Ensures projects deliver the value they were approved on — defining measurable benefits, baselining, tracking after delivery, and honest post-implementation review. Use this to define benefits for a business case, set a baseline, track whether value actually landed, or run a post-implementation review that produces something useful.
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Projects are approved on promised benefits and closed on delivered scope. The gap between those two sentences is why organizations repeat expensive mistakes with confidence.
A benefit that cannot fail to be claimed is not a benefit. Each needs a measure, a current baseline, a target, a date by which it should appear, and an owner who is accountable after the project closes — usually the operational owner, not the project manager, who has moved on.
Distinguish honestly:
The most common failure is a business case padded with non-cashable savings presented as though the budget will fall. It will not, and the credibility loss lands on the next case.
A baseline captured after go-live is not a baseline. Measure first, and record how it was measured — by the time anyone checks, the method will be disputed and nobody will remember.
Benefits appear months after delivery, when the project team has dispersed and attention has moved. This is precisely why it does not happen, and why it needs to be owned by the operational line and scheduled at approval rather than intended.
Set review points at meaningful intervals — ninety days, six months, a year — and hold them regardless of what the answer looks like.
Two questions: did the benefits appear, and would we make the same decision knowing what we now know?
Include estimation accuracy, since the systematic bias in an organization's estimates is one of the most useful things it can know about itself and is discoverable only by looking back.
Make it non-punitive or it will produce nothing true. A review that damages careers produces reviews
that say the project was a success. Feed the findings back to
pmo:portfolio-governance and finance:capital-allocation, which are where the next set of
approvals gets made.
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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