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affaan-m/literature-review

面向学术、生物医学、技术和科学主题的系统性文献综述工作流,涵盖检索规划、来源筛选、综合总结、引文核查与证据记录。

literature-review 是什么?

该技能提供了一套完整的系统性文献综述工作流,适用于学术、生物医学、技术和科学等领域。它能够帮助用户从选题规划、检索策略制定、数据库搜索、来源筛选与质量评估,到最终的综合分析与引文核查,实现全流程的结构化管理。该技能主要基于命令行或AI代理环境(如Claude Code、Cursor、Codex等)运行,通过内置的SKILL.md文档指导用户一步步执行文献综述步骤。用户可以利用它记录检索词、筛选结果、保存证据摘要,并自动生成规范化的参考文献列表。该技能的核心价值在于将繁琐、易出错的文献综述流程标准化和半自动化,显著提升研究效率与可重复性,特别适合研究生、科研人员及需要定期撰写综述报告的从业者。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/literature-review

Installed? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/everything-claude-code · View all 6 →

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打开一个已预加载此 Agent Skill 的新对话。

文档

literature-review 是做什么的?

系统性文献综述工作流,适用于学术、生物医学、技术和科学主题,包括检索规划、来源筛选、综合总结、引文核查与证据记录。

概述

此技能旨在简化和规范化系统性文献综述的完整流程。它提供了一个结构化的框架,从定义研究问题到最终的报告撰写,覆盖每个关键步骤。

功能特性

  • 检索规划:帮助定义研究问题,选择关键词和检索式。
  • 数据库搜索:指导在PubMed、Scopus、Web of Science等主流数据库中进行系统检索。
  • 来源筛选:支持基于标题/摘要筛选,以及全文阅读后的纳入/排除决策。
  • 质量评估:内置标准化质量评估量表(如Cochrane偏倚风险评估工具)。
  • 综合与总结:辅助提取数据、综合发现、撰写叙述性总结。
  • 引文核查:自动检测引用是否准确,并管理参考文献列表。
  • 证据日志:记录每个步骤的决策过程和证据来源。

使用方法

在支持的AI代理(如Claude Code、Cursor、Codex)中,可以直接引用此技能。例如:

# 初始化文献综述项目
literature-review init --topic "

或者通过自然语言指令触发工作流:

请帮我开始一个关于“人工智能在医疗诊断中的应用”的系统性文献综述,使用affaan-m/literature-review技能。

支持平台

  • Claude Code
  • Cursor
  • Codex
  • 其他兼容AI代理

许可证

Apache-2.0

Individual skills in this repo

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

accessibility

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.

affaan-m/claude-api

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.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

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.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

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.

agent-eval

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.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

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.

agentic-os

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.

agent-introspection-debugging

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.

agent-payment-x402

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.

agent-self-evaluation

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.

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