2,536 件 ・ 関連度順
概要と使いどころ
Build MindNode-inspired mind mapping interfaces — 8 node shapes, 27+ themes, layout algorithms (horizontal, vertical, radial with D3), keyboard shortcuts, focus mode, dual view. React + xyflow/react v12 + Zustand + Tailwind. Triggers on mind map, visual thinking tool, node-based diagram, brainstorm canvas, MindNode.
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 and record evidence-backed LLM and AI-search citation checks across approved assistant/search surfaces with timestamped queries, locales, modes, cited URLs, snippets, screenshots or transcripts, and caveats. Use this skill when the user asks whether ChatGPT, Perplexity, Claude, Gemini, Copilot, or another assistant cites a target site, but do not claim citation without direct evidence.
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。
Deep cross-domain synthesis engine for the vault. Triggers on "synthesize X", "what do I know about X", "connect ideas about X", "cross-domain analysis of X", "map everything in the vault about X". Searches comprehensively, surfaces non-obvious patterns and tensions, then challenges the user to generate original thought. The vault should surprise you — if synthesis only confirms what you already think, it failed.
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。
Design, build, refactor, or review terminal interfaces—full-screen TUIs, interactive prompts, and command-line tools. Use for terminal-app layout and UX, CLI behavior, ncurses-style tools, dashboards, REPLs, fzf-like pickers, and libraries such as Bubble Tea, Ratatui, Textual, or Ink, including requests that name a known TUI such as lazygit, k9s, btop, helix, or yazi instead of saying “TUI.” Do not use for browser or web UI, native GUI, editor or font configuration, or backend and shell work that has no terminal interface.
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。
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein and protein-ligand systems with PDBFixer, choose force fields and water models (AMBER14, CHARMM36m, ff19SB, GAFF2, TIP3P), solvate and add ions, run energy minimization, NVT/NPT equilibration and production MD on GPU, then analyze trajectories for RMSD, RMSF, radius of gyration, hydrogen bonds, native contacts, PCA and free energy surfaces. Use this skill for protein stability under mutation, ligand binding-mode and residence-time questions, conformational sampling, membrane proteins, and disordered ensembles. Also trigger on OpenMM, MDAnalysis, mdtraj, Simulation.step, LangevinMiddleIntegrator, PDBFixer, DCD or XTC trajectory, RMSD analysis, or production MD.
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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications.
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。
Provision and manage cloud infrastructure using Terraform with HCL modules, remote state backends, workspaces, and plan/apply workflow. Implement infrastructure as code patterns with variable management, output values, and state locking for team collaboration. Use when provisioning new cloud infrastructure, migrating from ClickOps or CloudFormation to declarative IaC, managing multi-environment infrastructure, versioning infrastructure changes alongside application code, or enforcing standards through reusable modules.
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。
Cultivate defensive situational awareness, threat assessment, and mental clarity under pressure. Covers the Cooper color code awareness system, body language reading and intent detection, verbal de-escalation, moving mindfulness in public spaces, combat focus and the OODA loop, rapid grounding techniques for acute stress, context-specific integration, and ongoing review and refinement of awareness skills. Use when entering unfamiliar or potentially hostile environments, needing to assess a situation for safety, de-escalating a verbal confrontation, or integrating awareness practice into daily movement.
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。
Cross-session shared memory for agents. Use at session start to load the persistent bias-log via `mcp__memex__recent_observations`, mid-session to search prior context with `mcp__memex__search`, and immediately when a bias surfaces to log a new `observation` via `mcp__memex__add`. The six-layer reconstruction trail (raw → embedded → indexed → graphed → curated → reflexive) means memex stores facts AND the agent's own reasoning patterns across sessions. Prefer querying memex before re-deriving prior architectural decisions; re-derivation is itself evidence that the trail is incomplete.
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。
Log a bias-log / vipassana observation to the memex store the moment a reasoning pattern surfaces, mid-session or at close. Use when you catch a bias in your own reasoning (anchoring, confirmation, trust-the-summary), when a pace tell or verification gap appears, or when meditate/observe produces a reflection worth persisting across sessions. Primary path pipes the body to `memex add` over stdin (`--title` required) then runs the `meditate-vipassana` extractor to make it queryable; secondary path appends a numbered bullet to `docs/OBSERVATIONS.md`.
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。
Work with the full set of Maxwell's equations in integral and differential form to analyze electromagnetic fields, waves, and energy transport. Use when applying Gauss's law, Faraday's law, or the Ampere-Maxwell law to boundary value problems, deriving the electromagnetic wave equation, computing Poynting vector and radiation pressure, solving for fields at material interfaces, or connecting electrostatics and magnetostatics to the unified electromagnetic framework.
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。
Instrument applications with OpenTelemetry for distributed tracing, including auto and manual instrumentation, context propagation, sampling strategies, and integration with Jaeger or Tempo. Use when debugging latency issues in distributed systems, understanding request flow across microservices, correlating traces with logs and metrics for root cause analysis, measuring end-to-end latency, or migrating from legacy tracing systems to OpenTelemetry.
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 AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.
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。
Set up centralized log aggregation with Loki and Promtail (or ELK stack), including log parsing, label extraction, retention policies, and integration with metrics for correlation. Use when consolidating logs from multiple services into a searchable system, replacing local log files with centralized queryable storage, correlating logs with metrics and traces, implementing structured logging with label extraction, or troubleshooting production incidents requiring cross-service log analysis.
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。
Unify metrics, logs, and traces for cohesive debugging. Implement exemplars for log-to-trace linking, build unified dashboards using RED/USE methods, and enable rapid root cause analysis across observability signals. Use when investigating complex incidents spanning multiple systems, reducing mean time to resolution, implementing distributed tracing, or moving from siloed tools to a unified observability platform.
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 reusable Shiny modules with proper namespace isolation using NS(). Covers module UI/server pairs, reactive return values, inter-module communication, and nested module composition. Use when extracting a reusable component from a growing Shiny app, building a UI widget used in multiple places, encapsulating complex reactive logic behind a clean interface, or composing larger applications from smaller, testable units.
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。
Analyze pre-trained generative diffusion models (Stable Diffusion, DALL-E, Flux) by computing quality metrics (FID, IS, CLIP score, precision/recall), inspecting noise schedules, extracting and visualizing attention maps, and probing latent spaces. Use when evaluating a pre-trained generative diffusion model's output quality, comparing noise schedule variants, analyzing cross-attention patterns for text-conditioned generation, interpolating between latent codes, or detecting out-of-distribution inputs.
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。
Spring Data JPA entity design, repository patterns, query methods, JPQL, Flyway/Liquibase migrations, and N+1 avoidance. Apply on Spring Boot projects with spring-boot-starter-data-jpa. Pairs with spring-boot-plugin:spring-conventions and java-foundation:java-conventions. Use this skill to: - Design JPA entities with proper annotations, ID strategy, and lazy loading. - Write JpaRepository interfaces with derived query methods and JPQL @Query. - Avoid N+1 queries using @EntityGraph or JOIN FETCH. - Manage schema evolution with Flyway or Liquibase. - Project query results with Spring Data Projections and DTOs. Do NOT use this skill for: - Service or controller patterns — see spring-boot-plugin:spring-conventions. - Build tool setup — see java-foundation:build-tooling. - Test slices (@DataJpaTest) — see java-foundation:jvm-testing.
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。
Modern Java idioms for any JVM project (Java 17+): records, sealed types, text blocks, pattern matching, Optional discipline, streams, immutability, null safety, package layout, var usage, and code organisation. Apply when the project is a Java 17+ project. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write self-documenting, immutable-by-default value types with records. - Model closed type hierarchies with sealed classes and interfaces. - Handle optionality explicitly with Optional instead of returning null. - Write expressive pipelines with Stream API without performance footguns. - Lay out packages consistently (feature-first or layer-first) and keep class responsibilities tight. Do NOT use this skill for: - Framework-specific idioms (Spring annotations, JPA mappings — those live in framework plugin skills). - Build tooling (Maven/Gradle) — see java-foundation:build-tooling. - Testing patterns — see java-foundation:jvm-testing.
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。
Composer conventions for any PHP project: dependency management, version constraints, PSR-4 autoloading, scripts, platform requirements, and the composer.json vs composer.lock contract. Stack-agnostic — referenced by every PHP plugin in the marketplace. Use this skill to: - Read composer.json to detect the PHP version, framework, and key packages before writing code. - Add dependencies with correct version constraints and the right require vs require-dev placement. - Configure PSR-4 autoloading and regenerate the autoloader after adding namespaces. - Use composer scripts and platform config consistently. Do NOT use this skill for: - PHP language idioms — see php-foundation:php-conventions. - Testing setup and runners — see php-foundation:php-testing. - Framework-specific package guidance (Laravel/Symfony bundles) — those live in framework plugin skills.
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 consistent nn_cfd W&B experiment comparison reports from selected run IDs using wandb and wandb_workspaces. Use this whenever the user asks to report experiment results, create a W&B Report, compare CFD training/eval runs, document baseline-vs-variant outcomes, reproduce the project experiment report template, or publish a leaderboard-style report for milieu/nn_cfd.
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 loading core files in GDB or LLDB, enabling core dump generation via ulimit or coredumpctl, mapping symbols with debuginfod, or extracting backtraces from production segfaults.
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 explaining or building on Linux container primitives: namespaces, cgroups v2, overlayfs, runc and the OCI spec, seccomp-bpf, capabilities, or escapes. Not for VM isolation: use qemu-kvm.
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 managing Cargo workspaces, feature flags, build scripts, CI caching, dependency auditing, or Cargo.lock with Rust.
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。