TL;DR
Thorsten Meyer AI has published Outcome-First Decisions, an open-source AI-agent skill listed as v1.1.0 and licensed under AGPL-3.0. The tool is designed to turn a business decision into a verdict, a one-week proof test, and three actions for the same day.
Thorsten Meyer AI has released Outcome-First Decisions, an open-source AI-agent skill meant to stop teams from spending months on plausible business ideas before testing whether a named buyer will pay.
The release describes Outcome-First Decisions as not a standalone app, but a skill installed inside tools such as Claude Code, Codex/OpenAI, and Cursor. Its stated output is narrow: a verdict, a proof test that can be run within a week, and three actions for today.
According to the project material, the skill refuses to approve a plan unless it includes a named buyer, one scoreboard number, a this-week proof test, and a written kill line. If one is missing, it is designed to ask the smallest question needed to fill the gap rather than produce a longer plan.
The project is listed as AGPL-3.0 and v1.1.0. Its five verdicts are Worth doing, Test first, Change, Defer, and Drop, with the project framing those labels as plain-language alternatives to scoring systems.
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.
Buyer Evidence Requirements
The release positions Outcome-First Decisions as a tool for testing business ideas before teams commit extended time or budget. The project material emphasizes buyer evidence, including whether a named buyer is willing to pay, before a plan is approved.
For operators, the stated use case is adding gates to AI-assisted planning. The skill requires a buyer, metric, test, and stop condition before it returns an approving verdict, which the project says is intended for founders, product teams, and solo operators evaluating business decisions.
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Built Around Proof Tests
The skill’s framework includes a Buyer Evidence Ladder, which ranks evidence from opinion toward repeat purchase. The project says the skill reads where the available evidence sits and designs the cheapest test that moves the decision up one rung.
It also includes a calibration feature that the project says activates after more than 10 calls in a category. At that point, it can compare a user’s stated confidence with past outcomes and discount future confidence claims when the user’s history does not support them.
The source material also describes two operating modes: Crisis Mode, for situations such as short runway or a lost major customer, and a Portfolio Command Deck, which tracks active bets, evidence rungs, capacity cost, and kill dates.
“Most tools help you do more. This one helps you do less — and proves the “less” is the part that earns.”
— Thorsten Meyer AI project material
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Adoption And Results Unknown
It is not yet clear how many users have installed Outcome-First Decisions, how often it is being used in live business decisions, or whether its verdicts improve outcomes compared with existing planning methods.
The project’s claims about avoiding wasted time and using $250 tests are presented as the product’s framing, not independently verified performance data. The material also states that the skill is decision support, not business, financial, legal, or investment advice.
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Installation And Field Testing
The next step is user testing inside supported AI-agent workflows. The source material provides an installation path for Claude Code and lists compatibility with Codex/OpenAI and Cursor.
Further evidence could include real user adoption, examples of decisions stopped or changed, and data on whether the tool’s kill lines and proof tests are used in budget, deadline, and team-planning decisions.
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Key Questions
What is Outcome-First Decisions?
Outcome-First Decisions is an open-source AI-agent skill that turns a business decision into a verdict, a one-week proof test, and three actions for today.
Who released the skill?
The source material identifies Thorsten Meyer AI and ThorstenMeyerAI.com as the publisher of the Built in Public Spotlight release.
What does the tool require before approving a plan?
It requires a named buyer, one scoreboard number, a proof test that can run this week, and a written kill line.
Is this business advice?
No. The project material says Outcome-First Decisions is a decision-support tool, not business, financial, legal, or investment advice.
What is still unproven?
Public adoption, real-world performance, and comparative results are still unclear. The release explains the method, but it does not provide independent outcome data.
Source: Thorsten Meyer AI
