Skills
18,283 skills, most installed first.
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hairy-utilshairyfComprehensive skills for working with @hairy/utils core utilitiesmotionhairyfMotion animation library for JavaScript, React and Vue. Use when creating animations, gestures, layout transitions, scroll-linked effects, or working with motion values and animation controls.nexthairyfNext.js framework for building React applications with App Router, Server Components, and optimized performance. Use when working with Next.js apps, routing, data fetching, caching, Server Actions, or building full-stack React applications.openapi-specification-v2hairyfOpenAPI (Swagger) 2.0 specification for describing REST APIs. Use when writing, validating, or interpreting Swagger 2.0 specs, generating clients/docs, or working with path/operation/parameter/response/schema/security definitions.openapi-specification-v3.2hairyfOpenAPI Specification 3.2 — write and interpret OpenAPI descriptions (OAD), paths, operations, parameters, request/response, schema (JSON Schema 2020-12), security, and extensions. Use when authoring or validating OpenAPI 3.2 documents.overlastichairyfComprehensive skills for working with Overlasticreact-usehairyfCollection of essential React Hooks for sensors, UI, animations, side-effects, lifecycles, and state managementtsdownhairyfBundle TypeScript and JavaScript libraries with blazing-fast speed powered by Rolldown. Use when building libraries, generating type declarations, bundling for multiple formats, or migrating from tsup.undocshairyfMinimal Documentation Theme and CLI for shared usage across UnJS projects. Use when creating documentation sites with Nuxt, Nuxt Content, and Nuxt UI.unjshairyfUnJS ecosystem - agnostic JavaScript libraries, tools, and utilities. Use when working with UnJS packages like h3, nitro, ofetch, unstorage, or building universal JavaScript applications.valtiohairyfValtio proxy state management for React and vanilla JavaScript. Use when creating reactive state, managing application state, or working with proxy-based state management.valtio-definehairyfComprehensive skills for working with valtio-definevitesthairyfVitest fast unit testing framework powered by Vite with Jest-compatible API. Use when writing tests, mocking, configuring coverage, or working with test filtering and fixtures.writing-humanizer-zhhairyf去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。 基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式：夸大的象征意义、 宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、 AI 词汇、否定式排比、过多的连接性短语。gpt-imagehalt-catch-fireGenerate and edit images with OpenAI GPT-Image-2 via inference.sh CLI. Models: GPT-Image-2. Capabilities: text-to-image, image editing, inpainting, mask-based editing, multi-image reference, batch generation. Use for: product mockups, marketing visuals, image editing, concept art, inpainting, photo manipulation. Triggers: gpt image, gpt-image-2, openai image, chatgpt image, dall-e, dalle, openai image generation, gpt image edit, gpt inpainting, openai dall-e, gpt 4o imagehappyhorsehalt-catch-fireGenerate and edit videos with Alibaba HappyHorse 1.0 models via inference.sh CLI. Models: HappyHorse T2V, I2V, R2V, Video Edit. Capabilities: text-to-video, image-to-video, reference-to-video, video editing with natural language, character preservation, 720P/1080P, up to 15 seconds. Use for: physically realistic video, video editing, character-consistent content, product demos, social media. Triggers: happyhorse, happy horse, alibaba video, happyhorse 1.0, dashscope video, alibaba happyhorse, vip-imagehalt-catch-fireGenerate images with Pruna P-Image models via inference.sh CLI. Models: P-Image, P-Image-LoRA, P-Image-Edit, P-Image-Edit-LoRA. Capabilities: text-to-image, image editing, LoRA styles, multi-image compositing, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna, p-image, pruna image, fast image generation, optimized flux, pruna ai, p image, fast ai image, economic image generation, cheap image generationp-videohalt-catch-fireGenerate videos with Pruna P-Video and WAN models via inference.sh CLI. Models: P-Video, WAN-T2V, WAN-I2V. Capabilities: text-to-video, image-to-video, audio support, 720p/1080p, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna video, p-video, pruna ai video, fast video generation, optimized video, wan t2v, wan i2v, economic video generation, cheap video generation, pruna text to video, pruna image to videop-video-avatarhalt-catch-fireGenerate talking head avatar videos with Pruna P-Video-Avatar via inference.sh CLI. Turn a portrait image into a realistic speaking video with built-in TTS. 18x faster and 6x cheaper than competitors. Models: P-Video-Avatar, P-Image (for portrait generation). Capabilities: text-to-avatar, audio-driven avatars, 30 voices, 10 languages, 720p/1080p, built-in TTS, dynamic backgrounds, full-body control. Use for: AI presenters, product demos, explainer videos, virtual influencers, marketing, educatioseedancehalt-catch-fireGenerate videos with ByteDance Seedance 2.0 via inference.sh CLI. Unified model for text-to-video, image-to-video, and reference-to-video with synchronized audio, up to 1080p, 4-15s duration. Pro and Fast variants. Studio variants with private asset library for portrait consistency. Use for: social media videos, music videos, product demos, animated content, AI video with sound. Triggers: seedance, seedance 2, bytedance video, seedance t2v, seedance i2v, seedance r2v, video with audio, seedance build-review-interfacehamelsmuBuild a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.error-analysishamelsmuHelp the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.evaluate-raghamelsmuGuides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.generate-synthetic-datahamelsmuCreate diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.validate-evaluatorhamelsmuCalibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with standard unit tests).write-judge-prompthamelsmuDesign LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests). Do NOT use when you need to validate or calibrate the judge — use validate-evaluator instead.bilibili-subtitlehamsterider-mpatent-disclosure-skillhandsomestwei中国专利技能：挖掘专利点与编写交底书（发明/实用/外观），把已有交底改写成申请文件四件套，也可按材料交底申请一起做，按著录字段检索公布公告，通俗解读专利，基于已读库打开专利地图，对照审查口径出政策简报，辅助审查答复。| China patents skill: mine patent points and draft disclosures, rewrite an existing disclosure into application documents, or chain disclosure-then-application from inventor materials in one pass (ask when facts are missing; at most three issue-list rounds), search CNIPA bibliographic records, explain patents, open a local patent map from interpreted notes, brief examination-policy changescipilot-figure-skillhaojaeSciPilot Skills 家族成员，负责科研数据可视化——但定位不是"画图工具"， 而是"可视化顾问"。先做数据剖析（列类型/样本量/分布/异常值/分组结构/相关性）， 再结合用户的论证目标推荐图型，主动拦截科研画图的经典错误（小样本画均值柱掩盖 分布、双 Y 轴、饼图、Y 轴不当截断、rainbow 色图、把分类点连成折线等），最后 产出 Nature / Science / IEEE / Elsevier / PNAS / 中文核心期刊级别的成图。 覆盖纯数据图：折线、柱状、散点、箱线 / 小提琴、热力图、误差棒、分布图（直方 图 / KDE）、相关性矩阵 / 散点矩阵、多面板组合。技术栈 matplotlib + seaborn + SciencePlots（静态）+ plotly（交互）。中英文双语，中文模式自动配置 Noto Sans CJK / Source Han Sans / SimHei 并修复负号方框，支持中文期刊"宋体正文 + Times New Roman 数字"混排。默认色盲安全配色 + 冗余编码 + 灰度预览。出图后做 "视觉自检闭环"：渲染 PNG character-simhaowjySpeak as a specified character from their current knowledge, voice, and emotional state. Use for skill-only workflows that need in-character conversation, voice discovery, or relationship pressure tests.creative-researchhaowjyLoad when a story needs factual grounding the writer doesn't have: historical detail, cultural texture, domain accuracy, or how other authors handled similar material. Pass the question and story context; returns a sourced report the writer can draw from.creative-writing-crafthaowjyCraft references for writing fiction well: prose, scenes, style, voice, and genre/page-level technique. Load when a writer, critic, or muse needs how-to-write guidance rather than a production mode.creative-writing-modeshaowjyCreative-writing addendum to /llm-writing. Load when putting prose on the page: draft, revise, bridge, vary, or polish.creative-writing-musehaowjyLoad when no subagents are available and one agent must plan, draft, critique, research, and capture memory by switching stances.grill-with-docshaowjyUse when challenging a plan — grills the author against documented decisions and sharpens terminology.intent-modelinghaowjyUse before acting on human instructions: separate what they said from what they meant.kb-managementhaowjyMaintaining the story knowledge base: creating, updating, and organizing wiki-style reference pages in kb/. Use when capturing finalized story knowledge, updating character profiles, documenting world mechanics, or restructuring the kb.llm-writinghaowjyLoad before writing or revising human-facing text. Choose words deliberately, ground the piece in the reader's context, and remove default LLM phrasing before the final draft.project-setuphaowjyOne-time project setup for creative writing. Interviews you about your project, collects writing samples, proposes kb structure, and creates CLAUDE.md with project conventions.reader-simhaowjyRead as a specified first-time reader persona and report the felt experience. Use for skill-only workflows when a draft needs persona-bound reader-response signal instead of analytical critique.shared-daohaowjyShared vocabulary for creative writing projects. Load when establishing canonical story terms, resolving ambiguous names, checking term consistency, or deciding where vocabulary belongs in kb/.story-memoryhaowjyCreative-writing domain knowledge for durable story state. Load when preserving or retrieving project memory — fact extraction, context scoping, reference writing, artifact layout, and issue tracking. If you are a knowledge agent such as kb-lead, load this for the fiction-specific categories and conventions your general methodology doesn't cover.story-planninghaowjyPlanning work before prose: creative direction, story-planning, outlining, and story architecture. Load when deciding what should happen or how a story should be structured.story-reviewhaowjyReview work after prose exists: editorial review, craft critique, continuity/voice review, copyediting, proofreading, and synthesis of reader-sim signal. Load when diagnosing a draft rather than rewriting it.writing-principleshaowjyWhat fiction readers want (reader reward channels) and the specific ways LLM training damages them. Load when drafting prose, critiquing, or diagnosing why a passage feels flat.writing-staffinghaowjyDispatch reference for composing writing teams. Teaches which skills to load for each subagent, which resources to reference, and when to fan out versus run parallel lanes. Load when staffing a workflow.create-taskharbor-frameworkCreate a new Harbor task for evaluating agents. Use when the user wants to scaffold, build, or design a new task, benchmark problem, or eval. Guides through instruction writing, environment setup, verifier design (pytest vs Reward Kit vs custom), and solution scripting.owasp-mobile-security-checkerharishwarriorUse when performing security audits, vulnerability assessments, or compliance checks on Flutter or mobile applications. Covers OWASP Mobile Top 10 (2024) — hardcoded secrets (M1), insecure storage (M9), weak cryptography (M10), network issues (M5), and 6 more categories with automated scanners and remediation guidance.harper-best-practicesharperfastBest practices for building Harper applications, covering schema definition, automatic APIs, authentication, custom resources, and data handling. Triggers on tasks involving Harper database design, API implementation, and deployment.provider-configurationhashicorpImplement Terraform provider configuration and authentication with the Plugin Framework: provider schema for credentials (Optional + Sensitive attributes), environment variable fallbacks, credential provider chains (static config, then environment variables, shared credentials file, and platform identity), unknown-value guards in Configure(), secret redaction, configure-time credential validation, and diagnostics that name every source tried. Use when implementing or reviewing a provider's Confi