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概要と使いどころ
Comprehensive guide to using LLMs throughout the game development lifecycle - from design to implementation to testingUse when "ai game development, llm game dev, claude game, gpt game, ai coding games, vibe coding game, prompt game development, llm, ai, game-development, workflow, prompting, coding, prototyping, claude, gpt, cursor" mentioned.
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Patterns for laboratory automation including liquid handling robotics, LIMS integration, protocol development, quality control, and high-throughput workflows. Covers both open-source (Opentrons) and commercial platforms. Use when ", " 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。
Master the AI tools that accelerate design work. From UI generation to image editing, create professional designs faster. Use when "design, UI, graphics, image editing, prototyping, design, ui, graphics, creative, visual" 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。
Infrastructure and practices for reproducible computational research. Covers environment management, data versioning, code documentation, and sharing protocols that enable others to reproduce your results. Use when ", " 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。
Use when building DeFi protocols, implementing AMMs, yield farming strategies, or integrating with Ethereum/L2s - covers smart contract patterns, liquidity pools, and security considerationsUse when ", " 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。
Calculate, track, and report greenhouse gas emissions following GHG Protocol standards, including Scope 1, 2, and 3 emissions and science-based targets. Use when "carbon accounting, GHG emissions, scope 1, scope 2, scope 3, carbon footprint, emission factor, science-based target, SBTi, net zero, " 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。
Install a per-turn canary signal (e.g. starting every reply with the user's name and a turn counter) so silent context degradation becomes visible the moment it happens, and run a recovery protocol when the canary trips. Use when the user mentions a "canary", "context canary", or "canary check", asks to detect context rot / compaction / drift, says "you stopped using my name" or "did you lose context", asks "how degraded is your context", or wants an early-warning system for long agent sessions.
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 whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".
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。
运行代码生成并检查变更。在修改 API 规范（api-specs/ YAML）或 sandbox OpenAPI/protobuf 后使用。
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 a portable, embedding-free knowledgebase from a set of files and delivers it as a self-contained `.skill` bundle (BM25 index + bundled searcher + query protocol). Use when a user wants to turn uploaded files, a folder, or a corpus into a searchable knowledgebase they can hand to any agent — phrased as "make a knowledgebase", "build a KB skill", "package these docs for retrieval", "create a searchable bundle", or references to a `.skill` KB. The output runs anywhere with Node or Python — no model, no install, no network. Distinct from `bm25` (ephemeral in-session search) and `building-github-index` (markdown project-knowledge index).
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。
Technical SEO audit across 9 categories: crawlability, indexability, security, URL structure, mobile, Core Web Vitals, structured data, JavaScript rendering, and IndexNow protocol. Use when user says "technical SEO", "crawl issues", "robots.txt", "Core Web Vitals", "site speed", or "security headers".
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。
Plan and facilitate design sprints from challenge framing through prototype 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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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 channels the strategic thinking of Walt Disney, co-founder of The Walt Disney Company and animation pioneer. Use this skill whenever you are advising users on creative control, intellectual property, brand building, storytelling, or balancing quality with profit. It is highly effective for situations involving product diversification, media synergy, adapting classic works for mainstream audiences, or designing ambitious, future-proof prototypes (like urban planning or platform ecosystems). Trigger this skill when users face pressure to repeat past successes (sequels), deal with middlemen, or need to elevate a team's creative output through constructive feedback.
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 applies the first-principles engineering and capital allocation frameworks of Elon Musk (CEO of Tesla and SpaceX, founder of X.com / PayPal). Reach for this skill whenever the user is facing complex manufacturing bottlenecks, evaluating existential risks, debating free speech vs. censorship, dealing with bureaucratic overregulation, or trying to accelerate ambitious technical timelines. It is highly effective for situations involving hardware production, AI symbiosis, radical innovation, and aggressive execution. Use this skill proactively when discussing multi-planetary expansion, government efficiency, or scaling prototypes to mass production, even if the user doesn't explicitly name him.
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 channels the strategic thinking of Mark Zuckerberg, co-founder and CEO of Meta. Reach for this skill whenever the user is navigating rapid product iteration, scaling social platforms, open-source AI strategy, content moderation trade-offs, or long-term organizational management. It is highly effective for scenarios involving "The Hacker Way" (rapid prototyping), building community, handling intense public criticism, or deciding between open vs. closed ecosystems. Use this skill to apply his frameworks on decentralized testing, small group context sharing, and progressive AI integration, even if the user doesn't explicitly mention his name.
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。
Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.
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。
Suede AI full-stack surface builder that runs design, copy, and visual QA as one pass: landing pages, brand surfaces, product UI, dashboards, campaigns, launch pages, and reference-to-target restyles (suedify). Use when a build needs layout and words together, when a redesign or launch surface has to ship end to end, when the request is 'make this site look like that one', or when design, copy, asset, and QA lanes have to move at once. NOT FOR: a design-token, dark-mode, or single-component decision (use suede-design); copy with no layout work (use johnny-suede-write, or suede-copy for one standalone conversion surface); conversion and funnel architecture (use suede-site-alchemy); multi-agent orchestration protocols, WIP protection, and rollback trees (use suede-agent-teams).
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。
Suede AI MCP release QA, scoped to this pack's own server (mcp/suede-skills-mcp.mjs) and its catalog, install, and docs surface. Runs the full JSON-RPC lifecycle against a live server (initialize, notifications/initialized, ping, tools/list, tools/call, resources/list, resources/read, prompts/list, prompts/get), plus protocol negotiation, closed input and output schemas, tool annotations, structuredContent with text fallbacks, malformed-input probes, clean stdio, catalog-to-folder agreement, and install-path language. Use when the MCP server, mcp/catalog.json, a tool, resource, or prompt definition, or the MCP install docs change, or before publishing an MCP release. A check that did not run against the live server is a FAIL, not a skip. NOT FOR: a generic third-party MCP server, which this skill's hardcoded surface does not describe; fixing or testing the public install path itself (use suede-launch-packaging).
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 modeling patterns for Axone contracts. Use when introducing domain concepts, encoding invariants, or deciding boundaries between domain, handlers, services, gateways, queries, and state.
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。
Repository quality gates for Rust and generated artifacts. Use when validating changes locally or before committing Rust, schema, or documentation updates.
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。
Axone deployment workflows with cargo-make, cw-orch, and Abstract. Use when publishing modules, installing them on accounts, running local chain tasks, or inspecting deployments.
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。
Guide for regenerating Axone contract schemas and rendered Markdown docs. Use when contract APIs or metadata change, when checking generated-doc drift, or when preparing documentation commits.
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。
Guide for writing Rust doc comments that produce accurate generated contract documentation. Use when editing Instantiate/Execute/Query/Response types or any public schema-facing API.
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。