reallocated by name, not vaguely “freed up.”
- Numbers carry provenance — no verdict rides on a half-remembered figure.
mkdir -p ~/.claude/skills && unzip outcome-first-decisions.zip -d ~/.claude/skills/
The honest tradeoff: it will not flatter you. Thin evidence, it says so; an idea that should die, it says so plainly. If you want reassurance, it’s the wrong tool. If you want fewer, better-aimed bets and a verdict you can defend — the friction is the feature.
Independent commentary, produced with AI assistance under human editorial oversight. The views are the author’s own and may change. Outcome-First Decisions is a decision-support tool, not business, financial, legal, or investment advice; its verdicts are one input to your own judgment, not a guarantee of outcomes, and dollar figures are illustrative. Software provided under its stated open-source licence, as-is, without warranty. Product, model, and company names are trademarks of their respective owners; mention does not imply endorsement.
A Spending Brake for Operators
The release matters for founders, consultants and small teams because many early business decisions fail after money and capacity have already been committed. The source positions Outcome-First Decisions as a way to slow that commitment by making buyer evidence, capacity cost and stop conditions visible before work begins.
The broader relevance is the way AI agents are being used inside operating workflows. Rather than using AI only to produce copy, plans or task lists, this skill tries to make an agent act as a gate on spending. That is a narrower and more accountable use case than general productivity assistance.
The claims are still product claims from the publisher, not independently verified performance results. The source says the skill can help users make fewer and better-aimed bets, but it does not provide customer data, adoption figures or external benchmarks.
decision support software for business testing
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Built Around Proof Tests
The spotlight presents the tool as part of ThorstenMeyerAI.com and its Built in Public series. The material says the skill is open source under AGPL-3.0 and includes commands such as /validate, /worth-filter, /kill-audit, /weekly-review, /portfolio and /crisis-mode.
A central feature is the Buyer Evidence Ladder, an eight-rung model that ranks evidence from opinion through repeat purchase. According to the source, the skill reads where a decision sits on that ladder and proposes the cheapest test that moves the user up one rung.
The source also says the skill can track judgment over time. After more than 10 calls in a category, it is described as comparing a user’s stated confidence with their past hit rate, then discounting future estimates when the record shows overconfidence.
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Adoption Data Is Missing
Several details remain unconfirmed from the provided material. It is not yet clear how many users have installed Outcome-First Decisions, whether the skill has been used in live businesses, or whether any outside reviewers have tested its effectiveness.
The source also does not provide an exact publication date, a public repository link, issue history or independent comparisons with other decision tools. The financial example of learning the truth for $250 rather than after three months is presented as illustrative, not as a verified average outcome.
The publisher also states that the tool is not business, financial, legal or investment advice. Its verdicts are described as inputs to judgment, not guarantees that a decision will succeed.
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Users Can Test Version 1.1.0
The next step for interested operators is to install the v1.1.0 skill into a compatible AI-agent environment and test it against a decision they are already considering. The source lists support for Claude Code, Codex/OpenAI and Cursor.
What to watch next is whether Thorsten Meyer AI publishes usage evidence, case studies, repository activity or examples showing how the skill performs across repeated decisions. Those details would make it easier to judge whether Outcome-First Decisions works beyond the stated method.
decision-verdict AI tools
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Key Questions
What is Outcome-First Decisions?
Outcome-First Decisions is described as an open-source AI-agent skill that turns a business decision into a verdict, a one-week proof test and three actions for the same day.
Is it an app?
No. The source says it is not an app users log into. It is a skill installed into compatible AI-agent environments such as Claude Code, Codex/OpenAI and Cursor.
What does the skill require before giving a verdict?
The source says it requires a named buyer, one scoreboard number, a proof test that can run this week and a written kill line.
What remains unproven?
The provided source does not include user numbers, independent testing or verified business outcomes. Its benefits are claims from the publisher at this stage.
Source: Thorsten Meyer AI
