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概要と使いどころ
Create a minimal Algolia JavaScript v5 indexing and search proof using a disposable index. Use when verifying credentials, learning the client boundary, or proving first connectivity. Trigger with "Algolia hello world", "first Algolia search", or "test Algolia setup".
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。
Prompt engineering patterns including structured prompts, chain-of-thought, few-shot learning, and system prompt design
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。
Auto-extract patterns from coding sessions, track corrections, and build reusable knowledge with confidence scoring
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。
Establish durable brand and voice context for cross-skill consumption. Generates BRAND.md (audience, positioning, do/don't editorial rules, taboo phrases, competitor differentiation) and VOICE.md (existing persona JSON re-expressed as readable prose), both written to the project root. When present, the blog orchestrator loads these files, fenced as untrusted data, for write, rewrite, brief, outline, calendar, and strategy workflows. Pairs with blog-persona, which manages the structured persona JSON. Use when the user says "blog brand", "create brand context", "BRAND.md", "VOICE.md", "establish editorial brand". Not for learning a voice from existing posts (blog-style) or managing persona JSON (blog-persona).
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。
Makes systems debuggable and reliably operable — instrumentation, alerting that is worth waking for, service objectives, and learning from failure. Use this to instrument a service, fix alerting that is ignored, set error budgets or reliability targets, prepare for on-call, or run a blameless post-incident review.
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。
Designs worksheets, workbooks and practice material that actually teach — sequencing difficulty, managing cognitive load, writing instructions a child can follow unaided, and building answer keys that hold up. Use this to design or fix a worksheet or workbook, decide how much practice a skill needs, structure a page so it can be completed without an adult reading it aloud, or diagnose material that looks right and does not work.
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。
Builds questions and assessments that measure what they claim — matching item type to the thing being assessed, writing distractors that diagnose, and telling a question that measures learning from one that measures reading speed or test-taking. Use this to write or review assessment items, design a unit check or diagnostic, work out why scores do not match classroom performance, or decide how much evidence a claim about mastery actually 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。
Aligns learning material to the standard it claims — choosing the right framework for the grade and subject, citing standards by code rather than reproducing their text, and telling a real alignment from a decorative one. Use this to align a worksheet, lesson or workbook to Common Core, NGSS or a state framework, to check an alignment claim before publishing, or to decide what may lawfully be printed on the cover.
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。
Builds capability — skills gaps, career frameworks, training that transfers to the job, and internal mobility. Use this to design a career ladder, close a capability gap, decide whether to build or hire a skill, structure onboarding into a role, or work out why training keeps failing to change anything.
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。
Record important information to long-term memory for learning user preferences, successful patterns, and error lessons. When you need to remember user preferences, save successful patterns, or record lessons from errors.
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 Video Notes skill for video content analysis and note generation. Extract key information from videos for learning, meetings, and content summarization. Use when user wants to analyze video content or generate notes from videos.
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。
基于本能的学习系统，通过钩子观察会话，创建具有置信度评分的原子本能，并将其进化为技能/命令/代理。
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。
一种基于本能（Instinct）的训练系统，通过钩子（Hooks）观察会话，创建带有置信度评分的原子化本能，并将其进化为技能（Skills）、命令（Commands）或智能体（Agents）。
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。
自动从 Claude Code 会话（Session）中提取可重用的模式（Pattern），并将其作为已学习的技能（Skill）保存以备将来使用。
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。
自动从Claude Code会话中提取可重复使用的模式，并将其保存为学习到的技能以供将来使用。
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。
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
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。
基于本能（Instinct）的学习系统，通过钩子（hooks）观察会话，创建带有置信度评分的原子本能，并将其演化为技能（Skills）、命令（Commands）或智能体（Agents）。v2.1 版本增加了项目作用域（project-scoped）的本能，以防止跨项目污染。
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。
Spawn three parallel review subagents over the active transcript, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.
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。
Machine learning toolkit for genomic interval (BED) data; use it when you need to tokenize BED collections and train embeddings for regions/cells/labels, build consensus peak universes, or run similarity search and downstream ML on chromatin accessibility datasets.
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。
Generates complete tumor immune-infiltration-guided bulk-transcriptome diagnostic biomarker and machine-learning research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, immune infiltration estimation, immune-linked module discovery, consensus feature selection, diagnostic modeling, nomogram construction, clinical association, and optional prognostic extension or validation. Covers five study patterns (immune-cell-first diagnostic workflow, immune-module-to-biomarker workflow, consensus-ML biomarker workflow, diagnostic-plus-prognostic hybrid workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, reference literature pack, and self-critical risk review.
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。
Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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。
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.
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。
Archive session learnings, debugging solutions, and deployment logs to .archive/yyyy-mm-dd/ as indexed markdown with searchable tags. Use when completing a significant task, resolving a tricky bug, deploying, or when the user says "archive this". Maintains .archive/MEMORY.md index for cross-session knowledge reuse.
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。
High-performance numerical computing and machine learning workflows using JAX. Supports array operations, automatic differentiation, JIT compilation, RNN-style scans, map/reduce operations, and gradient computations. Ideal for scientific computing, ML models, and dynamic array transformations.
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。