エンドツーエンドのマーケティングキャンペーン計画と実行。オーディエンス調査、ポジショニング、キャンペーンアングルの定義、ランディングページのコピー、メールシーケンス、ソーシャル投稿、広告コピー、ショートフォーム動画スクリプト、コンテンツカレンダーをカバーします。マルチチャネル製品ローンチのオーケストレーションレイヤーとして使用します。マルチチャンネル製品ローンチの計画や実行、またはランディングページ、メール、ソーシャル、広告コピーの作成時に使用します。
このスキルは、マーケティングキャンペーンをエンドツーエンドで計画および実行するための包括的なツールです。オーディエンスリサーチからポジショニング、キャンペーンアングルの定義、ランディングページやメールシーケンス、ソーシャルメディア投稿、広告コピー、ショートフォーム動画のスクリプト、コンテンツカレンダーの作成までをカバーします。マルチチャネルでの製品ローンチを円滑に進めるためのオーケストレーションレイヤーとして機能し、特にClaude CodeなどのAIエージェント環境で動作します。開発者やマーケティング担当者が一貫性のあるキャンペーンコンテンツを効率的に生成できるよう支援し、手動の作業を大幅に削減します。このスキルは、複数のチャネルにわたるプロモーション活動を統合し、スケーラブルなマーケティング戦略の実行を可能にします。
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/manim-videoエンドツーエンドのマーケティングキャンペーン計画と実行。オーディエンス調査、ポジショニング、キャンペーンアングルの定義、ランディングページのコピー、メールシーケンス、ソーシャル投稿、広告コピー、ショートフォーム動画スクリプト、およびコンテンツカレンダーをカバーします。マルチチャネル製品ローンチのオーケストレーションレイヤーとして使用します。マルチチャネル製品ローンチの計画や実行、またはランディングページ、メール、ソーシャル、広告コピーの作成時に使用します。
This repo contains 20 individual skills — each has its own dedicated page.
Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.
gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.
Example project-specific skill template based on a real production application.
Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.
Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.
Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.
Extract frames or short clips from videos using ffmpeg.
Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.
Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.
Obra superpowers agent skills.
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...
Prevent silent decimal mismatch bugs across EVM chains. Covers runtime decimal lookup, chain-aware caching, bridged-token precision drift, and safe normalization for bots, dashboards, and DeFi tools. Use when handling token amounts across EVM chains, or when a balance, price, or transfer amount is off by orders of magnitude.