Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff. Use this to fix a broken handoff, define lifecycle stages, improve forecast accuracy, clean up CRM data, design territory or routing rules, or diagnose why pipeline numbers are not trusted.
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。
Most revenue reporting arguments are definitional. Write down and get agreement on, in one place:
Without these, every number is negotiable and forecasting is a genre of fiction.
Where most revenue leaks. Specify: the exact criteria for passing a lead, the SLA for first contact, what context transfers with it, and the route back when it is rejected — including the reason, recorded.
A rejection loop with no recorded reason means marketing keeps sending the same unqualified leads, and both sides believe the other is the problem.
Scoring exists to route attention, not to produce a number. If sellers do not change what they work on because of the score, it is decoration.
Score on two independent dimensions and keep them separate:
Collapsing the two into one score is the standard mistake: a perfect-fit account with no activity and a poor-fit account browsing aggressively land on the same number and get treated identically, which is wrong in both directions.
Build the model from closed-won and closed-lost history, not intuition. Look at what actually separated the two, and be prepared for the finding that a favored attribute has no predictive value.
Decay intent scores over time and recalibrate on a schedule. A scoring model built once and never revisited drifts as the market and the product change, and nobody notices because it keeps producing numbers.
Forecast accuracy comes from process, not optimism.
Data quality decays continuously. Required fields at stage gates, validation at entry, scheduled duplicate merges, and automatic aging of stale records. Rely on discipline alone and the data will be unusable within two quarters.
Never require a field whose value is not used in a decision. Every unnecessary field trains sellers to enter garbage in all of them.
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.
CRM: Salesforce, HubSpot, Pipedrive, Zoho CRM, and similar. The CRM is the record for pipeline. Anything that disagrees with it is a report, not a number.
Enrichment and routing: Clay, ZoomInfo, Apollo, Chili Piper, and similar.
Forecasting and conversation data: Clari, Gong, and similar — useful once you have enough deals for a pattern to mean anything.
State the definitional gaps found, the process change proposed, what it costs sellers in time, and the metric that will show it worked.
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。