customer-onboarding-and-implementation
Takes a new customer from signature to working — setting a definition of live that both sides agreed before the contract was signed, planning and staffing the implementation, running data migration and integration realistically, training the people who will actually use it, and handing over to the ongoing relationship. Use this to design an onboarding motion, rescue a stalled implementation, work out why customers who bought never went live, or scope the services a deal actually needs.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/customer-onboarding-and-implementation && cp -r /tmp/customer-onboarding-and-implementation/plugins/customer-experience/skills/customer-onboarding-and-implementation ~/.claude/skills/customer-onboarding-and-implementationSKILL.md
# Customer onboarding and implementation The period between signature and first real use is where the largest share of preventable churn is created, and where a customer's opinion of the product is formed permanently. ## Agree what live means before the contract is signed The most common implementation failure happens in the sales cycle: nobody wrote down what success would look like, so the project has no finish line and every party quietly holds a different one. Define the go-live criteria concretely — which teams, doing what, with what data in place — along with the date, who does what on each side, and what the customer must supply. Put it in the contract or an agreed plan before the deal closes, not after. **Sales commitments become implementation scope.** If a deal was won on something the product does not do yet, the implementation team inherits it. Route that decision back to whoever can make it rather than absorbing it silently. ## Plan the implementation as a project with a customer-side owner Nothing predicts implementation failure like the absence of a named, empowered owner on the customer side. They need authority to make decisions and time genuinely allocated. If the customer cannot name that person, the project is at risk before it starts, and saying so early is a service. Set a cadence from the beginning and hold it — a stalled implementation almost never announces itself; the calls quietly stop being attended. ## Sequence for early value, not for completeness Get one team doing one useful thing quickly, then widen. A phased approach produces a customer who has seen the product work, which changes every subsequent conversation. The alternative — configure everything, migrate everything, launch to everyone — pushes all the value past a long window during which nothing works and the sponsor is defending the purchase. ## Data and integrations are where the time actually goes Both are routinely under-scoped because both depend on the customer's environment rather than yours. Profile their data early; expect it to be worse than described. For the mechanics of moving it, `technology:data-migration` covers the discipline. Integration work depends on the customer's other vendors, their change windows, and their security review — none of which you control. Establish those constraints in week one rather than discovering them in week six. ## Train for the job, not for the product Feature training teaches people what the buttons do. Task training teaches them how to do their actual work in the new system, and only the second changes behavior. Train close to go-live, not months before, and train the people who will use it rather than only the project team. Leave behind material they can use without you. ## Hand over deliberately An implementation that ends when the project ends leaves the account with nobody. Hand over with context — what was configured and why, what was deferred, what is fragile, who the people are — and confirm the receiving side has it rather than assuming. ## Never - Start an implementation without written go-live criteria both sides agreed. - Proceed without a named, empowered owner on the customer side. - Discover the customer's integration and security constraints after committing to a date. - Close an implementation on configuration complete rather than on people using it.
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.
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.
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.
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.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.