菜单
Orchestrate continual learning by delegating transcript mining and AGENTS.md updates to agents-memory-updater. Use when the user asks to mine prior chats, maintain AGENTS.md, or run the continual-learning loop.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Search, install, update, and rate AI agent skills from agentskill.sh (100,000+ skills). Use when the user asks to find skills, install extensions or plugins, discover new capabilities, check what skills are available, or says "how do I do X" when a skill might help. Also handles listing installed skills, checking for updates, rating skills, removing skills, and scanning skills for security issues. Triggers on: /learn, find skill, install skill, search skills, what skills, add capability, get plugin, check skill safety.
Review and improve AI agent skills (SKILL.md files) against best practices from the Agent Skills specification and Anthropic's authoring guidelines. Scores skills on 10 quality dimensions, identifies specific issues, and rewrites problem areas. Use when creating, editing, auditing, or improving agent skills. Triggers on: review skill, improve skill, audit SKILL.md, check skill quality, skill best practices, optimize skill description.
AI-native product thinking and modern product design philosophy. Every product should consider AI copilots, semantic search, intelligent automation, conversational workflows, contextual AI assistance, and recommendation systems. Products must feel modern, premium, intelligent, polished, and production-ready. Use when designing new features, reviewing product roadmap, or evaluating whether AI capabilities would enhance a workflow. Covers Azure OpenAI, OpenAI, Anthropic, RAG pipelines, vector DBs, semantic indexing, and AI workflow orchestration.
Autonomous senior-engineer bug fixing workflow. Investigates independently, inspects logs, traces root causes, reproduces issues, fixes thoroughly, and validates correctness — without asking for unnecessary guidance. Use whenever encountering bugs, failing builds, broken UX, CI failures, test failures, regressions, or any unexpected behavior. Operates the way a staff engineer would: hypothesis-driven, evidence-driven, root-cause-driven.
Aggressive consumer-product improvement doctrine. Runs the Analyze → Critique → Reimagine → Stress Test → Improve → Refine cycle on any shipped or in-flight feature. Evaluates UX friction, cognitive load, perceived intelligence, retention mechanics, delight, AI-native opportunities, and five user perspectives (first-time, daily, power, mobile, impatient). Use after every implementation pass, when auditing existing UI, when expanding a feature, when the user shares product feedback, or proactively to elevate "functional but boring" surfaces into premium consumer-grade experiences. Mandates feature-ecosystem thinking, not isolated screens.
Agent Performance Optimization Workflow workflow skill. Use this skill when the user needs Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
| name | continual-learning |
| description | Orchestrate continual learning by delegating transcript mining and AGENTS.md updates to agents-memory-updater. Use when the user asks to mine prior chats, maintain AGENTS.md, or run the continual-learning loop. |
| disable-model-invocation | true |
Keep AGENTS.md current by delegating the memory update flow to one subagent.
Use when the user asks to mine prior chats, maintain AGENTS.md, or run the continual-learning loop.
agents-memory-updater.