3,532 件 ・ 関連度順
概要と使いどころ
Use when the user needs to find, prioritize, or engage B2B prospects — defining an ICP, tiering accounts, building or enriching lists, reading buying signals, designing multi-channel cadences, cold calling, LinkedIn outreach, routing inbound leads, or diagnosing weak pipeline generation. Also for live prospecting coaching. Not for closing (use sales-skill), copy (use copywriter-skill), or CAC analysis (use channel-expert).
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 needs to design, run, or audit product or GTM analytics — metric hierarchies and north stars, funnels, cohort retention, churn, pipeline velocity, forecasting, lead scoring, NRR, A/B tests, or analytical SQL — or uploads a dataset to analyze. Triggers on questions like "why did activation drop", "is this metric meaningful", "review my dashboard", or "what should I measure". For writing the spec that defines a feature's success metrics, use product-spec.
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 needs to deploy, operate, secure, or scale running infrastructure — cloud architecture (multi-region, VPC, FinOps), CI/CD and deployment strategies, secrets/IAM/supply-chain security, observability and SLO alerting, SRE and incident response, Kubernetes vs ECS and autoscaling, Terraform/Pulumi/CDK, or platform engineering and DORA metrics. Also for generating runbooks, pipeline configs, Terraform modules, or Kubernetes manifests. For choosing the system's architecture or stack before it runs (monolith vs microservices, which database), use software-architect.
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。
Review a data analysis for quality, correctness, and reproducibility. Covers data quality assessment, assumption checking, model validation, data leakage detection, and reproducibility verification. Use when reviewing a colleague's analysis before publication, validating an ML pipeline before production deployment, auditing a report for regulatory or business decision-making, or performing a second-analyst review in a regulated environment.
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。
Configure render settings, compositing nodes, output formats, and execute renders via Cycles or EEVEE engines using Python API or command-line interface. Use when automating render execution for batch processing, configuring quality and performance trade-offs, setting up compositing pipelines for post-processing, generating multiple output formats from a single render, or producing final output for publication or presentation.
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。
Manage R package dependencies using renv for reproducible environments. Covers initialization, snapshot/restore workflow, troubleshooting common issues, and CI/CD integration. Use when initializing dependency management for a new R project, adding or updating packages, restoring an environment on a new machine, troubleshooting restore failures, or integrating renv with CI/CD pipelines.
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。
Optimize Docker build times using layer caching, multi-stage builds, BuildKit features, and dependency-first copy patterns. Applicable to R, Node.js, and Python projects. Use when Docker builds are slow due to repeated package installations, when rebuilds reinstall all dependencies on every code change, when image sizes are unnecessarily large, or when CI/CD pipeline builds are a bottleneck.
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。
Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.
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。
Install and configure the putior R package for workflow visualization. Covers CRAN and GitHub installation, optional dependencies (mcptools, ellmer, shiny, shinyAce, logger, plumber2), and verification of the complete annotation-to-diagram pipeline. Use when setting up putior for the first time, preparing a machine for workflow visualization tasks, when a downstream skill requires putior to be installed, or restoring an environment after an R version upgrade or renv wipe.
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。
Evolve SKILL.md files from agent execution traces using a three-stage pipeline: trajectory collection from observed runs, parallel multi-agent patch proposal for error and success analysis, and conflict-free consolidation of overlapping edits via prevalence-weighting. Based on the Trace2Skill methodology.
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 policy-as-code enforcement using OPA Gatekeeper or Kyverno to validate and mutate Kubernetes resources according to organizational policies. Covers constraint templates, admission control, audit mode, reporting violations, and integrating with CI/CD pipelines for shift-left policy validation. Use when enforcing resource configuration standards, preventing security misconfigurations such as privileged containers, ensuring compliance before deployment, standardizing naming conventions, or auditing existing cluster resources against policies.
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。
Deploy Shiny applications to shinyapps.io, Posit Connect, or Docker containers. Covers rsconnect configuration, manifest generation, Dockerfile creation, and deployment verification. Use when publishing a Shiny app for external or internal users, moving from local development to a hosted environment, containerizing a Shiny app for Kubernetes or Docker deployment, or setting up automated deployment pipelines.
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。
Improve an existing R-based pictogram glyph for the visualization layer. Covers visual audit of the current glyph, diagnosis of specific issues (proportions, readability, glow balance), targeted modifications to the glyph function, re-rendering, and before/after comparison. Works for skill, agent, and team glyphs. Use when a glyph renders poorly at small sizes, its visual metaphor is unclear, it has proportion issues, the neon glow effect is unbalanced, or after adding new palettes or changing the rendering 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。
Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.
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 R-based pictogram glyphs for skill, agent, or team icons in the visualization layer. Covers concept sketching, ggplot2 layer composition using the primitives library, color strategy, registration in the appropriate glyph mapping file and manifest, rendering via the build pipeline, and visual verification of the neon-glow output. Use when a new entity has been added and needs a visual icon for the force-graph visualization, an existing glyph needs replacement, or when batch-creating glyphs for a new domain.
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 a Blender scene programmatically via Python (bpy) with objects, materials, lighting, camera, and environment configuration. Use when creating reproducible 3D visualization scenes, automating product or architectural rendering setup, generating multiple scene variations programmatically, building template scenes for batch rendering workflows, or integrating 3D visualization into data pipelines.
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。
Configure the putior MCP server to expose 16 workflow visualization tools to AI assistants. Covers Claude Code and Claude Desktop setup, dependency installation (mcptools, ellmer), tool verification, and optional ACP server configuration for agent-to-agent communication. Use when enabling AI assistants to annotate and visualize workflows interactively, setting up a new development environment with putior MCP integration, or configuring agent-to-agent communication via ACP for automated pipelines.
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 a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.
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。
Add PUT workflow annotations to source files using the correct language-specific comment prefix. Covers annotation syntax, skeleton generation via put_generate(), multiline annotations, .internal variables, and validation. Supports 30+ languages with automatic comment prefix detection. Use after analyzing a codebase and having an annotation plan, when adding workflow documentation to new or existing source files, or when documenting data pipelines, ETL processes, or multi-step computations.
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。
Application and library conventions for plain Python projects (no web framework): src/ package layout, CLI tooling (argparse/click/typer), configuration management (pydantic-settings), logging setup, module design, entry points, and packaging conventions. Activated automatically by python-plugin/stack.md as a convention skill for the development phase. Use this skill to: - Organise Python application code in a src/ layout with proper package structure. - Build CLI tools with argparse, click, or typer and register them as console scripts. - Manage configuration from environment variables using pydantic-settings. - Set up structured logging for production-grade applications. - Package a Python project correctly with pyproject.toml. Do NOT use this skill for: - Language idioms (type hints, dataclasses, enums, match/case) — see python-foundation:python-conventions. - Package manager commands (ruff, mypy, pip/poetry/uv) — see python-foundation:python-tooling. - Testing patterns — see python-foundation:pytest-testing. - Web framework patterns (Django/FastAPI/Flask) — see those 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。
Angular 18-21 project structure, standalone components vs NgModule, control flow (@if/@for/@switch + *ngIf/*ngFor legacy), decorators, dependency injection (inject() function), lifecycle hooks, pipes, Angular Universal SSR pointer. Use this skill to: - Detect project style (standalone vs NgModule) and apply matching patterns. - Pick correct decorators and DI approach. - Use modern control flow (@if/@for/@switch) in Angular 17+ projects. - Apply `inject()` function over constructor injection where appropriate. - Wire bootstrap correctly (bootstrapApplication for standalone, AppModule for legacy). Do NOT use this skill for: - State management (see angular-state-and-rx). - Routing (see angular-routing). - Forms (see angular-forms). - Testing (see angular-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。
NestJS module structure, dependency injection, lifecycle, configuration, exception handling, and logging conventions. Apply when implementing or modifying NestJS backend code. Use this skill to: - Structure feature modules (one feature = one module). - Wire DI correctly (constructor injection, scopes, custom tokens). - Set up ConfigModule, ValidationPipe, exception filters in main.ts. - Use lifecycle hooks for init and graceful shutdown. - Pick the right exception class for each error case. Do NOT use this skill for: - Decorator-specific patterns (see decorator-patterns). - ORM/data access (see nest-data-layer). - GraphQL/WebSockets/Microservices (see nest-advanced). - Testing (see nest-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。
Maven and Gradle build tool conventions for Java projects: build tool detection, wrapper usage, dependency management, BOMs, plugin configuration, semver, and multi-module layouts. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Detect which build tool is in use and invoke it correctly. - Manage dependencies safely (BOMs, version properties, no wildcard versions). - Configure compiler, test, and code-quality plugins. - Handle multi-module projects. Do NOT use this skill for: - Framework-specific build plugins (Spring Boot Gradle plugin — in spring-boot-plugin:spring-conventions). - CI/CD pipeline config.
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。