Borrowing it
Nothing to install: this file belongs to SUSTech-GenAI/awesome-researchclaw. 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/SUSTech-GenAI/awesome-researchclaw/main/AGENTS.mdgit clone --depth 1 https://github.com/SUSTech-GenAI/awesome-researchclawWrote 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/sustech-genai/awesome-researchclaw/agents-md)<a href="https://agentmods.dev/instructions/sustech-genai/awesome-researchclaw/agents-md"><img src="https://agentmods.dev/badge/instructions/sustech-genai/awesome-researchclaw/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/sustech-genai/awesome-researchclaw/agents-md"><img src="https://agentmods.dev/badge/instructions/sustech-genai/awesome-researchclaw/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 | $0.02728 | $0.02728 |
| Opus 5 | $0.01364 | $0.01364 |
| Sonnet 5 | $0.00546 | $0.00546 |
| Haiku 4.5 | $0.00273 | $0.00273 |
Grade A, and why
awesome-researchclaw 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 9d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Awesome ResearchClaw 维护指南
项目定位
这是一个 Awesome 聚合站仓库，核心目标是：获取流量 → 引流 → 建立生态影响力 → 获得 Star 和社区贡献。
参考标杆仓库：
- ComposioHQ/awesome-claude-skills — ⭐ 49.5k，Skills 聚合 + 商业引流（Composio 平台）
- ai-agents-2030/awesome-deep-research-agent — ⭐ 581，学术论文聚合 + ArXiv survey 引流
- chchenhui/awesome-research-agents — ⭐ 7，论文+项目聚合，纯学术列表
核心策略：从三个标杆仓库学到的增长方法论
策略 1：Composio 模式（49.5k Stars）— 实用工具引流
核心做法：
- README 顶部放 Quickstart — 让用户 30 秒内就能跑起来
- 每个条目都是一个可直接使用的 Skill 目录（不只是链接）
- 仓库本身就包含实际可运行代码，不仅仅是链接聚合
- 通过
connect-apps-plugin引流到自家商业平台platform.composio.dev - 大量社区 PR（352 open PRs）— 让社区为你生产内容
我们应学习的：
- README 顶部放一个 "30 秒体验 ResearchClaw" 的 Quickstart 区块
- 在仓库内放一些可直接
git clone && 使用的模板/配置/示例 - 引导用户去 ResearchClaw 主仓库、Discord、文档站
- 降低 PR 门槛，鼓励社区贡献（
CONTRIBUTING.md+ PR 模板 + Good First Issue）
策略 2：Deep Research Agent 模式（581 Stars）— 学术权威引流
核心做法：
- 绑定一篇 ArXiv Survey 论文，仓库是论文的 "活文档"
- 用专业的学术分类法组织（Search Engine / Tool Use / Architecture / Tuning / Benchmarks）
- 每个条目都有 论文链接 + GitHub 代码链接（双链结构）
- 提供 BibTeX Citation — 让学术引用反哺仓库流量
- 在论文和仓库之间形成双向引流闭环
我们应学习的：
- 增加 📖 Papers 分区，按研究阶段分类（Idea → Literature → Experiment → Writing → Review）
- 每篇论文条目格式：
[时间] 标题 + 作者 + 代码链接 - 如果团队有能力，写一篇 Survey 论文绑定此仓库
- 添加 Citation 区块，方便学术引用
策略 3：Awesome Research Agents 模式（7 Stars）— 反面教材
问题诊断：
- 纯论文列表，没有社区互动
- 没有 Quickstart、没有可运行代码
- 只有 1 个贡献者
- 没有 Topics 标签优化（只有
awesome-list ai-assistants llm-agents deep-research） - 没有引流闭环
我们应避免的：
- ❌ 不要做纯静态论文列表
- ❌ 不要只有一个人维护
- ❌ 不要忽视 GitHub SEO（Topics、Description、Social Preview）
README.md 维护规范
结构规范（从上到下的注意力漏斗）
1. 标题 + Awesome Badge + Star Badge + PR Badge
2. 一句话描述（中英双语）
3. ⭐ Quickstart 区块（30 秒体验） ← 参考 Composio
4. 目录（Contents）
5. 核心项目（带 Star 徽章）
6. 📖 论文区（按研究阶段分类） ← 参考 Deep Research Agent
7. 🔧 工具/项目区（带描述和标签）
8. 数据源/API 表格
9. 社区 & 学习资源
10. Citation（BibTeX） ← 参考 Deep Research Agent
11. Contributing + License
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.
- 9d ago First seen · 248 lines · 2,728 tokens per session scan A 1bf23a59cb61
awesome-researchclaw AGENTS.md is an instructions file published in the GitHub repository SUSTech-GenAI/awesome-researchclaw (58 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 2,728 tokens to every session, about $0.0136 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
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).
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).
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.
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.