765 件 ・ 関連度順
概要と使いどころ
Guide Claude to use SigCLI correctly — check auth, login, get credentials, configure providers, and onboard new websites. Trigger when using sig commands, editing ~/.sig/config.yaml, or needing authenticated API access.
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, edit, replicate, import, and export draw.io diagrams with an offline YAML-first workflow: architecture, network topologies, flowcharts, UML/ER, org charts, Mermaid/CSV conversion, existing .drawio bundles, style presets, themes, and non-publication formula diagrams. For publication figures (paper, thesis, IEEE, camera-ready) use drawio-academic-skills instead.
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。
Fix Flutter macOS build failure caused by duplicate camera plugins. Use when: (1) macOS build fails with "Ambiguous use of 'CameraMacosPlugin.register(with:)'" error, (2) Both camera_macos and camera_macos_plus are in pubspec.yaml, (3) Build worked before adding a new camera package. The plugins define the same class name causing Swift registration conflicts.
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。
Creates structured context summaries for continuing work across AI sessions. Use when ending a session to enable seamless continuation in a new session without losing context.
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。
Generate executable code together with formal proofs certifying safety and correctness properties in Isabelle/HOL or Coq. Use when building verified software, safety-critical systems, or when formal guarantees are required. Produces code with accompanying proofs for memory safety, bounds checking, functional correctness, invariant preservation, and termination. Supports extraction to OCaml/Haskell/SML and integration with existing codebases.
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。
Extract abstract mathematical models from functional code (Haskell, OCaml, F#) for formal reasoning in Isabelle/HOL. Use when users need to: (1) Convert functional programs to Isabelle definitions, (2) Extract high-level algorithm essence from implementation code, (3) Generate formal specifications and properties from code, (4) Create verification-ready models that capture mathematical properties while abstracting away implementation details. Focuses on structural recursion, algebraic data types, higher-order functions, and invariant extraction.
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 analyzes configuration files to detect inconsistencies, conflicts, missing keys, and divergent values across environments, versions, or modules. Use when managing multi-environment configurations, detecting config drift, validating configuration changes, or ensuring consistency across microservices. Supports JSON, YAML, TOML, INI, XML, .env, and properties files. Identifies security issues like hardcoded secrets and provides actionable resolution 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。
Analyze and improve business processes. Trigger with "this process is slow", "how can we improve", "streamline this workflow", "too many steps", "bottleneck", or when the user describes an inefficient process they want to fix.
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。
GitHub repository automation (CI/CD, issue templates, Dependabot, CodeQL). Use for project setup, Actions workflows, security scanning, or encountering YAML syntax, workflow configuration, template structure 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。
Report which kasetto-installed skills and MCP servers are actually being used across the AI agents on this machine, and render a branded HTML dashboard of the result. Use whenever the user asks what agent assets they actually use, which skills or MCPs are dead weight, what to prune or clean up from kasetto.yaml, why their context is bloated with unused MCP servers, whether a skill has ever been invoked, or wants a usage report, audit, or dashboard of their agent setup. Also trigger on "kst ai assets usage", "ai asset usage", "what skills do I actually use", "which MCPs are worth keeping", "audit my agent assets", "am I using all these skills", or any request to review, trim, or justify an installed agent toolkit.
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。
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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 adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.
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。
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
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。
A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.
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。
Symfony UX Icons for rendering SVG icons in Twig templates. Supports 200,000+ Iconify icons (Lucide, Heroicons, Tabler, Material Design, etc.), local SVG files, and custom icon sets with aliases. Use when displaying icons, configuring icon defaults, importing or locking on-demand icons, creating icon aliases, or styling SVG icons with CSS. Code triggers: <twig:ux:icon />, ux_icon(), UX_ICONS_DEFAULT_ICON_ATTRIBUTES, icon.yaml, icons/, iconify:, lucide:, heroicons:, tabler:, mdi:, bin/console ux:icons:lock, bin/console ux:icons:import. Also trigger when the user asks "how to add an icon", "how to use Lucide/Heroicons/Tabler icons", "how to render an SVG icon in Twig", "how to lock icons for production", "how to create icon aliases", "how to style an icon", "icon not found", "icon not rendering". Do NOT trigger for interactive maps (use ux-map) or general Twig components (use twig-component).
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。
Configures Envoy, the high-performance C++ edge and service proxy, as an API gateway, load balancer or service mesh sidecar with L4/L7 routing, retries, circuit breaking, rate limiting, TLS and observability. Use when a user asks to set up Envoy, write an envoy.yaml, route traffic to backends, add health checks or rate limits, do a canary split, read Envoy admin stats, or understand the proxy behind Istio and Envoy Gateway.
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。
Detox is a gray-box end-to-end testing framework for React Native apps: it runs Jest tests against the app on an iOS simulator or Android emulator and waits for the app to go idle before each step. Use when the user wants to set up Detox, write or fix React Native E2E tests, debug a test that hangs or cannot find an element, or run Detox in CI. Also use when the user mentions "detox," "React Native E2E," "gray-box testing," "detox build," or "Wix Detox." For black-box testing of any mobile app, see appium; for simple YAML UI flows, see maestro.
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。
Generate professional CVs and resumes from structured YAML data using rendercv. Use when a user asks to create a CV, build a resume, generate a PDF resume, format a curriculum vitae, make a professional CV from their experience, or convert career data into a polished resume document. Supports multiple templates and PDF output.
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。
Continue is an open-source AI code assistant for VS Code, JetBrains and the terminal (the cn CLI) that works with any LLM: Claude, GPT, Gemini, Ollama and other local models. Use when a user asks to configure Continue with config.yaml, add chat or tab autocomplete models, write rules or custom prompts, connect MCP servers, run cn headless in scripts, or migrate from config.json. Continue was acquired by Cursor in June 2026: the release from that month is final and the Hub is offline, so the skill covers local, self-configured 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。
CodeRabbit reviews pull requests with AI on GitHub, GitLab, Azure DevOps and Bitbucket, and reviews local changes from a CLI before they are pushed. Use when a user asks to set up automated PR reviews, write or validate a .coderabbit.yaml, add path-specific review instructions, control a review with @coderabbitai commands, or run coderabbit review from the terminal or a coding agent.
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。
Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.
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 integrating Plaid, Stripe, payment processors, or financial APIs - covers account linking, payment processing, KYC/AML compliance, and webhook handlingUse when ", " mentioned.
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。
CI/CD is the backbone of modern software delivery. Continuous Integration catches bugs early. Continuous Deployment gets features to users fast. But poorly designed pipelines can be slow, flaky, and a source of constant frustration. This skill covers GitHub Actions (the most popular), GitLab CI, and general pipeline design principles. The focus is on pipelines that are fast, reliable, and maintainable. 2025 reality: Your pipeline is infrastructure code. It deserves the same care as your application. A flaky test in CI is worse than no test - it teaches developers to ignore failures. Use when "github actions, gitlab ci, ci cd, pipeline, workflow, deploy automation, continuous integration, continuous deployment, build pipeline, yaml workflow, ci-cd, github-actions, gitlab-ci, devops, automation, deployment, pipelines" mentioned.
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 fuzz test scaffolding for parsers handling external input (YAML, JSON, config files, user input). Seeds corpus from existing fixtures and runs initial pass. Use when implementing or changing a parser, or any code that ingests user input, webhook payloads, API responses, or file formats.
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。