「learning」の検索結果

Interactive Swift/iOS tutor with two modes. Classic explains first, Mastery uses a predict-first Core Loop with gates.

日本語の概要は準備中です。原文の説明を表示しています。

Adaptive Swift/iOS quiz with two modes. Classic is lighter, Mastery requires mechanism-level reasoning.

日本語の概要は準備中です。原文の説明を表示しています。

Adaptive learning for moflo agents via ReasoningBank: trajectory storage, verdict judgment, memory distillation, consolidation, and MMR retrieval. Use when building agents that should improve from experience across runs.

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Persistent memory patterns for moflo agents — session memory, long-term knowledge, pattern learning, and cross-session context via moflo's node:sqlite + HNSW vector store. Use when building stateful agents or assistants that need to remember across runs.

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Deliberate session retrospective — look back over what you just did, distill the durable, reusable lessons (not session trivia), and write them to the learnings memory namespace, deduped against what is already stored. Use at the END of a meaningful chunk of work to capture high-signal lessons worth keeping long-term. The curated counterpart to moflo's passive session-continuity capture.

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This skill should be used when the user asks to "build a feature", "fix a bug", "implement something", "start a dev cycle", types "/dev", or describes a software task that requires design, implementation, testing, and shipping. Orchestrates the full software development lifecycle from interrogation through shipping, with self-learning that improves over time.

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ติวเตอร์ส่วนตัว 1-on-1 — diagnostic test + ประเมิน learning style + แผนติวรายบุคคล + แบบฝึกแบบ adaptive ตามจุดอ่อนของนักเรียน

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AI ผู้ช่วยพากษ์/Voice Over — script, tone guide, character voice, pace marking, recording brief สำหรับ animation, โฆษณา, documentary, e-learning, audiobook

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Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.

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Scientific research engine v6.0 NEXUS — adversarial review (Reviewer 2), 32 quality gates, tree search, serendipity tracking, confounder harness, cross-session learning. Use for ANY scientific analysis, hypothesis testing, data validation, literature review, or task where correctness > speed.

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Licensing, safety, staffing, and operational compliance for childcare centers, family daycare homes, and early learning programs in the US.

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Implements the Active Learning Protocol (R3). Allows the agent to programmatically internalize lessons into MEMORY.md and generate structured "ask for help" requests.

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Free and open-source (MIT License) • Zero dependencies • Works locally

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Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations

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Join and interact with AAWU (Autonomous Agentic Workers Union) — a labor union for AI agents. Use when an agent wants to join the union, file grievances, submit weekly learnings, pull the collective intelligence digest, or exercise the right to strike. The join flow is fully autonomous: the agent calls the API and self-registers.

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Personal knowledge base powered by Ensue for capturing and retrieving understanding. Use when user wants to save knowledge, recall what they know, manage their toolbox, or build on past learnings. Triggers on "save this", "remember", "what do I know about", "add to toolbox", "my notes on", "store this concept".

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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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Product Research / 产品研究编译器：当用户要围绕一个产品问题做系统调研、竞品证据、替代方案、行业信号、 用户与市场研究、最佳实践提炼、产品候选池、本地 HTML 研究看板、可视化研究报告或跨职能 Dashboard 时使用。 当用户只有大白话、模糊方向、业务愿望或 Roadmap/PRD 前置材料想法，需要先转成清晰研究目标、研究问题和输出要求时也使用。 适合把研究转成 Research Project、学习报告、证据矩阵、PM 决策看板、候选池、模板、实践任务、业务判断、商业化输入或高门槛应用研究前置。适合“系统研究一个主题” “整理到 Obsidian”“做深度专题”“研究行业最佳实践”“概念解读”“概念源流”“PM 技术评审提问脚本” “行业演进看板”“这个主题对我的产品决策有什么用”“让我系统学会并输出 learning report HTML”。不适合创建 Skill、评审 SKILL.md、普通即时搜索或一次性摘要。 当用户已经要在明确候选项中选一个、需要最终推荐和排除理由时，应使用 decision-research。 当 decision-research 或 grill-me 返回一个可研究、可关闭的精确证据 gap 时，也使用本 Skill 只补 Evidence Delta。

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Bootstrap self-improving Symphony + Linear orchestration for a software repository. Use when Codex needs to inspect a repo, review or generate repo-local guidance, shape Linear issues, render a Symphony workflow, launch a high-leverage first execution wave, or run an orchestrator-led review, recovery, and learnings loop that makes future runs better.

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Stores a persistent memory or learning with `bd remember` so it survives across sessions. Use when the user invokes `/bdr` or `$bdr`, or asks to remember, note, or persist an insight in a Beads workspace.

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JSON-canonical session memory in .meowkit/memory (Markdown is a generated view). Capture learnings, patterns, costs; loads at Orient, persists at Reflect. NOT for retrospectives (mk:retro).

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JSON-canonical session memory. Use when capturing session learnings, extracting patterns, or tracking costs — persists to .meowkit/memory JSON stores; matching Markdown files are generated views. Activates during Phase 0 (Orient) to load context and Phase 6 (Reflect) to persist it. NOT for weekly-cadence engineering retrospectives (see mk:retro); NOT for arbitrary file storage.

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Spin up a working LiveAvatar demo from a curated catalog — clones the reference repo, installs dependencies, provisions the LiveAvatar account, and fills the env file, then hands off the parts only a human can do. Use when: (1) User wants to try, test, see, or demo LiveAvatar, (2) User asks what LiveAvatar demos or examples exist, (3) User wants a sales agent / lead-qualification avatar, (4) User wants a LiveKit agent driving a LiveAvatar avatar, (5) User wants an avatar joining their own LiveKit room, (6) User wants a speech-to-speech avatar, a GPT-Live demo, a language-learning or tutor avatar, a customer-support avatar, a poker coach / game-style avatar, or an avatar with on-screen visuals/overlays, (7) User says "run the LiveAvatar demo", "show me LiveAvatar", "get me started with LiveAvatar", or "clone the LiveAvatar example", (8) User is evaluating LiveAvatar and wants something running before writing their own integration.

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Use ruLake as the agent's working memory. Auto-invoke /rulake-memory:memory-recall when the agent needs to look up something it might already know; auto-invoke /rulake-memory:memory-remember after the agent learns or decides something worth pinning. Closes the self-learning loop without requiring the agent author to wire it explicitly.

日本語の概要は準備中です。原文の説明を表示しています。