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概要と使いどころ
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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 ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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。
Plan a huge chunk of work (more than one agent session can hold) as a shared map of decision tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.
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。
Summarize authored commits over a user-specified time period into a concise update
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。
TypeScript and JavaScript expert with deep knowledge of type-level programming, performance optimization, monorepo management, migration strategies, and modern tooling. Use PROACTIVELY for any TypeScript/JavaScript issues including complex type gymnastics, build performance, debugging, and architectural decisions. If a specialized expert is a better fit, I will recommend switching and stop.
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。
Update an OpenSpec change by revising its existing planning artifacts and keeping them coherent with one another. Use when the user wants to revise a change's plan, fold new decisions into it, or reconcile its artifacts after an edit. Never edits code.
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。
Guides Stripe integration decisions across API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, tax and registrations (Stripe Tax, automatic_tax, product tax codes), Treasury financial accounts, integration options (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, restricted keys, webhooks, OAuth). Use when building, modifying, or reviewing any Stripe integration, including accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, collecting sales tax, VAT, or GST, creating connected accounts, or implementing secure key handling.
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。
Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
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 skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great. Use as a craft companion after Core's frontend, emil-design-engineering, and anim guidance.
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。
Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations. Only runs when explicitly invoked; it does not trigger on its own.
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。
Turn supplied meeting notes or transcripts into sourced minutes, keeping discussion, statements, confirmed/proposed decisions and proposed actions distinct.
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。
Initialize or inspect a project, maintain sourced current facts and record durable decisions. Use for project context or remember-this requests; other record types retain their owners.
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。
Guided, hands-on course teaching architects how to use Codex or Claude Code — six short modules, each built around an exercise on a bundled sandbox project (a fictional Brooklyn art museum expansion). Resumable across sessions via PROGRESS.md. Use when the user runs $learn or /as:learn, says they're new to AI-assisted project work, or asks how to learn it.
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。
Check agent-produced plans, tests, or code against the owner's actual intent. Find missing meaning, unrequested work, stale truth, unreachable decisions, unsupported completion claims, and implementation debt. Use before approval or merge.
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。
Statistical visualization on matplotlib + pandas. Distributions (histplot, kdeplot, violin, box), relational (scatter, line), categorical, regression, correlation heatmaps. Auto aggregation/CIs. Use plotly for interactive; matplotlib for low-level.
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。
Query RegulomeDB v2 GET REST API to score variants for regulatory function and retrieve overlapping evidence (TF binding, histone marks, DNase peaks, footprints, motifs, eQTLs, chromatin state). Scores range 1a (strongest) to 7 (none). Use for GWAS hit prioritization, regulatory variant annotation, cis-regulatory discovery. Use clinvar-database for pathogenicity; gwas-database for trait associations.
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。
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections.
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。
Scientific manuscript writing: IMRAD, citation styles (APA/AMA/Vancouver/IEEE), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA/ARRIVE), writing principles (clarity/conciseness/accuracy), venue-specific style. For LaTeX see companion assets.
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。
Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.
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。
Methodology for exploratory data analysis on scientific files. Decision frameworks by data type (tabular, sequence, image, spectral, structural, omics), quality assessment, report generation, format detection across 200+ formats. Use when given a data file for initial exploration or to pick an analysis before a pipeline.
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。
Statistical visualization on matplotlib with native pandas support. Auto aggregation, CIs, grouping for distributions (histplot, kdeplot), categorical (boxplot, violinplot), relational (scatterplot, lineplot), regression (regplot, lmplot), matrix (heatmap, clustermap), grids (pairplot, FacetGrid). Use for quick statistical summaries; matplotlib for fine control; plotly for interactive HTML.
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 ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。
Foundational software design principles applied specifically to robotics module development. Use this skill when designing robot software modules, structuring codebases, making architecture decisions, reviewing robotics code, or building reusable robotics libraries. Trigger whenever the user mentions SOLID principles for robots, modular robotics software, clean architecture for robots, dependency injection in robotics, interface design for hardware, real-time design constraints, error handling strategies for robots, configuration management, separation of concerns in perception-planning- control, composability of robot behaviors, or any discussion of software craftsmanship in a robotics context. Also trigger for code reviews of robotics code, refactoring robot software, or designing APIs for robotics libraries.
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。