1,858 件 ・ 関連度順
概要と使いどころ
Map this token system to or from any external design system (Material Design 3, Apple HIG, Fluent, Carbon, Ant, shadcn/ui, Radix, Chakra, Mantine, Bootstrap…) — adopt their look, build on their stack, or migrate between systems. Use when the user mentions interop, migration, or a specific design-system/component-library bridge.
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。
House style for user-facing documentation — voice, scope, structure, and what to leave out. Use this skill whenever writing, editing, or reviewing anything a user reads to learn how to use a tool — help center articles, getting-started guides, tutorials, feature docs, README usage sections, in-app help, release notes, or FAQ entries. Trigger it even when the request is phrased plainly — "document this feature", "write docs for X", "explain this to users", "write a README for this library", "turn these notes into a guide" — and even when no style guidelines are mentioned. Also use it to review existing docs for tone, bloat, or leaked implementation detail. Do not use it for internal engineering docs, architecture write-ups, RFCs, or code comments.
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。
dontbesilent 个人决策系统。把任何一个需要长期跟踪的领域（业务、关系、健康、职业、学习、投资……）做成一个本地知识工程：四层结构、来源标签、写完不改的快照、能炼出规律的概念库。 触发方式：/dbs-decision、/决策系统、/决策立案、/结果回填、/状态画像 Personal decision system. Turns any long-running domain into a local knowledge project with four layers, source tags, immutable snapshots, and a concept library that learns patterns over time. Trigger: /dbs-decision, /决策系统, /决策立案, /结果回填, /状态画像
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 the user wants to reverse-engineer an existing image ad into a reusable prompt template. Validates via Arcads — picks gpt-image-2 or Nano Banana at Phase 1. Triggers on "clone this ad as a template", "reverse engineer this ad", "turn this ad into a prompt", "extract a template", "make this ad reusable", "add to my prompt library", "study this ad and make a template". Input is an EXISTING ad image; does NOT trigger for fresh generation (use chatgpt-image-ad or nano-banana-image-ad).
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。
Creates and retrieves AI video and image-related assets via the Arcads external API (Seedance 2.0, Sora 2, Veo 3.1, Kling, Grok Video, Nano Banana, b-roll, scene, script/actor flows). Loads prompts from the bundled prompting guide and model library, respects HTTP Basic auth from ARCADS_API_KEY, and polls assets/videos until ready. Use when the user mentions Arcads, external-api.arcads.ai, Seedance, Sora2, Veo, Kling, Nano Banana, b-roll, UGC scripts, or generating marketing creative through Arcads.
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。
Publish finished creatives as live Meta (Facebook/Instagram) ads via the Meta Marketing API, plus research and ad-copy support. Uploads an image or video, builds a multi-variant TEXT_LIQUIDITY creative, and creates a PAUSED ad in an existing ad set. Also pulls top-performing ads (ranked by ROAS) and competitor ads from the Ad Library to inform copy. Use when the user asks to deploy / publish / launch a creative as a Meta or Facebook ad, build a Meta ad, push a video or image into an ad set, pull their top ads, or research competitor ads. Not for generating creative (use the image/video skills) and not for writing AdTable/Airtable rows (use adtable-light).
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 ships iOS/macOS apps with XcodeGen, SwiftUI and SPM: Xcode build fixes, device deployment, Apple Developer signing/notarization, CI/CD pipelines. Use for XcodeGen project.yml setup, "Library not loaded @rpath" crashes, code-signing errors (Error -25294, adhoc fallback), or Electron notarization failures. Not for TCC permission dialogs (use macos-permissions) or disk cleanup (use macos-cleaner).
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。
Gated checklist to choose or review a library, framework, storage, data format, model, build-vs-buy or architecture. Run favorites-search before external research when installed. Compare candidates and trade-offs; select within authorization or return user-owned choices. Use for 用哪个 / 选什么框架 / 要不要自建 / A 还是 B / 这个方案行不行 and before committing to one. Research reports → deep-research.
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。
Finding and accessing AI/LLM model brand icons from lobe-icons library. Use when users need icon URLs, want to download brand logos for AI models/providers/applications (Claude, GPT, Gemini, etc.), or request icons in SVG/PNG/WEBP formats.
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 learning paths for programming tools, and define what information should be researched to create learning guides. Use when user asks to learn, understand, or get started with any programming tool, library, or framework.
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。
Work with Spotify: read your profile, playlists, top tracks and artists, search the catalog, and (with confirmation) create playlists, add tracks, and save tracks to your library. Trigger phrases: spotify, my playlists, my top tracks, top artists, save track, search spotify.
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。
AI design specialist skill with DESIGN.md management, anti-pattern enforcement, optional Stitch MCP integration, and component library guidance. Covers typography, color systems, motion design (motion/react, GSAP, Three.js), responsive-first layouts, and accessibility (WCAG 2.2).
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。
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
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。
Make a showreel-grade motion-graphics launch video, with music, that introduces a CLI, library, or product from its repo. Use when asked for a launch, promo, sizzle, or showreel video, an animated intro for a project, or a video to post on X/social.
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。
Retrieve machine-readable context with tanstack libraries, tanstack doc, tanstack search-docs, tanstack create --list-add-ons --json, and --addon-details for agent-safe discovery and preflight validation.
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 preparing an accepted ACM CCS paper for the ACM Digital Library proceedings, covering de-anonymization, the ACM sigconf final format, ACM rights and eRights forms, badge placement from artifact evaluation, incorporation of shepherd and minor-revision requirements, disclosure-timing coordination, registration, and in-person presentation.
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。
Provider reference for collecting public profiles, posts, comments, transcripts, trends, and ad-library data from major social platforms through ScrapeCreators. Use as a dependency of research workflows, not as the final user-facing deliverable.
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 and install skills from the Orthogonal skill library. Use when you need capabilities you don't have, want to discover available skills, or need to add new tools to your agent.
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。
Scrapes LinkedIn job postings using the JobSpy library (python-jobspy). Use this skill whenever the user wants to find jobs on LinkedIn, search for open roles, pull job listings, build a job pipeline, source job targets for GTM research, or monitor hiring signals. Even if the user just says "find me some jobs" or "what roles is [company] hiring for", use this skill. It runs a local Python script that outputs a CSV of job postings with title, company, location, salary, job type, description, and direct URLs.
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。
Creates a new graphics format spec (custom canvas dimensions + content rules) and publishes it to the Gooseworks library so any agent can render against it. Use when the existing community formats (carousel, story, infographic, slides, poster, chart, tweet, plus community additions) don't fit the user's canvas — e.g., LinkedIn banner 1584×396, story cover 1080×1920, event flyer 8.5×11in. Mirrors goose-graphics-create-style but for formats.
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。
Scrape competitor ads from Meta's Ad Library (Facebook, Instagram, Messenger, Threads, WhatsApp). Search by company name, Facebook Page URL, or keyword. Returns ad creatives, spend estimates, reach, impressions, and campaign details. Use for competitive ad research, messaging analysis, and creative inspiration.
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。
General-purpose X/Twitter research agent. Searches X for real-time perspectives, dev discussions, product feedback, cultural takes, breaking news, and expert opinions. Works like a web research agent but uses X as the source. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", "/x-research", (2) user is working on something where recent X discourse would provide useful context (new library releases, API changes, product launches, cultural events, industry drama), (3) user wants to find what devs/experts/community thinks about a topic. NOT for: posting tweets or account management. Note: currently uses recent search (last 7 days). Full-archive search is available on the same pay-per-use X API plan but not yet implemented in this 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。
Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.
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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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。