Builds reporting and self-serve analytics that people actually use — metric trees, dashboard design, distribution, and the discipline that stops dashboards proliferating. Use this to build a dashboard or report, design a metrics framework, set up self-serve analytics, decide what to measure, or diagnose why reporting exists but nobody uses it or trusts it.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Most organizations have too many dashboards and too little insight. The two are related: when everything is measured, nothing is watched.
Every report answers one question for one audience who can act on it. Before building, name the decision it informs and what a viewer would do differently based on it.
If nothing would change, do not build it. That single filter removes most dashboard requests, and the ones surviving it get used.
Structure metrics as a tree, not a list. One primary outcome at the top, decomposed into the drivers that mathematically produce it, each decomposed again.
Revenue = customers × average value. Customers = new + retained. New = traffic × conversion. And so on.
This does two things a metric list cannot: when the top number moves, you can walk down to find where; and it makes clear which metrics are levers and which are outcomes. Teams should be measured on levers they control, not on outcomes they influence.
Self-serve works when the semantic layer is trustworthy and the questions are anticipated. It fails when people are handed raw tables and left to define metrics themselves — that produces confident wrong answers, which is worse than a queue.
Give governed metrics, curated datasets, and templates for common questions. Keep the raw layer for analysts.
Reporting nobody trusts is not used, and trust is lost far faster than it is rebuilt. Protect it by showing freshness on every dashboard, surfacing failures rather than serving stale data silently, and reconciling against the system of record for anything financial.
When a number is wrong, say so prominently and fast. Quietly correcting it is how a team learns to check every figure by hand.
Dashboards accumulate. Review usage periodically and retire what nobody opens — with a notice period, since the one person using it may be using it for something important.
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.
BI: Power BI, Looker, Tableau, Metabase, Omni, Hex, and similar.
Metric definitions belong in a semantic layer — dbt's, Looker's LookML, Cube — rather than in each dashboard's SQL, or the same metric will disagree with itself across two tabs.
Spreadsheets remain the most-used BI tool in every organization. Plan for the export rather than pretending it will not happen.
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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.
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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.
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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.
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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.
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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.
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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.
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