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Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.

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。

Writes and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and integrations on the Salesforce platform. Use when developing Salesforce applications, customizing CRM workflows, managing governor limits, bulk processing, or setting up Salesforce DX and 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。

Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.

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 monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity 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。

Builds, optimizes, and debugs cross-platform mobile applications with React Native and Expo. Implements navigation hierarchies (tabs, stacks, drawers), configures native modules, optimizes FlatList rendering with memo and useCallback, and handles platform-specific code for iOS and Android. Use when building a React Native or Expo mobile app, setting up navigation, integrating native modules, improving scroll performance, handling SafeArea or keyboard input, or configuring Expo SDK projects.

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。

Integrates with Atlassian products to manage project tracking and documentation via MCP protocol. Use when querying Jira issues with JQL filters, creating and updating tickets with custom fields, searching or editing Confluence pages with CQL, managing sprints and backlogs, setting up MCP server authentication, syncing documentation, or debugging Atlassian API integrations.

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 building Django web applications or REST APIs with Django REST Framework. Invoke when working with settings.py, models.py, manage.py, or any Django project file. Creates Django models with proper indexes, optimizes ORM queries using select_related/prefetch_related, builds DRF serializers and viewsets, and configures JWT authentication. Trigger terms: Django, DRF, Django REST Framework, Django ORM, Django model, serializer, viewset, Python web.

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 writing E2E tests with Playwright, setting up test infrastructure, or debugging flaky browser tests. Invoke to write test scripts, create page objects, configure test fixtures, set up reporters, add CI integration, implement API mocking, or perform visual regression testing. Trigger terms: Playwright, E2E test, end-to-end, browser testing, automation, UI testing, visual testing, Page Object Model, test flakiness.

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 Angular 17+ standalone components, configures advanced routing with lazy loading and guards, implements NgRx state management, applies RxJS patterns, and optimizes bundle performance. Use when building Angular 17+ applications with standalone components or signals, setting up NgRx stores, establishing RxJS reactive patterns, performance tuning, or writing Angular tests for enterprise apps.

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 Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.

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。

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

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 GSD workflow toggles and model profile

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 GSD settings — workflow toggles, advanced knobs, integrations, and model profile

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。

Audit Google Ads measurement, Search, Shopping, Performance Max, Demand Gen, YouTube-linked inventory, keywords and search terms, negative-keyword generation or review, creative assets, bidding, budgets, settings, and policy. Use for Google Ads, AdWords, Search campaigns, search terms reports, broad negatives, Shopping, Performance Max, PMax, Demand Gen, GAQL, Google conversion tracking, or Google campaign optimization.

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。

Scaffolds evaluation suites for the Axiom AI SDK. Generates eval files, scorers, flag schemas, and config from natural-language descriptions. Use when creating evals, writing scorers, setting up flag schemas, or configuring axiom.config.ts.

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。

Writes and reviews structured Lean 4 proofs and designs Lean libraries following Mathlib conventions. Use when proving theorems in Lean, formalizing mathematics or specifications in Lean 4, defining new types or definitions in a Lean library, reviewing Lean proofs for readability and maintainability, refactoring long tactic proofs into lemmas, filling in sorry placeholders in a Lean development, setting up CI or linters for a Lean project, diagnosing slow proofs or maxHeartbeats timeouts, or writing custom tactics, macros, or linters.

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。

Enrolls a project in OSS-Fuzz, Google's free continuous fuzzing service for open source, and drives it locally. Covers project.yaml, Dockerfile and build.sh setup, the helper scripts, reproducing OSS-Fuzz crash reports, and the acceptance criteria. Use when setting up continuous fuzzing for an open-source project, reproducing an OSS-Fuzz bug report, or testing an OSS-Fuzz build before submitting 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。

Creates devcontainers with Claude Code, language-specific tooling (Python/Node/Rust/Go), and persistent volumes. Use when adding devcontainer support to a project, setting up isolated development environments, or configuring sandboxed Claude Code workspaces.

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 mewt or muton campaigns, analyzes surviving mutants, and investigates bugs exposed by testing gaps. Use when setting up mutation testing, reviewing campaign results, identifying equivalent mutants, or finding bugs from surviving mutations.

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。

Practical offensive fuzzing methodology covering target identification, fuzzer selection (AFL++, libFuzzer, Honggfuzz, Boofuzz, syzkaller), harness writing, corpus curation, mutation strategies, coverage measurement, and crash triage. Use when setting up or running fuzz campaigns against any target: file parsers, network protocols, kernel drivers, EDR engines, embedded firmware, or language runtimes.

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 a person in their own Claude Code or Codex session mentions OpenRig, rigs or the rig command, or wants a team of coding agents (Claude Code, Codex) on their project. That includes installing, setting up, starting or updating OpenRig, seeing, getting back to or re-attaching to their OpenRig agents (including bringing a stopped team back), TUI or operator, starting or sharing a team, and joining this session to one. Operating a running rig from inside it (fleet recovery, seat handover, watchdog wakes, in-rig upgrades) is the openrig-skills skill.

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。

Audit or critique a product flow, journey, workflow, funnel, onboarding path, checkout path, settings path, screen, or multi-step product experience by capturing screenshots first, then reporting UX, design, and accessibility findings inline from that evidence. Use Figma only when the user explicitly asks for a board. Use when the user asks to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on a product 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。

Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating 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。

This skill installs mixpanel_headless, pandas, numpy, matplotlib, seaborn, networkx, anytree, scipy (and pyarrow on Python 3.11+), then verifies Mixpanel credentials. It should be invoked when setting up a new environment for Mixpanel data analysis, when dependencies are missing, or when configuring service account or OAuth credentials for the first time.

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。