Turn the current conversation and codebase context into a structured implementation spec, then publish it to the configured project issue tracker.
Direktori skill
Temukan skill yang dapat digunakan kembali untuk AI agents.
Setiap rekomendasi tetap terhubung dengan repositori, audit, dan jalur pemasangannya.
Hasil pencarian: conversation
Direktori bahasa InggrisBreak a plan, spec, or conversation into independently actionable tracer-bullet tickets with explicit blocking relationships.
Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation.
Export and Share your ChatGPT conversation history
Voice AI SDK is a reusable Android library that gives any app a full voice-driven AI conversation pipeline in minutes. Voice Assistant + Android Voide AI + SDK + MVVM + Kotlin
desktop app to browse and analyze your Claude Code conversation history
A plugin for OpenCode that provides dynamic skill loading, context injection, and other tools for using reusable AI agent skills.
Claude Code plugin that generates individualized knowledge systems from conversation. You describe how you think and work, have a conversation and get a complete second brain as markdown files you own.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation
Installable agent skills for creating and managing reversible themes for AI desktop apps like Codex and WorkBuddy.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting โ e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.