「learning」の検索結果

Use when an ad account HIERARCHY needs designing or cleaning up — campaign, ad set, and ad structure by objective, a naming convention that keeps data readable, CBO versus ABO by stage, how many ad sets and creatives to run, and the minimum daily budget an ad set needs to exit learning. Trigger on 'account structure', 'campaign structure', 'naming convention', 'how many ad sets should I run', 'CBO or ABO', 'our account is a mess of duplicated campaigns'. Not for — scoring an existing account against 84 checkpoints, see `21-ads-audit-global`; pixel and conversion setup, see `53-tracking-setup-global`; budget across channels, see `54-media-plan-global`; scaling a winner, see `55-scaling-ads-global`.

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Zero-config goal-to-tasks engine. Takes any goal (software, pentest, business, learning), runs adaptive discovery, generates a validated spec, parses into TaskMaster tasks, creates an implementation plan, and executes with built-in CDD verification. Use when user says "PRD", "product requirements", "I want to build", or any goal-driven phrase.

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Zero-config goal-to-tasks engine (the Atlas engine). Takes any goal (software, pentest, business, learning), runs adaptive discovery via brainstorming, generates a validated spec, parses into TaskMaster tasks, and hands off to execution. Use when user says "PRD", "product requirements", "I want to build", invokes /atlas, or wants task-driven development.

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Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

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Your education and edtech brain trust. 25 named personas across 7 cells covering learning science, curriculum design, AI tutoring, higher-ed strategy, edtech platforms, assessment, and workforce development.

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Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.

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Teach a skill or concept at the user's current level, optionally preserving a lightweight multi-session learning record.

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Turn a customer problem into distinct ideas, a small prototype and a measurable learning test. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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Review session corrections, trigger misses and recurring agent mistakes to propose targeted skill-library changes. Use for retrospective, rework or routing-gap analysis before registry edits; load +common/common-learning-log for redacted evidence.

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Pre-write audit for skill violations: checks planned code against loaded skill anti-patterns before any file write. Use when writing Flutter/Dart/TS code or editing SKILL.md files with active project skills. Load as composite; on auto-fixed violation, also load +common/common-learning-log.

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User-invoked learning toolkit — guided reading, investigation, pattern extraction, or second opinions. Manual `/maestro-learn` only; NEVER auto-invoke for code exploration or analysis — route those intents to the analyze step via /maestro-next

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Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning…

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Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries; Use when learning about libraries, patterns…

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Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.

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Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

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Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

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Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.

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Adapts an existing K-12 lesson (math, ELA, science, or social studies) for students at different proficiency levels (below / at / above grade level). Load this skill BEFORE asking the teacher any clarifying question about the lesson, tiers, or student levels. Triggers on explicit asks to differentiate, tier, or scaffold a lesson, and on implicit signals like "my students are at different levels". Produces 1 teacher-facing differentiation plan + 3 student-ready tier documents as editable, printable documents in a single output turn (shared content is written once, so tiers cannot drift). Uses the Learning Commons Knowledge Graph when connected; works without it. This skill adapts a lesson the teacher brings or names. Not for creating a new lesson from scratch — a new-lesson request that asks for differentiated or leveled materials is k12-lesson-plan-creation's job, one package. Not for grading, rubrics, assessment feedback, or quizzes.

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This skill should be used only when the user explicitly asks to use `$ralph-specum-refactor`, or explicitly asks Ralph Specum in Codex to revise spec artifacts after implementation learnings.

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Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

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Capture learnings from the current session into the project-rules file (AGENTS.md, CLAUDE.md, or local override) so future sessions benefit. Use when the user says "revise the rules", "update AGENTS.md / CLAUDE.md with what we just learned", "save this to project memory", "remember this for next time", or at the end of a productive session when valuable context has emerged that is not yet documented. This complements agents-md-improver — improver audits, while this one captures.

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Show how vibecosystem works - agents, skills, hooks, and self-learning pipeline.

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Query the memory system for relevant learnings from past sessions using semantic search.

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。

Store a learning, pattern, or decision in the memory system for future recall.

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。