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概要と使いどころ
Reduce visual intensity, saturation, and noise while preserving hierarchy and character. Use when the structure is basically sound but the design feels too loud, bold, garish, aggressive, or overstimulating—not when the real problem is structural clutter, too many features, or not enough personality.
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 features, fix bugs, and keep the codebase healthy and maintainable. Works from a defined PBI, issue, or task description.
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 this skill when working with Twilio communication APIs for SMS/MMS messaging, voice calls, phone number management, TwiML, webhook integration, two-way SMS conversations, bulk sending, or production deployment of telephony features. Includes official Twilio patterns, production code examples from Twilio-Aldea (provider-agnostic webhooks, signature validation, TwiML responses), and comprehensive TypeScript 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。
Use this skill when working with Telnyx communication APIs for SMS/MMS messaging, voice calls, phone number management, messaging profiles, webhook integration, two-way SMS conversations, bulk sending, message scheduling, or production deployment of telephony features. v2.0 includes official Telnyx documentation, production patterns from Twilio-Aldea (Ed25519 signature validation, provider-agnostic webhooks, idempotency), and TypeScript code 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。
Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.
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 a polished release video for a tbh-meter release in the Claude-Code motion style (recreated animated UI + real game sprites + synth soundtrack), to post with a launch. Primary input is a GitHub release LINK — the skill turns it into a video named after that release, covering ONLY the features that shipped in that release, version stamped on the intro. Use when asked to make/update a release video, showcase clip, feature trailer, or promo video for a meter release, or "make the video for <release link>".
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。
Probe the runtime state of a named feature flag in a CLI binary. Covers the four-pronged evidence protocol (binary strings, live invocation, on-disk state, platform cache), the four-state classification (LIVE / DARK / INDETERMINATE / UNKNOWN), gate-vs-event disambiguation, conjunction-gate handling, and skill-substitution scenarios where a flag appears DARK but the capability is delivered by other means. Use when verifying whether a documented or inferred capability has rolled out, when auditing dark-launched features, or when a prior probe's conclusions need refreshing against a new binary version.
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。
Maintain a changelog following Keep a Changelog format. Covers entry categorization (Added, Changed, Deprecated, Removed, Fixed, Security), version section management, and unreleased tracking. Use when starting a new project that needs a changelog, adding entries after completing features or fixes, preparing a release by promoting Unreleased entries to a versioned section, or converting a free-form changelog to Keep a Changelog format.
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 comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.
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。
Field identification of fungi using morphological features, spore prints, habitat analysis, and seasonal context with a safety-first approach. Covers cap, gill, stem, and spore characteristics, look-alike differentiation, toxicity risk assessment, and the critical rule of absolute certainty before consumption. Use when encountering an unknown fungus, foraging for edible mushrooms and needing to confirm species before consumption, assessing whether fungi in a garden or property are harmful, or differentiating an edible species from a dangerous look-alike.
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 themed Mermaid flowchart diagrams from putior workflow data. Covers theme selection (9 themes including 4 colorblind-safe), output modes (console, file, clipboard, raw), interactive features (clickable nodes, source info), and embedding in README, Quarto, and R Markdown. Use after annotating source files and ready to produce a visual diagram, when regenerating a diagram after workflow changes, or when switching themes or output formats for different audiences.
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。
Read-only audit of a GitHub repository's security posture. Gathers ref protection (rulesets AND classic branch protection), Actions token permissions, code and supply-chain features (Dependabot, secret scanning, push protection, CodeQL), and repo hygiene toggles via `gh api`, then classifies findings against essential / recommended / advanced tiers into a PASS/GAP report. Makes NO changes. Use when reviewing a repo before open-sourcing or a release, auditing a public user-owned repo whose CI auto-commits to the default branch, verifying a hardening change actually took effect, or producing a baseline security posture report for a repository.
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。
Next.js project structure, App Router conventions, file-based routing primitives, layouts, error boundaries, metadata, image and font optimization, configuration. Apply when implementing or modifying Next.js features. Use this skill to: - Pick the correct file convention (page/layout/loading/error/not-found/route/template/default). - Structure a feature within the App Router tree. - Set up metadata for SEO and OpenGraph. - Configure next.config.js for headers, redirects, image domains. - Use built-in fonts and images correctly. Do NOT use this skill for: - Server vs Client component boundaries (see server-component-patterns). - Data fetching patterns (see nextjs-data-fetching). - Complex routing (parallel/intercepting routes — see nextjs-routing). - Testing (see nextjs-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。
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
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。
Orchestrate complex development with AUTOMATIC parallel subagent execution, continuous dispatch scheduling, dependency analysis, file conflict detection, and IOSM quality gates. Analyzes task dependencies, builds critical path, launches parallel background workers with lock management, monitors progress, auto-spawns from discoveries. Use for multi-file features, parallel implementation streams, automated task decomposition, brownfield refactoring, or when user mentions "parallel agents", "orchestrate", "swarm", "continuous dispatch", "automatic scheduling", "PRD", "quality gates", "decompose work", "Mixed/brownfield".
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 human says "overhaul", "rebuild this subsystem", or "rewrite it from scratch". Not for thin-slice features: use incremental-implementation. Not for root-cause repair: use strike-the-root.
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 bug, failure, flake, regression, review finding, or ticket needs the core fixed so it cannot recur. Not for greenfield features: use tdd. Not for style-only review or typo-class one-liners.
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 wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.
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。
TanStack Table best practices for building headless, type-safe data tables in React with sorting, filtering, pagination, row selection, and column management. Use when building data grids, implementing client-side or server-side table features, defining column structures, managing table state, or optimizing table rendering performance.
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 the technical architecture for a NowStack Mobile app from .agents/plan/PRD.md, mapping the product onto the FIXED boilerplate stack (Convex schema + functions, Expo Router screens, Better Auth, Apple IAP/Stripe, R2), then save .agents/plan/ARCHITECTURE.md. Use for "design the architecture", "ns architecture", "plan the schema and screens", or as the second planning step after ns-prd and before ns-tasks. Not a blank-slate stack design — the stack is set; this maps features onto 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。
Lightweight build: explore → plan → approve → implement in a single Opus context, no sub-agent fan-out. Verifies build + tests, optionally commits, and offers a separate /code-review pass at the end. Use by default for everyday features; reach for /build only when the work needs parallel fan-out or exceeds one 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。
Lightweight build: explore → plan → approve → implement in a single Opus context, no sub-agent fan-out. Verifies build + tests, optionally commits, and offers a separate /code-review pass at the end. Use by default for everyday features; reach for /claude-setup:build only when the work needs parallel fan-out or exceeds one 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。
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when the user wants to stress-test a plan against the project's language and documented decisions. Run BEFORE /claude-setup:build on non-trivial features in repos that have (or should have) a CONTEXT.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。