Results for “feedback-automation”

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

Automate Gatherup review management tasks using Composio's Gatherup toolkit via Rube MCP.

Design feedback patterns for user actions, including confirmations, status updates, and notifications.

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

Evolution Agent

Discover, audit, repair, adapt, and design bounded, verifiable AI-agent loops with explicit triggers, actions, stopping conditions, and guardrails.

Discovers, audits, repairs, adapts, and designs bounded AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs.

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.

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

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.

Discovers, finds, audits, repairs, adapts, crafts, runs, debriefs, saves, and prepares repeatable AI-agent loops for publication, treating loops as bounded feedback systems.

Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows. Use when deciding which agent actions require review, adding approve/reject or dual-control flows, preventing unauthorized autonomous effects, creating decision records, reducing rubber-stamping, or recovering safely from rejected, expired, or failed actions.

Analyzes repeated agent failures or noisy routing and proposes evidence-backed changes to instructions, skills, gates, or runbooks.

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

Rates an agent's own output on five axes — accuracy, completeness, clarity, actionability, conciseness — producing a structured scorecard with evidence and improvement suggestions.

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

Use this skill for GitHub Actions, checks, release gates, deployment automation, reproducible pipelines. Trigger when the task involves devops work related to CI CD Automation, production implementation, audits, debugging, strategy, or validation.

GAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper.

Produces an evidence-backed inventory of live automations (jobs, hooks, connectors, MCP servers, wrappers) and recommends keep/merge/cut/fix-next actions before rewriting anything.

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

Automate repetitive tasks and workflows using scripting, file watchers, scheduled jobs, CI triggers, and API polling to eliminate manual toil. Use when the user requests task automation or provides relevant inputs for this workflow.

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.

Automates repetitive infrastructure and deployment tasks with Terraform, OpenTofu, Ansible, GitHub Actions, and n8n, enforcing safe practices like plan review and confirmation before applying changes.

Full autonomous execution from idea to working code

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.

Analyze user feedback data to identify segments with sentiment scores, JTBD, and product satisfaction insights.