536 件 ・ 関連度順
概要と使いどころ
Interact with 80+ websites and desktop apps from the CLI via Chrome session reuse — zero LLM cost, deterministic JSON/YAML/CSV output. Covers Twitter/X, Reddit, YouTube, HN, Instagram, Spotify, Amazon, LinkedIn, and more; also automates browser actions and registers local CLI tools. Triggers on 'opencli', 'CLI for website', 'scrape without API', 'browser automation', 'operate command'.
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 interactive live slide decks with Vite + React 19, TanStack Router, WebGPU shaders, Framer Motion orchestration, 1920x1080, access-gated SPA. 39 components (layout, interaction, graphics, chrome, navigation), direction-aware slide transitions, presenter mode with speaker notes and BroadcastChannel sync, Playwright PNG/PDF export, YAML-driven authoring, Cloudflare/Vercel/Netlify deploy. Triggers on: slide deck, presentation, pitch deck, investor deck, product demo, conference talk, live slides, WebGPU slides, interactive presentation, presenter mode, speaker notes, overwatch deck.
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。
Review a SKILL.md file for compliance with the agentskills.io standard. Checks YAML frontmatter fields, required sections, line count limits, procedure step format, and registry synchronization. Use when a new skill needs format validation before merge, an existing skill has been modified and requires re-validation, performing a batch audit of all skills in a domain, or reviewing a contributor's skill submission in a pull request.
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 Quarto document for reproducible reports, presentations, or websites. Covers YAML configuration, code chunk options, output formats, cross-references, and rendering. Use when creating a reproducible analysis report, building a presentation with embedded code, generating HTML, PDF, or Word documents from code, or migrating an existing R Markdown document to Quarto.
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。
Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries by cofolding, and interpret confidence scores (pLDDT, pTM, ipTM, PDE) and affinity output (binder probability and log10 IC50). Also trigger on Boltz, Boltz-1, Boltz-2, cofolding, boltz predict, affinity_pred_value, affinity_probability_binary, ipTM, or open-weights AlphaFold3 alternatives.
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。
Testing React Native: Jest with jest-expo / react-native preset, React Testing Library Native, native module mocking, hook testing. Optional sections for Detox (native automation) and Maestro (declarative YAML e2e). Use this skill to: - Configure Jest preset based on workflow. - Write component tests with @testing-library/react-native. - Mock native modules (react-native-reanimated, expo-*, react-native-mmkv). - Test custom hooks via renderHook. - Set up Detox or Maestro for e2e (optional, requires native toolchain). Do NOT use this skill for: - General RN conventions (see rn-conventions). - Web-React testing (see react-plugin:react-testing — different jsdom setup). - Plain Node testing patterns (see nodejs-plugin equivalents).
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 AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, defer capability loading, stream output, define agents from YAML, or test agent behavior.
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。
从游戏客户端（安装包/APK/IPA/EXE 或 dump.cs、lua、usmap、抓包等）反推服务端协议并复现可部署服务端。含阅读路径分派、原理层(primer：三要素/数据包协议/协议表/热更源码)、四阶段路线图(workflow-roadmap：静态分析→建工具+登录链→重定向→补包循环→清单迭代)、11 种反推方法选择器（含内联服务端路线）、接口清单提取器（tools/）、协议规格模板(protocol.spec.yaml)、wire 级定点改写（不等 schema 齐就能跑）、客户端地址来源清查、三轴状态与验收体系、发布运维清单、进度清单(TRACKER.md)、登录链条与功能清单模板。覆盖：账号登录注册、房间匹配、战斗、掉落物资、抽卡、充值与邮件、部署(本地/服务器/Termux/Windows)、客户端对接、闭环验证；并支持 Unity(IL2CPP/Mono/Lua)、Unreal(UE4/UE5)、Cocos2d-x/Cocos Creator(JS/Lua 热更)、C#、AS3、Java、JS 等客户端；含进服后的 Live 运营手册；并含「内联服务端」路线（客户端进程内合成响应、运行时对象合成与字段发现、平台 SDK 登录态复用与目录服/区服两段准入）。
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。
执行模型量化。通过 msmodelslim quant 依据 Practice YAML 对模型进行量化。
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。
量化专家经验调优库：按「L1 通用专家调优意见 → L2 结构化专家调优意见 → L3 模型专属专家调优意见」三级递进， 输出量化精度调优的完整手段（离群值抑制 / 量化方法·粒度·对称性选型 / 校准集调整 / 敏感层回退 / 模型专属策略）， 每条结论附专家原因分析与专家意见可信度等级。 本 Skill 是专家经验库，负责说明可选离群值抑制算法并询问用户选择哪些算法，以及回答「怎么调、哪些层需要回退及为什么」；不运行离群值抑制 processor，不修改 YAML、不执行量化、不做 EP 检查 / 服务化 / 任务评测。
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。
Append a DiT-tuning round to history.yaml. DiT-side counterpart of the LLM-path accuracy_append.py; records practice.md5, inference_outputs, fp_baseline_outputs, and the scoring fields (scores / overall_score / loss_vs_baseline / is_satisfied) populated by quant-tuning-score-dit. Idempotent on practice_id.
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。
执行模型测评（统一入口）。LLM/VLM 走主流程 scripts/run_evaluation.py + Evaluation YAML；model_family=dit 走 DiT 扩展节（references/dit/evaluate_workflow.md，vbench.py 批量推理产出视频）。
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。
Generate msmodelslim evaluation YAML configuration (service_oriented + aisbench + vllm-ascend). Use when user asks for evaluation config generation.
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。
当用户直接要求进行数据集压缩测评，或在量化调优过程中需要使用 AISBench 评测工具输出数据集精度等场景时使用。 本 Skill 负责：使用 AISBench 数据集压缩代码、用 RBF Kernel Herding 算法生成压缩后的数据子集（coreset），并按用户目标交付两种产物之一： ① 接入量化调优主流程：产出独立的 coreset 数据集配置（不覆盖原始数据），回传「全集 config_name + coreset config_name」给编排层，供 evaluation.yaml 切换； ② 仅数据集压缩测评（不做量化调优）：基于 example 模板生成「全集测试脚本」和「子集测试脚本」两份 shell 脚本。 暂时支持 AIME 2025 和 GPQA，其他数据集需要再结合 aisbench tools目录下 herding_coreset_selector 源码进行分析。
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。
Generate 5 different narrative scripts for visual slideshows from a single positioning angle. Each script uses a different storytelling framework (AIDA, PAS, BAB, Star-Story-Solution, Stat-Flip) producing genuinely different stories, not layout variations. Make sure to use this skill whenever the user wants slideshow scripts, TikTok content scripts, carousel copy, narrative frameworks for visual content, or says anything about writing scripts for slides or social media storytelling. Even 'write me some TikTok content' or 'I need carousel copy' should trigger this. Outputs structured YAML content specs that chain directly to /paper-marketing for visual design.
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。
On-brand visual marketing content using Paper MCP and parallel agent teams. Reads the project's brand/ directory to build a design system, then spawns designer agents in parallel — each creating a unique artboard with a different on-brand layout interpretation. Intelligently adapts agent count and approach based on user goals via AskUserQuestion interrogation. Produces Instagram carousels, TikTok slideshows, social posts, story slides, and visual assets. When used with content spec YAMLs from /slideshow-script, each agent gets a unique script AND unique design direction. Make sure to use this skill whenever the user wants visual marketing content designed in Paper — carousels, slideshows, social posts, story slides, or any visual asset. Even if they just say "design something" or "make slides" or "create visual content", this is the skill. Also triggers on "paper marketing", "instagram design", "TikTok design", "slideshow design", "create carousel", or "social graphics".
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。
Set up or refine open-source .NET code-quality gates for CI: formatting, `.editorconfig`, SDK analyzers, third-party analyzers, coverage, mutation testing, architecture tests, and security scanning. Use when a .NET repo needs an explicit quality stack in `AGENTS.md`, docs, or pipeline YAML.
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。
Datadog provider for obz. Supports metrics (Datadog Query Language), logs (Datadog Log Query), and traces. Requires api-key and app-key configured under auth in config.yaml. Does not support metric labels, label-values, or series commands. This skill should be used when the user mentions "Datadog", "DD", "obz -p dd", Datadog Query Language, or needs to configure Datadog provider instances with regional endpoints.
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。
Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, host, publish, or set up their application on Render's cloud platform.
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。
Generates Claude Code plugin agent .md files: writes YAML frontmatter (name, model, color, tools, hooks, disallowedTools), crafts system prompts, and creates triggering example blocks with context and commentary. Validates agent structure against naming, length, and format constraints. Use when the user asks to create an agent, add an agent, write a subagent, configure agent frontmatter, design agent system prompts, set agent tools or colors, build an autonomous agent, or add agent hooks.
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。
Generate AGENT.md files with proper YAML frontmatter, role definitions, expertise areas, and prompt templates following Babysitter SDK conventions.
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。
Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production 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。
This skill should be used when the user asks to "create an agent", "write a subagent", "generate agent definition", "add agent to plugin", "write agent frontmatter", "create autonomous agent", "build subagent", needs agent structure guidance, YAML frontmatter configuration, invocation criteria with examples, or wants to add specialized subagents to Claude Code plugins with proper capabilities lists and tool access definitions.
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, update, review, fix, or debug VS Code agent customization files (.instructions.md, .prompt.md, .agent.md, SKILL.md, copilot-instructions.md, AGENTS.md). Use for: saving coding preferences; troubleshooting why instructions/skills/agents are ignored or not invoked; configuring applyTo patterns; defining tool restrictions; creating custom agent modes or specialized workflows; packaging domain knowledge; fixing YAML frontmatter syntax.
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。