Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do, naming the riskiest assumption and testing that one first, and reaching a decision rather than a summary. Use this to validate a problem, test an idea cheaply, decide whether to build something, or fix a discovery process that keeps confirming what the team already believed.
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Discovery is how you find out you were wrong while it is still cheap. A process that never kills anything is not discovery, it is a preparation ritual with research attached.
Every idea rests on a stack: that the problem exists, that people care enough to change what they do, that your approach solves it, that they would pay, that you can build and deliver it. They are not equally uncertain, and testing them in order of comfort is how teams spend six weeks confirming the safe one.
Write the assumptions down, mark the one that would be most damaging to be wrong about, and test that one first. Usually it is the second: the problem is real, and people are living with it comfortably enough not to move.
Interviewing your friendliest customers produces reliable encouragement. Talk to people who churned, people who evaluated and chose something else, and people who solved it another way — those three groups carry most of the information.
Five to eight conversations in a segment usually exhausts the new material. If you are still hearing new things at eight, the segment is too broad.
People are poor at predicting their own behavior and generous when asked to react to an idea. They are reliable narrators of what they actually did.
"Would you use this?" and "would you pay for this?" produce answers that do not predict anything. What predicts is whether they have already spent money or time on the problem.
Rank evidence honestly: what they have paid for, what they have built themselves, what they do in an unprompted usage log, what they say in an interview, what they say in a survey. A prototype someone tries in front of you sits high on that list; an enthusiastic reaction to a mockup sits low.
Match the artifact to the assumption. Demand risk is testable with a landing page or a sales conversation, usability risk with a rough prototype, feasibility with a spike. Building a working version to test whether anyone wants it is the expensive way to answer the cheap question.
Define the outcome that would change your mind before you run it. A test with no failing threshold is a demonstration.
Discovery output is build, do not build, or a specific next test — not a deck of themes. Write what you learned, what you still do not know, and what you are doing about it, and keep the record of the ideas you killed and why. That record is what stops the same idea returning every six months.
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概要と使いどころ
Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
日本語の概要は準備中です。原文の説明を表示しています。
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.
日本語の概要は準備中です。原文の説明を表示しています。
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
日本語の概要は準備中です。原文の説明を表示しています。
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
日本語の概要は準備中です。原文の説明を表示しています。
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
日本語の概要は準備中です。原文の説明を表示しています。