Results for “feedback-automation”

Designs and reviews feedback loops for AI agents to ensure goals are machine-decidable, loops are damped, and human judgment is preserved.

当在线业务的一个环节（如产品、内容）运行后，需要系统性地收集反馈并驱动整体优化时

Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine.

Design 360-degree feedback programs with surveys. TRIGGERS - Use when user needs help with 360-feedback related tasks.

Collecting user feedback via NPS surveys, review analysis, sentiment analysis, feedback classification, and insight extraction reports. Use when establishing feedback loops.

Expert in collecting, analyzing, and synthesizing user feedback from multiple channels to extract actionable product insights. Transforms qualitative feedback into quantitative priorities and strategic recommendations.

Design 360-degree feedback programs with surveys. TRIGGERS - Use when user needs help with 360-feedback related tasks.

Makes AI responses feel more human by adding an inner dialogue loop that reflects on and revises the initial instinctive reply.

[OMX] Strict autonomous loop: $deep-interview -> $ralplan -> $ultragoal (+ $team if needed) -> $code-review -> $ultraqa

Use when designing or auditing AI product human-in-the-loop — write approval, proposal accept/reject, action confirm, spend caps, run detail, memory capture, or any AI action with irreversible blast radius. Trigger on confirmation UX, undo, cost-before-act, show-the-plan, citations, or keeping the owner in control.

Turns a brief product idea into working, verified code by orchestrating requirements analysis, planning, parallel implementation, QA, and multi-perspective validation.

Facilitate a structured sprint retrospective to surface insights and produce actionable improvements with owners and deadlines.

PUA Pro extensions: self-evolution notes, compaction state continuity, KPI-style summaries, flavor switching, and feedback tools.

Runs a multi-perspective review of a pull request, including code quality, tests, and potential silent failures, then reports findings ranked by severity.

Package a customer escalation for engineering, product, or leadership as a decision-ready handoff — impact, timeline, facts vs. hypotheses, and a specific ask with an owner.

PUA Pro extensions: self-evolution notes, compaction state continuity, KPI-style summaries, flavor switching, and feedback tools.

A runbook written in a wiki decays silently: the dashboard is renamed, the