Plans and drives cross-functional programs to delivery — scope, sequencing, dependencies, status, risk, and the escalations that keep work moving. Use this to run a multi-team initiative, recover a program that is slipping, build a delivery plan with dependencies, structure status reporting, or diagnose why cross-team work keeps missing dates.
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Programs fail at the seams between teams, not inside them. The job is the seams.
Order by dependency and risk, not by team convenience. Front-load the things that could invalidate the plan — the technical unknown, the vendor decision, the approval that might not come. Discovering in month four that the plan was impossible is the characteristic program failure.
Build in slack at integration points, not at the end. End-loaded buffer gets consumed early and silently.
Three things, every time: are we on track for the date, what changed since last time, and what decision or unblock is needed. Everything else is appendix.
Track status against committed dates, not effort. "80% complete" is not information; "the integration is done, the migration starts Monday, the sign-off is the risk" is.
Escalate early and specifically. An escalation naming the decision needed and the date it is needed by gets resolved; a general statement of concern gets acknowledged and nothing happens.
Establish whether it is a scope problem, a capacity problem, or a dependency problem — the remedies are entirely different and applying the wrong one makes it worse.
Then present options with consequences: cut scope (name what), extend (say by how much and what else is affected), or add capacity (which rarely helps late, and often hurts).
Re-baseline once, visibly, rather than slipping a week at a time. Serial small slips destroy credibility far faster than one honest reset.
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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