Business

conditional-customer-success

Guides customer-success practices for recurring account relationships, including applicability decisions, success plans, evidence-based health records, renewals, escalations, handoffs, QBRs, and closed-loop feedback.

★ 88 GitHub Stars
91 Qualitätsbewertung
9 Dateien

About this Skill

Guide recurring human-relationship practices — success plans, health evidence, renewal and expansion signals, QBRs, handoffs, escalation, and closed-loop Voice of Customer. Do not use this skill for products without accounts, renewals, QBRs, or a customer-success team, including some internal tools, pure transactional products without recurring relationships, and public services without account-based engagement. Load only when the product context includes a recurring human relationship; decline or route away otherwise.

Anwendungsfälle

  • Assess whether customer-success practices apply to a product
  • Create customer success and outcome plans
  • Document account health and renewal risk evidence
  • Define escalation and cross-team handoff paths
  • Close the loop on customer feedback and product changes

Funktionen

  • Conditional applicability and routing guidance
  • Templates for success plans, health-risk records, and escalation closure
  • Evidence-based health assessment with confidence and conflicting signals
  • Human-review requirements for escalations and health decisions
  • Privacy boundaries for customer feedback and account data

Unterstützte Agents

Not specified

Skill files

Related Skills

justin-sun-perspective

Provides a Justin Sun-inspired perspective for analyzing crypto industry trends, business decisions, competitive strategy, regulation, attention economics, and entrepreneurial cho…

0xquqi/sun-skill ★ 296
justin sun crypto blockchain entrepreneurship

ai-governance

Provides a methodology for designing and operating AI governance programs, including risk tiering, lifecycle controls, accountability, compliance mapping, agent reviews, GxP overl…

magnus919/agent-skills ★ 88
ai governance responsible ai model risk ai compliance

product-operations-and-governance

Helps agents design and run product governance through decision rights, review cadences, evidence standards, operating patterns, exception records, and escalation paths.

magnus919/agent-skills ★ 88
product-operations product-governance decision-rights review-cadence

ai-operating-economics

Helps evaluate whether AI-enabled workflows should be adopted, scaled, constrained, redesigned, or retired using outcome evidence, countermetrics, full operating costs, uncertaint…

magnus919/agent-skills ★ 88
ai economics value realization ai adoption outcome measurement

product-lifecycle-learning

Helps agents compare intended and observed product outcomes, update evidence-backed assumptions, assess feature health, choose lifecycle actions, and plan retirement with migratio…

magnus919/agent-skills ★ 88
product lifecycle post-launch review feature health assumption ledger

product-experimentation

Guides end-to-end product experiments by mapping assumptions, selecting appropriate test methods, defining guardrails and decision rules, and recording readouts that update produc…

magnus919/agent-skills ★ 88
product-experimentation hypothesis-testing experiment-design ab-testing

technical-program-management

Helps coordinate multi-project technical programs by defining mandates, aligning dependencies and capacity, governing risks and decisions, tracking benefits, managing adoption, an…

magnus919/agent-skills ★ 88
program management program governance benefits realization dependency management

gap-analysis

A platform-agnostic method for comparing evidence-based current and target states, explaining gaps and uncertainty, prioritizing interventions, and producing traceable registers, …

magnus919/agent-skills ★ 88
gap-analysis current-state target-state capability