1!function(){try{var e="undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof globalThis?globalThis:"undefined"!=typeof self?self:{},n=(new e.Error).stack;n&&(e._posthogChunkIds=e._posthogChunkIds||{},e._posthogChunkIds[n]="01a0db20-ecff-7f71-8865-2292b6f94305")}catch(e){}}();const e=`Read \`memory/{{personalDir}}/USER_PROFILE.md\` and \`memory/{{personalDir}}/COMPANY.md\` to understand the product, customer segments, and known issues. 2 3Pull from connected services for the last 7 days: 4 5**Support tickets** (Intercom, Zendesk, Help Scout, or similar) â new tickets, resolved tickets, unresolved tickets aging past SLA. Categorize by type: bugs, feature requests, confusion/UX, billing. 6 7**NPS / CSAT** â any survey responses. Specific quotes from detractors. Patterns in promoter reasoning. 8 9**App reviews** â new reviews on App Store, Google Play, G2, Capterra if connected. 10 11**Slack** â any customer-facing channels with feedback threads. 12 13Write to \`session/customer-feedback.md\`: 14 15**Top issues this week** â the 3-5 issues or requests appearing most often. Exact user quotes where available. Frequency count. 16 17**Bugs reported** â specific bugs customers hit, with reproduction detail if given. 18 19**Feature requests** â recurring requests worth flagging to product. 20 21**Notable conversations** â individual tickets or threads worth reading in full (link if accessible). 22 23**Metrics** â ticket volume vs. last week, resolution rate, NPS trend if available. 24 25End with a one-paragraph synthesis: is customer sentiment improving or degrading? What's the single biggest lever? 26`;export{e as default}; 27 28//# chunkId=01a0db20-ecff-7f71-8865-2292b6f94305
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.