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概要と使いどころ
Use this skill on every programming task — writing code, fixing bugs, refactoring, explaining code, running tools, or answering technical questions. Apply token-saving and quality-preserving practices throughout. Activate even when the user doesn't explicitly ask for efficiency — this skill is always relevant for software development work. Do not skip this skill for "simple" tasks; the rules apply at every scale.
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。
Run 150+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok
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 REST/GraphQL APIs with OpenAPI 3.1, error handling, pagination, rate limiting, webhooks, and idempotency. Use when user asks to design an API, create endpoints, define REST/GraphQL schema, or generate OpenAPI spec. Do NOT use for database schema design, frontend API integration, or non-HTTP protocols (gRPC, WebSocket, MQTT).
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。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
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 only after explicit Codex `$superloopy:superloopy-backend` or Claude Code `/superloopy:superloopy-backend` invocation, a backend task beginning with leading `loopy` or `루피`, or an active Superloopy loop routes backend work here. Relevant work includes backend application boundaries, APIs, persistence, migrations, transactions, background jobs, caches, events, reliability, security, and runtime database-agent tools. Plain API, server, database, SQL, or agent vocabulary does not activate this workflow.
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。
Sub-skill: Implement ARD (Agentic Resource Discovery). Publish /.well-known/ai-catalog.json so agents can discover MCP servers, A2A agents, skills and API tools (agenticresourcediscovery.org / ai-catalog).
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。
Objectively evaluate a UI/web design against the pols.dev anti-slop design law: detect catalogued slop tells with cited evidence, score 8 weighted axes (color, type, components, layout, motion, execution, signature, cohesion), and emit a Slop Report with a 0–100 Slop Index and grade. Use when the user asks to "evaluate design slop", "slop report", "is this design AI slop", "audit this landing page design", "de-slop review", or wants an objective score of how generic/machine-made a design looks. To fix text (not design), use human-ai or humanizar skills 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。
Encrypt PDFs with AES-128, AES-256, or RC4 options and decrypt authorized PDFs with ComPDF. Use for PDF passwords, permission controls, encryption, decryption, and document-access protection.
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 using UV (Python package manager), needs to set up Python virtual environments, install/manage Python CLI tools, migrate from pip/pipx to UV, Python version management, or troubleshoot UV-related issues.
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 before installing dependencies, packages, or tools required for building 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。
Implement CLI tools in Python, JS/TS, Bash, or Go following clig.dev standards. Use when writing or reviewing CLI 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。
Used to invoke the PXMeter tool for rigorous quality assessment of biomolecular structure prediction models (e.g., proteins, nucleic acids, small molecules). This skill covers single-sample CIF evaluations and large-scale dataset benchmarks, providing rich result parsing support. Trigger this skill when the user asks to "run PXMeter", "compare PDB/CIF evaluation results of different tools", "check benchmark scores", or asks "how accurate is the structure generated by the 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。
Welcome and set up a new Claude for Financial Advisors user. Gets to know the advisor's RIA, learns their day-to-day pain points and tech stack, walks them through connecting their tools, explains the plugin and how skills work, runs a live pre-meeting demo, shows them how to customize skills, and closes with a personalized recommendation for which skill to try next. Use when the advisor is getting started or says any of: "onboarding," "/onboarding," "get started," "help me get started," "set up claude for financial advisors," "claude for financial advisors setup," "what can this plugin do," "I'm new to this," "show me around," or is in their first session with the plugin.
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。
Start a new prospect's intake, with or without their files in hand — a request naming a new prospect and attaching no documents is a normal starting point, because Step 1 searches the connected tools or asks the advisor to upload. Cleans and normalizes their account files (statements, exports, screenshots) into a consolidated view, then hands back a plain-English prospect summary, a detailed analyst handoff the full proposal gets built from, and a friendly "what to expect" memo, offered as a Gmail draft on approval. Does not produce the proposal, recommend an allocation, or quote fees. Triggers on "prospect intake", "/prospect-intake", "new prospect intake", "clean up [prospect]'s statements", "process [prospect]'s files", "what to expect note for [prospect]" — and on how an advisor raises a new prospect before any file exists: "I met with a potential client/customer", "[name] is interested in becoming a client", "I have a new prospect", "how should I proceed with [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。
Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.
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。
Find a direct path to structured data through ready-made workflows, data APIs, and indexes. Follow the search skill to discover and inspect tools, then the scrape skill to execute them.
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。
Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this 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。
End-to-end production workflow guidance for Blender — the order to assemble scenes (block-out → camera → light → forms → materials → detail → render → composite → export), critique protocols, time budgets, and recovery patterns. Use whenever the user asks to "make a complete scene / hero shot / production-quality render", "what's the right order to do this", "set up a full pipeline", or has a multi-step request crossing modeling + lighting + materials + rendering. Make sure to use this skill for any request that spans multiple phases of 3D work, even if the user does not say "workflow" — also covers "make a final image of X", "produce a hero render", "professional-looking result".
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 transactional email systems and send infrastructure. Use when implementing resets, receipts, deliverability controls, templates, or inbound email handling.
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。
Sizes queues and worker pools. Use when estimating Kingman G/G/1 mean waiting time, Erlang or Little's Law sizing, p99 under load, retry storms, worker counts.
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 tool and slash-command runtimes for coding-agent CLIs: registries, deferred loading, tool search, dispatch, forks. Use when modeling tool pools. Not approvals or plugins.
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 lakehouse platforms across Iceberg, Delta, Hudi, and Paimon. Use when choosing catalogs, CDC paths, query engines, governance, or cost controls.
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 operating models for AI in product teams. Use when planning platform ownership, provider strategy, data boundaries, evals, or sensitive-data controls.
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。
Chooses DPO, GRPO/RLVR, reward models, and over-optimization controls. Use when adapting a pretrained or SFT model with preference or verifiable reward signals.
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。