Builds the help center, in-product guidance, and knowledge base that let customers resolve problems without contacting anyone — content, findability, maintenance, and deflection measurement. Use this to build or fix a help center, reduce support volume, write documentation for customers, improve findability, or decide what deserves a help article versus a product fix.
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Good self-service is the cheapest support you will ever run and the most neglected. It is also frequently the wrong answer — an article explaining a confusing screen is a bandage on a design problem.
Before writing, ask whether the contact should exist. If people repeatedly need instructions for one screen, the screen is the defect. Documenting it makes the problem permanent and invisible.
Write articles for things that are genuinely complex, genuinely occasional, or genuinely outside your control. Not for things that are merely badly designed.
Rank by contact volume, not by feature importance. The most-viewed help content is almost never what the team expected — it is billing, access, and the one confusing setting.
Structure each article around the customer's task, in their words, not your feature's name. People search for what they are trying to do.
An article nobody finds does not exist. Findability comes from titles matching real search language, in-product links at the moment of confusion, and search that tolerates the words customers actually use rather than your internal vocabulary.
Read your help-center search logs, especially the queries returning nothing. That list is your content backlog, ranked by demand, already written for you.
Guidance at the point of confusion deflects far more than a help center does, because it requires no decision to go looking. A well-written empty state, field hint, or error message removes contacts that documentation never would.
Documentation rots silently and confidently. Every article needs an owner and a review date, and anything describing an interface needs checking whenever that interface changes.
Wrong documentation is worse than none: it costs the customer time and then a contact anyway, and it spends trust.
Deflection honestly — contacts avoided, not page views. Approximate it by looking at whether contact volume for a topic falls after content ships.
Watch articles with high views and a high subsequent contact rate. Those are articles that are failing to answer, and they look like your best-performing content.
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.
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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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