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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Account-based marketing inverts the usual model: instead of generating leads and finding out which accounts they came from, you choose the accounts and work them. It is a good fit for a narrow set of businesses and an expensive mistake for the rest.
It works when deal sizes are large enough to justify per-account effort, the addressable market is small enough to enumerate, buying groups have several people, and sales cycles are long enough for sustained effort to compound.
It does not work when the deal size cannot carry the cost, when the market is too large to name, or when marketing and sales will not actually coordinate. That last one is the usual failure: the tooling gets bought, the account list gets built, and the program becomes a more expensive way to run the same campaigns.
Run the arithmetic first. Total program cost divided by the number of accounts, against expected deal value and a realistic win rate, tells you the tier structure you can afford — or that you cannot afford this at all.
Start from the accounts that already look like your best customers — not by revenue but by why they bought and whether they stayed. Add observable signals: hiring, technology in use, funding, regulatory pressure, a change in leadership.
Then commit. A list that churns quarterly cannot compound, and compounding is the only reason this model beats broad demand generation. Agree the list with sales and get their explicit acceptance, because a list sales does not believe in is a list sales will not work.
Purchases at this size are made by several people with different concerns: the person with the problem, the person with the budget, the person who will operate it, and whoever can veto on security, legal or procurement grounds.
Reaching one champion and mistaking that for account coverage is the most common structural error. Track how many roles you have reached within each account, and treat single-threading as a status that needs fixing rather than a warning to note.
Being honest about which tier an account is in prevents the common outcome where everything is nominally one-to-one and nothing is actually customized.
The account should see one coherent effort. That requires marketing and sales working the same plan, with agreed timing, agreed messaging, and an agreed sequence of who reaches out when.
Set up a shared cadence between the two teams for the tiered accounts and hold it. Without that meeting the program degrades into marketing sending things and sales prospecting separately, which is the status quo with extra software.
Lead counts are the wrong instrument. What matters is how accounts move: coverage of the buying group, engagement across it rather than by one person, movement into and through pipeline, and eventually win rate and deal size against non-target accounts.
Expect the timeline to be long. Judging an account-based program on a quarter is judging it before any of its mechanism has had time to work, and canceling it there is the most common way the investment is wasted entirely.
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。
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。
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
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。