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文献综述

针对学术、生物医学、技术和科学主题的系统性文献综述工作流程,包括检索规划、文献筛选、综合整理、引文核查和证据记录。适用于需要查找、筛选、综合并引用学术或技术文献的任务。

文献综述 是什么?

该技能为AI Agent提供了一个完整的文献综述工作流程,适用于学术、生物医学、技术及科学领域。它支持叙述性综述、范围综述、系统综述和荟萃分析等多种综述类型,并能根据用户需求推荐合适的严谨级别。核心工作流程包括:将用户问题转化为可检索的研究问题(如使用PICO框架),制定并执行检索方案,对文献进行去重和分阶段筛选(标题、摘要、全文),提取结构化数据(如研究设计、人群、方法、结果),然后按主题进行综合并区分证据置信度(高、中、低)。最后,它会验证引文(DOI、PMID等)的准确性,并生成包含研究问题、检索策略、证据总结、主题综合、局限性和引用列表的标准输出模板。该技能可在Claude Code、Cursor、Codex等支持自定义知识或技能的AI Agent平台上运行,通过系统化的步骤确保综述的可重复性和严谨性,适用于论文背景章节撰写、研究前沿探索或临床证据评估等场景。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

文献综述 是做什么的?

当任务需要查找、筛选、综合并引用学术或技术文献时,使用此技能。

使用时机

  • 构建系统性综述、范围综述或叙述性综述。
  • 综合某一研究问题的研究现状。
  • 发现知识空白、矛盾点或未来研究方向。
  • 为论文或报告准备有引文支持的背景部分。
  • 比较来自同行评审论文、预印本、专利和技术报告的证据。

综述类型

  • 叙述性综述:广泛综合,有助于了解概况。
  • 范围综述:映射概念、方法和证据空白。
  • 系统综述:预定义方案、可复现检索、明确的筛选和排除标准。
  • 荟萃分析:系统综述加上定量效应汇总。

询问用户需要何种严谨级别。如未指定,探索性工作默认为范围综述,发表或临床声明默认为系统综述。

工作流程

1. 定义问题

将提示转化为可检索的研究问题。

对于临床或生物医学工作,使用PICO:

  • 人群(Population)
  • 干预或暴露(Intervention or exposure)
  • 比较组(Comparator)
  • 结局(Outcome)

对于技术工作,使用:

  • 系统或领域(system or domain)
  • 方法或干预(method or intervention)
  • 比较基线(comparison baseline)
  • 评估指标(evaluation metric)

2. 规划检索

在收集文献前制定检索方案:

  • 要检索的数据库
  • 日期范围
  • 语言
  • 出版物类型
  • 纳入标准
  • 排除标准
  • 确切的检索词

最小实用数据库集:

  • PubMed(生物医学和生命科学文献)
  • arXiv(计算机科学、数学、物理学、定量生物学及预印本)
  • Semantic Scholar 或 Crossref(广泛的学术发现)
  • 领域特定来源,如临床试验注册库、专利数据库、标准机构或官方技术文档。

3. 检索并记录证据

保留使综述可复现的检索日志:

| 数据库 | 检索日期 | 检索式 | 筛选条件 | 结果数量 | 导出格式 |
| --- | --- | --- | --- | ---: | --- |
| PubMed | 2026-05-11 | `("CRISPR"[tiab] OR "Cas9"[tiab]) AND "sickle cell"[tiab]` | 2020:2026, 英语 | 86 | PMID列表 |
| arXiv | 2026-05-11 | `CRISPR sickle cell gene editing` | q-bio, 2020:2026 | 9 | BibTeX |

将原始ID、URL、DOI、摘要和笔记与最终文稿分开保存。

4. 去重

按以下顺序去重:

  1. DOI
  2. PMID或arXiv ID
  3. 确切标题
  4. 标准化标题加第一作者和年份

记录移除了多少重复项。

5. 筛选文献

分阶段筛选:

  1. 标题
  2. 摘要
  3. 全文

对于系统综述,记录排除原因:

  • 人群不符
  • 干预不符
  • 结局不符
  • 非原始研究
  • 重复
  • 无法获取全文
  • 超出日期范围

6. 提取数据

使用结构化提取表:

| 研究 | 设计 | 人群/数据 | 方法 | 比较组 | 结局 | 关键发现 | 局限性 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 作者 年份 | RCT/队列/综述等 | 样本或语料库 | 方法 | 基线 | 测量结局 | 结果 | 注意事项 |

对于技术论文,包含数据集、基准、指标、基线和可复现性说明。

7. 综合

按主题分组证据,而非逐篇总结论文。

有用的综合视角:

  • 最强证据
  • 矛盾证据
  • 方法学弱点
  • 人群或数据集限制
  • 时效性与可复现性
  • 实际意义
  • 未解答的问题

按置信度区分结论:

  • 高置信度:跨来源可复现、高质量证据。
  • 中等置信度:合理但受样本、方法或时效性限制。
  • 低置信度:早期、推测性、单一来源或测量薄弱。

8. 验证引文

定稿前:

  • 验证DOI、PMID、arXiv ID或官方URL
  • 检查作者姓名和发表年份
  • 不要引用未做出该结论的论文
  • 将预印本标记为预印本
  • 区分综述与原始研究

输出模板

# 文献综述:<主题>

生成日期:<日期>
综述类型:<叙述性 | 范围性 | 系统性 | 荟萃分析>
检索窗口:<日期>
数据库:<列表>

## 研究问题

## 检索策略

## 纳入与排除标准

## 证据总结

## 主题综合

## 空白与局限性

## 参考文献

## 检索日志

常见陷阱

  • 不要将检索摘要视为证据。
  • 不要混用预印本、综述和原始研究而不加以标注。
  • 不要遗漏阴性或矛盾的发现。
  • 在没有可复现方案的情况下,不要声称具有系统综述的严谨性。
  • 除非明确限定范围,否则不要仅使用单个数据库进行广泛声明。

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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