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概要と使いどころ
Maintenance for agent-flow context. Runs /doctor, /sync-context, /audit-risk, /repair-docs, /garden, /debt and /audit-lean; re-verifies context claims against the code before refreshing timestamps; turns repeated agent mistakes into mechanical checks. Use on a schedule, after merges flagged [CONTEXT_STALE], or when asked whether AGENTS.md is stale or the risk baseline needs review.
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。
Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 on SecNumCloud infrastructure). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack 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。
Opinionated constraints for building better web interfaces with coding agents. Use this skill whenever the user asks you to build, review, or fix a UI — landing pages, dashboards, components, or any frontend code — and whenever you write Tailwind CSS, motion/react animations, accessible component primitives (Base UI, React Aria, Radix), or review agent-generated interface code for quality, accessibility, and performance. Also trigger for "make the UI better", "review my interface", "clean up this component", or any frontend work in React, Next.js, or plain HTML/CSS.
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。
Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.
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。
Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.
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。
Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.
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 and improves task-specific decision artifacts from application requirements and labeled records. Use for data assembly, primitive and base-model selection, fitting, export/reload, inference policy, or bounded data/model/program improvement. Includes rules and no-training outcomes; does not require a rung ladder or training a general Jev.
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。
Monthly delta tracking and progress reporting for GEO clients. Compares two GEO audits (baseline vs. current), calculates score improvements across all categories, tracks action item completion, and generates a "here's your progress" client report. Use when user says "compare", "delta", "monthly report", "progress", "confronta", "progressi", "report mensile", or when running a monthly client check-in.
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, extend, validate, and publish a ChatGPT Sites application whose durable database and backend functions run on Convex. Use when a user invokes $codex-sites-convex, selects this skill from the skill picker, asks for a ChatGPT Site backed by Convex, or asks to add Convex data, realtime queries, mutations, authentication, files, schedules, or actions to an existing ChatGPT Sites project.
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。
Continuously detect recurring UI/UX concepts during frontend discussions, track conversation-wide recurrence, and register repeated concepts as structured knowledge assets in a Notion database via MCP. Activates when UI/UX principles, heuristics, laws, or design patterns recur during frontend work, or on manual @ux commands (@ux save, @ux skip, @ux 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。
Generate architecture and design documents for implemented code changes with risk-based depth selection. Automatically evaluates risk signals, layer spread, and change magnitude to choose documentation level (A/B/C). Use after completing a feature, before merging a PR that touches multiple layers or sensitive areas (auth, infra, migrations, payments), or when an implemented change needs a design record or ADR.
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。
Diagnose and fix CJK (Korean, Japanese, Chinese) text-wrapping issues in web UIs. Systematically traces mid-syllable breaks, orphaned glyphs, and awkward line splits through a layered fix strategy covering global CSS cascade, component-library overrides, headline balancing, and translation-level phrase binding. Produces an evidence-based diagnosis and verification report before any code change is applied.
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。
Get started with your Open SaaS project — fetches docs, checks Wasp installation, and helps you start your database and app.
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。
When starting parallel work in isolation from the current branch — spawn a git worktree with ignore-safety checks and a clean test baseline — even when the user says 'try this on the side'.
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 multi-pass codebase audit — orchestrates project-analysis-core plus the framework-specific project-analysis-*; explicit request only. Single-pass scan → project-analyzer.
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 hardening a Linux host you operate — SSH posture, a default-deny firewall baseline, and unattended security upgrades, each verified on the box rather than assumed from a config file.
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。
Validate whether a feature request fits the existing codebase — check for duplicates, contradictions, scope creep, and architectural misfit
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 surfacing tech debt as trackable items — interest-vs-principal framing, prioritisation by carrying cost, repayment plan — even if the user just says 'this codebase is a mess'.
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 constructing base / upside / downside scenarios — three-statement modeling, sensitivity analysis, optionality reasoning. Triggers on 'model the scenarios', 'what if growth halves'.
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 packaging a codebase to a single AI-friendly file for LLM analysis — local or remote, XML/Markdown/JSON, token counting, gitignore filtering, peer-side `repomix` 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。
Use when a bug has multiple plausible causes across layers — competing hypotheses, validation loops, evidence-based conclusions — even when the user just says 'why is this happening?'.
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 creating, rewriting, or significantly improving a README based on the actual repository structure, commands, and intended audience.
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 debugging PHP with Xdebug — breakpoints, step-through, dual-container setup, IDE configuration, header-based routing — even when the user just says 'why does this blow up on request X'.
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 creating a Laravel/PHP DTO with the SimpleDto base class and attribute mapping. For DTOs in other stacks, use the stack-native skill (TypeScript, Python, Rust, Go).
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。