MemoryBenchを使用して、プロバイダー、データセット、評価モデル、チェックポイント、構造化レポートにわたって、再現可能な会話メモリとRAGのベンチマークを実行します。
MemoryBenchは、AIエージェントのメモリおよびRAG（Retrieval-Augmented Generation）システムのパフォーマンスを、統一されたフレームワークで評価・比較するためのツールです。複数のメモリ/RAGプロバイダーを横断的にベンチマークできるため、特定のユースケースに最適なプロバイダーを客観的に選定できます。また、実行結果は構造化されたレポートとして出力され、成功/失敗の状況や各種メトリクスを詳細に分析できます。Bunランタイム上で動作し、GitHub上で公開されているリポジトリから簡単にインストール可能です。AIエージェント開発者にとって、システムのメモリ性能や検索精度を定量的に評価し、改善につなげるための強力なツールです。このスキルはClaude CodeやCodexなどのエージェントフレームワークと組み合わせて使用することを想定しています。
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/benchmark-agent-memory-and-rag-systems-with-memorybenchMemoryBenchを使用して、プロバイダー、データセット、評価モデル、チェックポイント、構造化レポートにわたって、再現可能な会話メモリとRAGのベンチマークを実行します。
Bun、MemoryBenchリポジトリ、少なくとも1つのメモリ/RAGプロバイダーAPIキー、少なくとも1つの評価モデルAPIキー、ベンチマークデータセット
基本的な使い方やスタートガイドのメモ:
🆚 マルチプロバイダー比較: 同じベンチマークをプロバイダー間で横並びに実行
📊 構造化レポート: 実行ステータス、失敗、メトリクスを分析用にエクスポート
bun install
元のドキュメントから抽出: https://raw.githubusercontent.com/supermemoryai/memorybench/HEAD/README.md
This repo contains 20 individual skills — each has its own dedicated page.
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act is an open-source CLI tool that runs GitHub Actions workflows locally using Docker, enabling fast feedback on workflow changes without pushing to GitHub. It is a standard tool for local Actions development and testing.
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Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.
Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.
Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.
Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.
Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.
Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.
Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.
Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.
Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.
Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.
Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.
Calls Adobe Firefly
Extract frames or short clips from videos using ffmpeg.
Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.
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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...
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