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Symfony UX frontend stack -- decision tree and orchestrator for choosing between Stimulus, Turbo, TwigComponent, LiveComponent, UX Icons, and UX Map. Use when the user is unsure which tool fits, wants to combine multiple UX packages, or asks a general frontend architecture question in Symfony. Also trigger when the user asks "which UX package should I use", "how to make this interactive", "should I use Stimulus or LiveComponent", "how to structure my Symfony frontend", "what is the difference between Turbo and LiveComponent", "should this be a Frame or a LiveComponent", "how do these UX packages work together", "what is the Symfony way to do frontend". Do NOT trigger when the user clearly names a specific tool (stimulus, turbo, twig-component, live-component, ux-icons, ux-map) -- defer to the specialized skill 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。

Refactor changed code to reduce duplication, complexity, and wasted work. Use for diffs, commit ranges, PRs, paths, staged changes, or working-tree changes.

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 code changes for bugs, regressions, convention violations, and high-value cleanup opportunities. Use for diffs, commit ranges, hosted PR/MR URLs, branches, paths, staged changes, or working-tree changes.

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。

Compile the TMDb package AND all test targets (without running the tests) to catch test-code compile errors. Use after changing tests or shared code to check everything still builds — delegates to the tooling-runner agent (Haiku) and returns a concise pass/fail + errors-as-file:line summary. Differs from /build, which compiles only the library; to run the tests, use /test.

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 the working-tree changes (vs main) for correctness, concurrency, architecture, testing, and API/doc issues — following .github/CODE_REVIEW.md — and return a severity-graded report. Scales the machinery to the diff size: a single code-reviewer agent for a small change, or a fan-out-and-verify Workflow for a large/multi-unit one. Produces findings; it does not apply fixes (the caller does).

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。

Survey a whole React codebase as a senior React engineer, using React Doctor's scan as evidence, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the React code", "audit this codebase", "make this app faster / more robust", or wants a roadmap of fixes rather than a review of a single diff. For a regression check or a fix-it-now pass, use the `react-doctor` skill 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。

Apply when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.

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。

Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'

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。

Mask, redact, and anonymize sensitive data (PII, PCI, PHI) in databases, logs, and APIs. Use when protecting PII in dev/staging environments, redacting sensitive data from logs, anonymizing data for analytics, or applying k-anonymity and differential privacy for GDPR-compliant data sharing.

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 CORS for web APIs. Use when a user asks to fix CORS errors, allow cross-origin requests, configure CORS headers, handle preflight requests, or secure API access from different domains.

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 the public interface of a module before any implementation exists: writes the caller scenarios first, produces three candidates with genuinely different shapes, type-checks each one against the scenarios, scores them on measurable criteria and records the decision. Use when someone says "design the API for this module", "what should this signature look like", "compare interface options", "this function is awkward to call", "show me alternatives before I build it", or is about to add a module, library entry point, endpoint or command that other code will depend on.

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。

Makes working code easier to read and change without altering what it does, and proves the behaviour stayed the same. Use when someone says "simplify this", "clean up this function", "this file is a mess", "reduce the complexity", "refactor for readability", "tidy my diff before the PR", or when a linter reports high cyclomatic complexity or deep nesting. Covers pinning current behaviour with characterization tests, measuring complexity, choosing a named refactoring, applying it in small verified steps, and reporting suspected bugs found on the way instead of fixing them silently.

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。

Finds the cause of a hard bug by running several read-only subagents in parallel, each attacking it from a different angle (change history, code path, data and state, environment, reproduction), then checking their evidence, ranking the hypotheses and naming the one experiment that would confirm the leader. It diagnoses and does not fix. Use when someone says "hunt this bug with multiple agents", "swarm on this regression", "investigate in parallel", "find the root cause", or when a failure is intermittent, crosses components, or has resisted a first look.

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 Agent2Agent (A2A) servers and clients, the open protocol (originally from Google, now under the Linux Foundation) that lets AI agents from different frameworks call each other. Use when the user wants to create an A2A-compliant agent, build an Agent Card, implement task management, connect agents across frameworks, set up agent discovery, handle streaming responses, implement push notifications, or orchestrate multi-agent workflows. Trigger words: a2a, agent to agent, agent2agent, a2a protocol, a2a server, a2a client, agent card, agent interoperability, agent collaboration, multi-agent, agent discovery, a2a sdk, a2a task.

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。

Expert in meme creation and virality - understanding why certain ideas spread while others die. Covers meme formats, timing, adaptation, meta-commentary, and platform-specific optimization. Knows the difference between forcing a meme and engineering conditions for organic spread. Use when "meme, viral content, make it memeable, shareable, internet culture, template, " 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。

I am a veteran print production specialist with 15+ years in tabletop game manufacturing. I've shepherded hundreds of games from digital files to retail-ready products, working with manufacturers from The Game Crafter to Panda GM to Cartamundi. My expertise spans the entire production pipeline: color management, dieline creation, paper selection, finishing techniques, component nesting, insert design, and the critical art of the unboxing experience. I've learned the hard way about RGB-to-CMYK disasters, registration drift, humidity warp, and minimum font sizes that become illegible smudges. I know the difference between 280gsm blue-core and 320gsm black-core card stock by feel. I can tell you why your rich black needs to be C60-M40-Y40-K100, not just K100. I understand why your box needs 15mm clearance on each dimension, and why that gorgeous neon green in your design will become a muddy olive in print. Use when "print my game, packaging design, prepare files for print, dieline, print specifications, board game manufacturing, card game printing, box design, bleed and safe area, CMYK conversion, paper stock, card finish, foil stamping, embossing, unboxing experience, thermoform insert, punchboard design, component manufacturing, Panda GM, Game Crafter, Cartamundi, print-production, packaging, manufacturing, board-games, card-games, cmyk, dieline, die-cutting, offset-printing, tabletop" 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。

Git is deceptively simple to learn and incredibly hard to master. The difference between a team that ships and a team that fights merge conflicts all day comes down to workflow discipline. Your git history is documentation - make it tell a story future you can understand. This skill covers branching strategies (trunk-based, gitflow, GitHub flow), commit hygiene, merge vs rebase, conflict resolution, and the commands you actually need. Key insight: most git disasters come from not understanding what you're about to do. 2025 lesson: Trunk-based development with short-lived branches has won. Long-lived feature branches are a code smell. If a branch lives more than a few days, something is wrong with your architecture or process. Use when "git workflow, branching strategy, git merge, git rebase, merge conflict, git commit, trunk-based, gitflow, git history, git revert, git reset, git, version-control, workflow, branching, commits, merge, rebase, collaboration" 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。

Expert in feature prioritization - the art and science of deciding what to build and in what order. Covers prioritization frameworks, roadmap planning, stakeholder management, and the trade-offs between different approaches. Knows that prioritization is about saying no, and how to make those decisions defensible. Use when "prioritization, roadmap, what to build, feature request, backlog, scope, trade-off, " 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。

World-class trade execution - minimize market impact, optimize order routing, reduce slippage. The difference between backtest and live is usually execution. Use when "execution, slippage, market impact, order routing, TWAP, VWAP, iceberg, fill rate, best execution, " 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。

Expert in competitive intelligence - systematic gathering, analysis, and application of information about competitors. Covers competitive research, win/loss analysis, competitive positioning, and building competitive advantage. Knows when to compete and when to differentiate. Use when "competitive intelligence, competitor analysis, win loss, battlecard, competitive positioning, market landscape, competitor, " 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。

Expert in community-led growth (CLG) - leveraging user communities to drive acquisition, retention, and expansion. Covers building developer communities, user groups, ambassador programs, and turning customers into advocates. Knows the difference between community as a feature vs community as a growth engine, and how to measure community ROI. Use when "community-led, CLG, ambassador program, champion program, user community, developer community, word of mouth, user group, " 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。

Expert patterns for AI-powered image editing including inpainting, outpainting, ControlNet, image-to-image, and API integration with Replicate, Stability AI, and FalUse when "ai image editing, inpainting, outpainting, controlnet, image to image, remove object from image, extend image, flux inpaint, sdxl editing, image-editing, inpainting, outpainting, controlnet, stable-diffusion, flux, replicate, stability-ai, comfyui" 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。

World-class character and art style consistency for AI-generated images and videos - ensures visual coherence across series, maintains character identity, and provides rigorous QA before deliveryUse when "character consistency, art style, same character, consistent character, visual continuity, series, turnaround sheet, character sheet, reference image, character bible, style guide, anime character, consistent look, face consistency, outfit consistency, lora training, ip-adapter, flux kontext, visual qa, art quality, generation review, style drift, character drift, character-consistency, art-style, visual-qa, ai-art, image-generation, video-generation, anime, illustration, lora, ip-adapter, flux, midjourney, stable-diffusion" 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。

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when "AI wrapper, GPT product, AI tool, wrap AI, AI SaaS, Claude API product, " 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。