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Benchmark deep research agents across factual, quality, and proc

Score deep research agents on benchmark tasks using factual verification, report-quality scoring, and process evaluation before model or workflow changes ship.

Benchmark deep research agents across factual, quality, and proc 是什麼？

Benchmark deep research agents across factual, quality, and proc is a Claude Code agent skill that score deep research agents on benchmark tasks using factual verification, report-quality scoring, and process evaluation before model or workflow changes ship.

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/benchmark-deep-research-agents-across-factual-quality-and-process-dimensions-with-miroeval
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說明文件

Benchmark deep research agents across factual, quality, and process dimensions with MiroEval

Score deep research agents on benchmark tasks using factual verification, report-quality scoring, and process evaluation before model or workflow changes ship.

Prerequisites

Python, uv, model result JSON, required API keys for judge and retrieval services

Installation

No source-backed install or usage instructions could be extracted automatically. Review the upstream project before running this skill in a sensitive workflow.

Documentation

Source

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

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Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

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.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

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Add multimodal lifelong memory to MCP and Python agents with Sim

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Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

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Add temporal context memory to agent workflows with Zep

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.

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