TL;DR
Thorsten Meyer AI has published a Built in Public spotlight on Outcome-First Decisions, an open-source AI agent skill that turns business ideas into a verdict, a one-week proof test and three actions. The confirmed release details describe version v1.1.0, AGPL-3.0 licensing and compatibility with Claude Code, Codex/OpenAI and Cursor.
Thorsten Meyer AI has spotlighted Outcome-First Decisions, an open-source AI agent skill designed to force business ideas through buyer evidence, a one-week proof test and a written stop line before teams commit months of work.
The project is described as a decision-support skill, not a standalone app. According to the source material, users install it into an AI agent and use it to convert a fuzzy business decision into three outputs: a verdict, a proof test for the current week and three actions for today.
The skill’s core gate requires four inputs before it supports a decision: a named buyer, one scoreboard number, a test that can run within days and a written kill line. If one is missing, the source says the skill asks a narrow follow-up question instead of producing a larger plan.
Thorsten Meyer AI lists five plain-language verdicts: Worth doing, Test first, Change, Defer and Drop. The material also says the current release is v1.1.0, licensed under AGPL-3.0 and compatible with Claude Code, Codex/OpenAI and Cursor.
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.
Fewer Bets, Earlier Proof
The project matters because it targets a common business risk: plausible ideas that attract support but do not face buyer proof until after substantial time and money have been spent. The source frames the cost as the difference between a small proof test and a three-month build cycle.
For founders, product teams and operators using AI agents, the promised value is not faster task execution. It is a harder filter on what work should happen at all. The skill’s approach pushes users to name who pays, define what number matters and state when to stop before expanding a plan.
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How The Decision Gate Works
The spotlight positions Outcome-First Decisions within Thorsten Meyer AI’s Built in Public portfolio coverage. The material says the skill uses a Buyer Evidence Ladder, moving from opinion toward stronger proof such as purchase behavior and repeat purchase.
The source also describes two operating modes. Crisis Mode is said to strip decisions down to a one-line verdict and three timed actions when runway, payroll or a major customer loss creates pressure. Portfolio Command Deck is described as a broader view of active bets, evidence levels, capacity cost and kill dates.
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Adoption Data Is Missing
The supplied material does not provide download numbers, active user counts, independent customer results or third-party testing. It is also not yet clear how widely the skill is being used outside the Thorsten Meyer AI audience.
The source presents dollar figures, including the comparison between $250 and three months, as illustrative. The material also states that Outcome-First Decisions is not business, financial, legal or investment advice and that its verdicts are one input for judgment, not a guarantee of results.
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Testing Moves To Users
The next step is practical use by teams and solo operators who install the skill in supported AI-agent environments and apply it to decisions they are already considering. Based on the source material, the near-term test for the project will be whether users can produce clearer verdicts, cheaper proof tests and faster stop decisions in real workflows.
Further updates would need to show usage evidence, examples of decisions changed by the skill and whether the Buyer Evidence Ladder improves judgment over repeated use.
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Key Questions
What is Outcome-First Decisions?
Outcome-First Decisions is described as an open-source AI agent skill that helps users make business decisions by producing a verdict, a one-week proof test and three immediate actions.
Is it a standalone app?
No. The source material says it is not an app users log into. It is a skill installed into AI agent environments such as Claude Code, Codex/OpenAI and Cursor.
What does the skill require before backing a decision?
It requires a named buyer, one scoreboard number, a proof test that can run this week and a written kill line.
What remains unproven about the project?
The supplied source does not include independent adoption data, measured business outcomes or third-party validation. Claims about reduced waste should be treated as the project’s stated aim unless supported by future evidence.
Source: Thorsten Meyer AI
