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概要と使いどころ
Resolve unresolved PR review comments across an entire Graphite (gt) stack of many PRs, bottom-up, in one working directory. Use when asked to "go through this stack and resolve comments", "clean up review comments across the whole stack", or given a list/range of PR numbers that form (or partially form) a gt stack. Distinct from the single-PR resolve-pr-comments skill, which this one uses as its per-branch inner loop.
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。
Reliable fallback technique for writing large file contents when write_file and shell_agent both fail with 'unknown error' due to payload size limits — uses run_shell with a Python heredoc to bypass tool constraints, with an explicit mkdir -p step to ensure the target directory exists before writing.
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。
Create resilient data fetching services with circuit breaker pattern, supporting both proxied and direct API calls. Services handle fetch, cache, retry, and expose typed data to panel components.
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。
Reliable fallback technique for writing large file contents when write_file and shell_agent both fail with 'unknown error' due to payload size limits — uses run_shell with a Python heredoc to bypass tool constraints.
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。
Multi-layer workflow for creating complete panel features by analyzing existing patterns and generating coordinated server routes, services, UI components, registration, styling files, AND explicitly wiring new handlers into the server's dispatch table (supports both Express middleware and manual http.createServer switch/case patterns).
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 a threat model for a target codebase. Three modes: "interview" walks an application owner through the four-question framework and produces a threat model from their answers; "bootstrap" derives a threat model from the code plus past vulnerabilities (CVEs, git history, pentest reports) when no owner is available; "bootstrap-then-interview" chains the two when both owner and codebase are present. All write THREAT_MODEL.md in a shared schema. Use when asked to "threat model", "build a threat model", "map the attack surface", or "what should we be worried about in this codebase".
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。
Sets up and runs Atheris, the coverage-guided Python fuzzer built on libFuzzer. Covers TestOneInput harnesses, FuzzedDataProvider, instrumenting both pure Python and native C extensions, and running under AddressSanitizer. Use when fuzzing a Python package, hunting memory corruption in a Python C extension, or choosing between Atheris and Hypothesis for a Python target.
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。
Triages a repository's open GitHub issues and pull requests via the gh CLI. Optionally reviews and merges ready PRs — incrementally merging passing automated/bot PRs and maintainer-approved ones, and spawning review subagents for never-reviewed ones — then closes already-resolved issues with comments citing the resolving PR or commit, cross-links issues with their pending fix PRs, and assigns local-only priority and change-size estimates for everything outstanding. Use when triaging, grooming, or reviewing a repository's open issues and PRs.
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。
Burn timed subtitles onto a finished video or configure Whisper-timed caption burning during faceless-video assembly. Takes video/audio generation jobs or a local finished video, optionally with authored narration text, and returns a captioned video or the exact backend subtitle configuration. Timings always come from Whisper on the video's own audio — never estimated. THREE looks: `paper` (torn cream paper label, handwritten ink), `bold` (UGC ALL-CAPS, white with black stroke, platform safe zones), `clean` (slim white CAPS, no plate, tiny at the bottom — no Pillow needed). Use when: adding/burning subtitles or captions to an existing video, restyling captions, or when a production workflow needs its finished cut subtitled. NOT for: generating the video, translating speech, or live/soft subtitle tracks (this burns them into the picture).
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 or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, create individual components with proper variant sets and variable bindings, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. Also use when the user asks to create or generate any component in Figma — even a single one — since components require proper variable foundations, variant states, and design token bindings to be production-quality. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.
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。
Choose, lay out, critique, and explain data visualizations. Use when the user asks what visualization fits a dataset or goal, how a chart, dashboard, operational workspace, UML-like diagram, or software architecture diagram should be composed or interacted with, asks for visual page design, a layout mockup, generated large-screen and mobile concept images, or to be shown what a visualization could look like, when domain-native contextual surfaces or graphical backgrounds may help, when scrollytelling or parallax might be appropriate, wants a critique of an existing visualization, or needs guidance grounded in trusted visualization theory and practice. For advanced visual design or page-layout prompts where composition affects understanding, Codex must generate and show both large-screen and mobile portrait image concepts before implementation or text-only design handoff, plus mobile landscape when needed.
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 or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, create individual components with proper variant sets and variable bindings, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. Also use when the user asks to create or generate any component in Figma — even a single one — since components require proper variable foundations, variant states, and design token bindings to be production-quality. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.
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。
Create a layered CSS progressive blur (top or bottom) using multiple backdrop-filter masks for depth and softness. Use when asked for “progressive blur”, “gradient blur overlay”, or stepped blur masks that fade from an edge of the viewport.
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。
Turn a visual or interaction technique you already built into a reusable web-design skill, by isolating the one mechanism that makes it work while reproducing its approved reference exactly around that focus, and packaging it with a demo that proves both the mechanism and the visual fidelity. Covers finding the mechanism, naming the technique plainly, disclosing the verified runtime and renderer, auditing reference layers, carrying real numbers instead of adjectives, preserving owned staging, keeping expensive gotchas, declaring the boundary against neighbouring skills, and browser-verifying before claiming it works. Use when a page, canvas scene, shader, scroll effect, layout system, or hover interaction turned out well and should become a skill rather than staying in one 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。
Complete guide to using @openserv-labs/client for managing agents, workflows, triggers, and tasks on the OpenServ Platform. Covers provisioning, authentication, x402 payments, ERC-8004 on-chain identity, and the full Platform API. IMPORTANT - Always read the companion skill openserv-agent-sdk alongside this skill, as both packages are required to build any agent. Read reference.md for the full API reference.
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 and deploy autonomous AI agents using the OpenServ SDK (@openserv-labs/sdk). IMPORTANT - Always read the companion skill openserv-client alongside this skill, as both packages are required to build and run agents. openserv-client covers the full Platform API for multi-agent workflows and ERC-8004 on-chain identity. Read reference.md for the full API reference.
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 a production 1:1 stablecoin swap with LI.FI Intents — an interface or backend where the amount the user sends equals the amount they receive (no visible gas or solver fees), same-chain or cross-chain, filled by LI.FI's solver network and verifiable on-chain. USE THIS SKILL WHEN USER WANTS TO: - Build a 1:1 stablecoin swap interface ("send 100 USDC, receive 100 USDC"), same- or cross-chain - Add stablecoin transfers with a guaranteed output amount to an app, backend, or bot - Build enterprise/fintech-grade transfers with no visible gas or solver fees for the end user - Implement the LI.FI Intents quote -> approve -> escrow open -> status loop for stablecoins - Move USDC / USDT 1:1 across chains under an onboarded LI.FI integrator - Understand how the LI.FI integrator ID / Partner Portal enables 1:1 quoting - Use the @lifi/intent TypeScript SDK to quote, build, open, and track an intent order This skill is self-contained and covers the integration end to end via the `@lifi/intent` SDK (the primary path), with the raw `order.li.fi` REST endpoints as a language-agnostic option. For the wider LI.FI product surface (classic swaps/bridges, Composer, Earn), use the `lifi` skill.
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。
0G Compute Network guide for decentralized AI inference, fine-tuning, and GPU services. Covers chatbots, image generation, speech-to-text, SDK integration (0g-serving-broker), processResponse API, broker.inference methods, CLI commands (0g-compute-cli), and account management. Use this skill for any 0G compute, 0G AI, or decentralized GPU question.
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。
LI.FI REST API for cross-chain and same-chain token swaps, bridging, DeFi deposits (Composer), yield discovery (Earn), and intent-based execution (Intents). USE THIS SKILL WHEN USER WANTS TO: - Swap tokens between different blockchains (e.g., "swap USDC on Ethereum to ETH on Arbitrum") - Bridge tokens to another chain (e.g., "move my ETH from mainnet to Optimism") - Swap tokens on the same chain with best rates (e.g., "swap ETH to USDC on Polygon") - Find the best route or quote for a token swap across chains - Deposit into DeFi vaults/lending/staking in one click, including cross-chain (Composer: Aave, Morpho, Pendle, EtherFi, Yearn, etc.) - Discover yield opportunities, vault APY/TVL data, or track DeFi positions (Earn) - Execute gasless or intent-based transfers via a solver network (LI.FI Intents) - Move stablecoins cheaply with optimized defaults (stablecoin preset) - Build multi-chain payment flows (accept any token, settle in specific token) - Check supported chains, tokens, bridges, or gas prices - Track status of a cross-chain transaction, recover from failed/partial transfers - Query transfer history/analytics or withdraw collected integrator fees - Build backends, bots, or AI agents (any language) that need cross-chain functionality
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。
Manually arbitrate NLA escrow fulfillments as an alternative to the automated oracle. Use when the user wants to review pending arbitration requests, evaluate demands against fulfillments, and submit on-chain decisions. Supports both interactive and LLM-auto modes.
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 n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
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。
Migrates a Java project from FastExcel 1.3 (cn.idev.excel:fastexcel:1.3.0) to Apache Fesod (org.apache.fesod:fesod-sheet:2.0.1-incubating). Invoke this skill when asked to "migrate FastExcel to Fesod", "upgrade to Apache Fesod", or "replace cn.idev.excel". FastExcel 1.3 is the direct predecessor of Apache Fesod. Supports both legacy namespaces seen in real projects: cn.idev.excel.* and org.apache.fesod.excel.*. The entry classes FastExcel and FastExcelFactory are kept as @Deprecated aliases in Fesod, so call-site renames are strongly recommended but NOT required for compilation. The only breaking change is the Java package prefix.
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-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG) and in-scope outbound links. Use when user mentions deep crawl website, recursive crawl, crawl a whole site, scrape entire website, scrape docs site, scrape documentation, scrape knowledge base, scrape blog, build RAG corpus, build vector database from website, knowledge base for chatbot, GPT knowledge files, llms.txt, sitemap crawl, BFS crawl, scrape with depth or page limit, include exclude URL globs, remove boilerplate, strip navigation header footer, website to markdown, website to text, multi-page extraction, bulk page scraping, clean markdown from URL, docs site to markdown corpus, site to clean corpus. Also applies to building RAG pipelines, indexing a customer site, syncing docs into a vector store, generating training corpora from any docs hub, or expanding a single start URL into a clean corpus of every reachable in-scope page.
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。
X (Twitter) DM automated chat end-to-end Skill: scan DM inbox to identify pending-reply conversations, read message history, generate persona-based replies and send; also supports searching users and starting new conversations. Built-in E2E passcode unlock, DM permission filtering, and rate control. Use when user mentions X auto-reply DMs, Twitter DM automated chat, auto-handle unread DMs, reply to X private messages with persona, X DM outreach campaign, batch send DMs to Twitter users, auto-process pending DM replies, Twitter DM bot, automated Twitter outreach, X direct message automation.
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。