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概要と使いどころ
Plan a unit using backwards design from desired outcomes through assessment evidence to learning activities. Use when starting a new unit or redesigning an existing one from standards.
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Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.
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Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
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。
Use when wrapping up a session before ending. 4 parallel subagents detect doc updates, repeated patterns, learning points, and follow-ups, then a verifier deduplicates and presents choices. Triggers on /session-wrap, session wrap, session cleanup, end of session.
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。
Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.
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。
Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.
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。
Use when the user wants to learn Godot while building — teaching mode that explains the concept, the editor setup, and what to verify, instead of just delivering code. Triggers on "teach me", "explain as we go", "I'm learning Godot", "guide me", "walk me through", "help me understand".
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。
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
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。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
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。
Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies, reinforcement learning, scientific workflows, or other expensive black-box evaluations where reading the project can improve trial selection.
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。
The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules fr
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We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positi
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The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.
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。
This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in
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A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.
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Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".
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Captures executable contracts and coding conventions into .trellis/spec/ documents. Use when learning something valuable from debugging, implementing, or discussion that should be preserved for future sessions.
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Seller storefront analysis and competitive intelligence for Amazon. Analyzes seller revenue estimation, product portfolio strategy, growth trajectory, and market positioning. Reverse-engineer successful seller strategies and identify expansion opportunities. Use when the user asks about analyzing sellers, competitor seller analysis, seller revenue estimation, storefront analysis, seller strategy, or learning from successful Amazon sellers.
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Applies Eric Ries's Lean Startup methodology for building products under extreme uncertainty. Use when iterating toward product/market fit, designing MVPs, deciding whether to pivot or persevere, setting up actionable metrics, or accelerating the Build-Measure-Learn loop. Triggers include 'how do we test this idea fast', 'what should our MVP look like', 'our metrics look good but we're not growing', 'should we pivot', 'we're building features no one uses', 'how do we measure validated learning', 'vanity metrics vs real metrics', 'how to do innovation accounting'. NOT for companies with proven product/market fit scaling a known playbook (use Crossing the Chasm), not for determining Market Type (use Four Steps), not for sales methodology (use SPIN Selling), not for pricing strategy (use Monetizing Innovation).
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。
Minimal MDA source demonstrating relationship-graph footnotes and metadata.mda.* MDA-extended fields. Use as a reference fixture when learning the MDA source format.
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。
Blameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning
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Dung khi da co winner va muon tang quy mo ma khong lam hong — 7 dieu kien du de scale, tang doc +20-30% moi lan, mo rong ngang bang tep va kenh moi, va rui ro reset learning phase. Kich hoat khi user nhac 'scale ads', 'tang budget', 'nhan doi ngan sach', 'ads dang tot muon day manh', 'horizontal scaling', 'tang chi tieu'. Quy tac cung: khong tang budget khi CPL dang xau. Khong dung cho — CPL dang xau thi chay skill 21-audit-ads-performance truoc; nhan tep warm thi dung skill 56-retargeting-plan.
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。
Use when a WINNING ad has to be scaled without breaking it — readiness checks, vertical scaling at plus 20 to 30 percent per step, horizontal scaling into new audiences, creatives, and channels, learning-phase reset risk, how Advantage+ and Performance Max behave when scaled, creative refresh cadence, and the signals that mean stop. Trigger on 'scale ads', 'increase the ad budget', 'scale a winner', 'CPA went up after I scaled', 'how fast can I scale', 'it worked at 50 a day and broke at 200'. Not for — fixing an account that is not working yet, see `03-performance-eval-global`; warm audience plans, see `56-retargeting-plan-global`; proving the winner first, see `19-ab-test-setup-global`.
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。