Plans the shape and size of the workforce — demand for roles, build-versus-buy, attrition, and sequencing hiring against budget. Use this to build a hiring plan, decide which roles to open and in what order, model attrition and backfill, plan for a growth or contraction scenario, or connect headcount plans to the budget.
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A hiring plan is an org design decision spread over twelve months and a large financial commitment made in small pieces. It deserves the scrutiny of both.
Headcount is an input. Start from what the organization must be able to do that it currently cannot, then work out what closes the gap — hiring, developing existing people, contracting, or not doing the work.
The shape follows from people:org-design; this is about sequencing and quantity over time, funded
against finance:budgeting-and-forecasting on fully loaded cost rather than salary. Salary is
typically well under the true cost, and plans built on it are wrong by a wide and consistent margin.
Twelve hires in a year is not one number; it is twelve dated decisions with dependencies.
Some attrition is expected and healthy; the planning error is treating it as noise. Model it by segment — teams, levels and tenure bands differ substantially — and plan backfill lead time from the resignation date, not the leaving date.
Watch regretted attrition specifically, and by manager. Aggregate attrition looks fine while one team is bleeding people, and the aggregate is what gets reported.
Plan the base case, the growth case and the contraction case. Knowing in advance what you would stop doing, and which commitments are hard to reverse, is what makes a downturn a decision rather than a scramble.
Offers, notice periods and some contractor arrangements are commitments before anyone starts. Know which parts of the plan can still be unwound and by when.
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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