Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly. Use this to run an analysis, review one before acting on it, or work out why two people looking at the same data reached opposite conclusions.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
A wrong answer here is rarely an arithmetic error. It is a correct calculation on the wrong comparison, or on data that does not mean what the field name suggests.
Start from the decision. "How is retention doing" has no answer; "is the cohort we changed onboarding for retaining better than the one before it, enough to justify rolling it out" does.
Write down what you expect to find and what you would do in each case before you look. If every possible result leads to the same action, the analysis is not worth running — and knowing that in advance is worth more than the analysis would have been.
Almost every meaningful number is a comparison, and the choice of what to compare against does more work than the calculation.
Name the counterfactual explicitly. "Revenue rose after the campaign" is a comparison against nothing, and it is the single most common way credit is claimed for a trend that was already happening.
Look at the raw rows. Check when collection started and whether the definition changed partway. Check null rates, duplicates, and test or internal accounts still in the set. Check whether recent periods are still filling in — partial data at the tail is what produces the "sudden decline" that resolves itself a week later.
A field's name is not its definition. Find out what actually writes it and under what conditions, especially for anything named status, type, active, or created.
Blended numbers hide the finding almost every time — one segment moving hard while the rest sit still. Split by the two or three dimensions that plausibly matter and check whether the effect is general or local.
Then stop. Slicing until something looks interesting finds noise, reliably, and the result will not replicate.
Give the estimate, the range around it, and what would change the answer. State the sample size and the period. Say plainly what the analysis cannot determine — an analysis honest about its limits gets trusted on the things it can determine.
Distinguish what the data shows from what you infer. Both belong in the report; conflating them is how a plausible interpretation becomes a fact by the third time it is repeated.
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
まだレビューはありません。使ってみた感想をお寄せください。
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。