Designs and runs the support function — channels, queues, routing, staffing, service levels, quality, and the metrics that show whether it is working. Use this to set up or fix support operations, choose channels, size a team, set or renegotiate service levels, reduce cost per contact, diagnose long queues or poor quality, or decide what to automate.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Categorize a real sample of recent contacts — a few hundred, read individually, not a report. Almost every support operation finds the same shape: a small number of causes generating most of the volume, and most of those are preventable rather than answerable.
That analysis decides everything downstream. Staffing to demand you have not examined means staffing to demand you could have eliminated.
In order of cost, cheapest first. Push volume up this list rather than getting faster at the bottom:
Most support improvement programs work on level 5 exclusively, because it is the visible one.
Pick by what the work needs, not by what is fashionable. Asynchronous channels are cheaper and better for anything requiring investigation. Synchronous channels are worth their cost for urgency, high-value accounts, and anything where a customer is stuck mid-task.
Every channel you open must be staffed to its expectation. An unstaffed live-chat widget is worse than no chat.
Set by severity and customer tier, published internally, and — this is the part usually missing — checked against actual capacity before being promised. A commitment the staffing cannot meet is a commitment to fail visibly.
Measure first response and time to resolution separately. They have different causes: first response is a staffing problem, resolution is usually a product or escalation problem.
Be careful with time-to-close and volume handled. Both are easily gamed and both reward closing over solving.
Size to peak-hour concurrency, not to daily volume — queues form in hours, not days. Model the shrinkage honestly: training, breaks, meetings, leave. A plan assuming full utilization understaffs by a wide margin and then blames the team.
Review a sample of resolved contacts against a rubric agreed with the team, and coach against it. Reviewing only escalations trains for defense rather than quality.
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.
Ticketing: Zendesk, Freshdesk, Intercom, Front, Help Scout, and similar; Jira Service Management where support and engineering work one queue.
Knowledge base: usually the ticketing tool's own, or Confluence, Notion, or Guru.
Quality review and workforce management — Klaus, Assembled, and similar — start paying off once you staff shifts rather than a team. Before that they are overhead.
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。