---
name: al-676-let-a-support-agent-decide-fair-fee-or-penalty-waivers-fr
description: "OpenAI's Nov 5, 2026 cult.fit customer story describes moving customer support from a mostly human-led chat model toward AI-first workflows inside Sprinklr. One example: when a member asks to waive a penalty for a missed group fitness class, an AI agent assesses the request using signals such as local weather and that class's attendance patterns before deciding whether the fee should be waived, with connections into cult.fit backend systems. Cult.fit reports more than 50% reduction in customer support costs alongside the broader AI rollout (Codex standardization, AI Trainer, and support agents). The waiver flow is presented as a concrete agent decision path grounded in policy and signals rather than free-form chat. Figures are cult.fit's as reported by OpenAI. Use when the user wants help with policy-based fee and penalty waiver agent (customer support) for a small business."
---

# Let a support agent decide fair fee or penalty waivers from real signals—weather, attendance patterns, policy—so agents stop rubber-stamping every missed-class or no-show request

OpenAI's Nov 5, 2026 cult.fit customer story describes moving customer support from a mostly human-led chat model toward AI-first workflows inside Sprinklr. One example: when a member asks to waive a penalty for a missed group fitness class, an AI agent assesses the request using signals such as local weather and that class's attendance patterns before deciding whether the fee should be waived, with connections into cult.fit backend systems. Cult.fit reports more than 50% reduction in customer support costs alongside the broader AI rollout (Codex standardization, AI Trainer, and support agents). The waiver flow is presented as a concrete agent decision path grounded in policy and signals rather than free-form chat. Figures are cult.fit's as reported by OpenAI.

## Inputs to collect first
Ask the user for anything below that they haven't already given you. Don't guess missing facts.

- Waiver rules
- Weather API, attendance, tenure, prior waivers
- Support tool, billing

## Instructions

Do the task below with the user's inputs in place of each [bracket].

Build a fee/penalty waiver agent for my support desk. Policy: [paste waiver rules], signals I can check: [weather API, attendance, tenure, prior waivers], systems: [support tool, billing]. Rules: never waive outside policy; never invent weather or attendance; escalate chargebacks, repeated abuse, and VIP exceptions. 1) Turn my policy into a decision tree with clear yes/no/escalate branches. 2) List required signal checks in order. 3) Draft customer-facing grant and deny replies. 4) Propose audit log fields and a weekly false-grant review. 5) Give a two-week pilot plan with success metrics (auto-resolve rate, reopen rate, $ waived). Do not change a real account in this chat.

## Safety checks (share these with the user)

- Check every figure, price and total against your own records before anything is sent, posted or booked. A person approves refunds, discounts, payments and price changes.
- Remove names, contact details, account numbers and other personal data the task doesn't need before you paste, and check your AI tool's data and training settings.
- Read and edit the draft before it reaches a customer or goes public. If you later automate this prompt, start with "draft and notify me", not "send automatically".
- Check names, dates, numbers and any claim against your own source material before you use the answer. AI can misread or invent details.
- Say clearly which parts need a person to review or approve before anything is sent, paid, posted or filed.

## About this skill

- Works with: ChatGPT, Claude, Sprinklr (or Zendesk/Intercom), your billing system, weather/attendance APIs
- Based on this real-world example: https://openai.com/index/cultfit-builds-with-codex/
- Free prompt from AgentPlaybook Real Estate: https://agentic-library.netlify.app/prompt/al-676-let-a-support-agent-decide-fair-fee-or-penalty-waivers-from/
- To install in an agent that supports Agent Skills, save this file as SKILL.md in a folder with the same name as `name` above (for example in your skills directory). Install steps for Claude.ai, Claude Code, Cursor and Codex: https://agentic-library.netlify.app/skills/