Builds quality into operations — defining standards, catching defects at the right point, root cause analysis, and continuous improvement. Use this to reduce a defect or error rate, design quality controls into a process, run a root cause analysis, respond to a customer quality complaint, or set up quality metrics that drive behavior.
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Inspection at the end sorts good from bad. It never makes anything good. Quality is decided by the process that produced the work, so that is where the effort belongs.
A standard nobody outside the team recognizes is a preference. State quality in terms a customer would agree with: correct, on time, complete, usable — with a threshold, so conformance is a fact rather than an opinion.
Then distinguish:
A process can hit specification perfectly while producing something nobody wants. Only the second question protects against that.
Cost of correction rises steeply with distance from the point of creation. Order of preference:
Every control pushed one step earlier is worth more than an additional control at the end.
"Human error" is where analysis stops, not where it should. Ask what made the error easy to make and hard to notice: an ambiguous form, an unenforced sequence, a target that rewarded speed.
Work backwards through the causal chain until you reach something you can change structurally. A corrective action that depends on people being more careful is not a corrective action — the same conditions will produce the same result with different people.
Verify the fix by watching the defect rate, not by confirming the action was completed.
Any quality metric attached to individual performance will be gamed, usually by reclassifying defects rather than preventing them. Measure at the process level, review trends rather than points, and pair any rate metric with a volume metric so improvement by doing less is visible.
Escaped defects — those the customer found — are the honest measure. Everything else is a proxy.
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.
A quality management system is a regulatory purchase before it is an operational one. Greenlight Guru, Qualio, MasterControl, ETQ and similar exist because regulated industries need document control, training records and corrective actions in an auditable system. Outside those industries a well-run issue tracker and a document repository cover the same ground.
Corrective and preventive action tracking is the part worth having in one place regardless of tool, because the recurring finding is always an action from last time that was never closed.
Statistical process control needs measurement more than software. A control chart in a spreadsheet built on real measurements beats a platform fed by nobody.
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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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