Borrowing it
Nothing to install: this file belongs to stephenlzc/AI-Powered-Literature-Review-Skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/stephenlzc/AI-Powered-Literature-Review-Skills/main/AGENTS.mdgit clone --depth 1 https://github.com/stephenlzc/AI-Powered-Literature-Review-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/stephenlzc/ai-powered-literature-review-skills/agents-md)<a href="https://agentmods.dev/instructions/stephenlzc/ai-powered-literature-review-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/stephenlzc/ai-powered-literature-review-skills/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/stephenlzc/ai-powered-literature-review-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/stephenlzc/ai-powered-literature-review-skills/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04040 | $0.04040 |
| Opus 5 | $0.02020 | $0.02020 |
| Sonnet 5 | $0.00808 | $0.00808 |
| Haiku 4.5 | $0.00404 | $0.00404 |
Grade A, and why
AI-Powered-Literature-Review-Skills AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md - Literature Reviewer Skill
项目概述
本项目是一个 Kimi CLI Skill(技能插件),名为 literature-reviewer-skill,用于帮助用户进行系统性的学术文献回顾(Literature Survey)。
版本: 3.1.0
最后更新: 2025-03-09
核心特性
- 8阶段工作流:完整的文献调研流程
- 浏览器自动化:无需API配置,直接访问数据库
- 多数据库支持:CNKI、Web of Science、ScienceDirect、PubMed
- 结构化输出:GB/T 7714-2015引文 + 标题 + 摘要的Markdown文档
- 综述生成:自动生成结构化的文献综述
技术栈
- 语言: Python 3.8+
- 架构: 顺序执行工作流
- 数据库访问: 浏览器自动化(Playwright)
- 外部依赖:
- Kimi CLI 的
browserskill - Kimi CLI 的
docxskill(可选)
- Kimi CLI 的
项目结构
literature-reviewer-skill/
├── SKILL.md # Skill 定义文件(主入口)
├── AGENTS.md # 本文件
├── README.md # 项目说明文档
│
├── agents/ # Agent 模板目录
│ ├── explore-agent.md # 搜索 Agent 模板
│ ├── verify-agent.md # 验证 Agent 模板
│ ├── synthesize-agent.md # 综述 Agent 模板
│ └── orchestrator.md # 协调器 Agent 模板
│
├── references/ # 参考资料文档
│ ├── cnki-guide.md # CNKI 检索详细指南
│ ├── database-access.md # 各数据库访问指南
│ └── gb-t-7714-2015.md # GB/T 7714-2015 引用格式规范
│
└── assets/ # 资源目录(预留)
8阶段工作流
Phase 0: Session Log → 创建会话目录
Phase 1: Query Analysis → AI生成关键词和检索策略
Phase 2: Parallel Search → 浏览器自动化检索
Phase 3: Deduplication → 去重筛选
Phase 4: Verification → 元数据验证
Phase 5: Data Export → 导出文献信息
Phase 6: Paper Analysis → 单篇文献深度分析
Phase 7: Citation Format → GB/T 7714-2015格式化
Phase 8: Synthesis → 生成综述(大纲→撰写→审查→润色)
Phase 0: Session Log(会话管理)
目标:创建会话目录,记录工作进度,支持中断续传
目录结构:
sessions/{YYYYMMDD}_{topic_short}/
├── session_log.md # 工作日志
├── metadata.json # 会话元数据
├── papers_raw.json # 原始检索结果
├── papers_deduplicated.json # 去重后文献
├── papers_analysis.json # 文献分析结果
└── output/
├── references.md # 文献清单(含摘要)
├── papers_analysis.md # 单篇文献深度分析
└── literature_review.md # 最终综述(含摘要、关键词)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 470 lines · 4,040 tokens per session scan A bd563c3252e8
AI-Powered-Literature-Review-Skills AGENTS.md is an instructions file published in the GitHub repository stephenlzc/AI-Powered-Literature-Review-Skills (141 stars, last pushed 5d ago), licensed MIT. It adds 4,040 tokens to every session, about $0.0202 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).