我维护的多平台内容分发工具字流，过去一年累计提交了 192 次代码。
收件箱采纳了超过 1300 条评论和私信。
支撑这一切的 AI 工具，全年订阅成本约 1.7 万元。
如果你在 Claude Code 里不加裁剪，直接跑完一套企业级六阶段安全审计 Skill。
单次并发子智能体消耗的 Token 账单，就会直接击穿一人公司的预算底线。
这篇不谈开源生态的宏大叙事。
我直接拆解如何在 Claude Code 中接入 Cloudflare 开源的 security-audit-skill。
并且针对出海产品的核心暴露面裁剪工作流，避免无节制消耗额度。
这两天技术圈有个标志性事件。
Anthropic 在 2026 年 10 月 22 日宣布，把开放标准 Agent Skills 捐赠给 Linux 基金会旗下的 AAIF。
FourWeekMBA 10 月 23 日报道，这个标准已有 100 万个技能被发布，累计安装量近 2.8 亿次。
它支持包括 ChatGPT、Cursor、GitHub Copilot 和 VS Code 在内的超过 70 个智能体平台。
同一天 AAIF 宣布新增了四个托管项目，包括 Anthropic 的 Agent Skills、OpenAI 的 Open Responses、Hugging Face 的 OpenEnv 和阿里巴巴的 OpenSandbox。
这个由 Anthropic、Block 和 OpenAI 在 2025 年底共同创立的基金会，首年就凑齐了 10 个核心托管项目。
很多开发者看到基金会接管，第一反应是担心本地配置失效。
但仔细看 OrcaRouter 10 月 23 日梳理的清单，移交的四项具名资产仅限于名称、标志、GitHub 组织和 agentskills.io 域名。
它附带了两名具名首席维护者，以及三项提议的路线图项目。
对于一线写代码的人来说，技能的加载方式、文件夹格式和运行界面都没变。
我们无需加入任何组织，就能继续使用该标准。
真正值得一人公司立刻动手的，是趁着底层规范中立化，把大厂验证过的工业级 Skill 装进自己的工作流。
一人公司的出海产品经不起全量扫描
我日常写代码的主力环境是 Codex 搭配 Claude Code。
手里除了维护字流，还在跑美国公司实体 Nextfield Labs LLC 旗下的海外产品 Kirkify，以及网站生成器项目 ShipSite。
2025 年 9 月底正式从大厂离职全职创业后，我越来越清楚一件事。
做出产品不等于拥有生意。
守住生意的底线，是安全边际与成本控制。
独立开发者做商业化交付时，往往把精力全扑在功能迭代上，或者忙着打通 Stripe 和 PayPal 的收付款。
代码库里的安全隐患，全靠肉眼顺手看一眼。
等到产品真的有了海外付费用户，哪怕收到 1000 个人的付款，我也经常会去想这 1000 个数字背后的真实画面。
我猜他可能是在某个大雨天的深夜，坐在十几平米的出租屋里。
刚用我的工具跑完一批出海网页，正准备绑定信用卡开通订阅。
如果这时候鉴权接口漏做了一层租户隔离，或者 Webhook 签名校验写得有漏洞，导致他的配置数据和账单信息暴露。
这丢掉的不是几行代码，是用户对你的全盘信任。
那问题来了，小团队该怎么办？
人工审计没有精力，请第三方安全团队做渗透测试又超出了预算。
把自动化安全审计塞进日常使用的编码智能体，成了顺理成章的解法。
这正是 Cloudflare 开源的 cloudflare/security-audit-skill 近期冲上热榜的原因。
说到这个，很多资讯号这两天把它说成是刚开源的新项目。
fireup.pro 10 月 21 日的报道里写得很清楚，Cloudflare 早在 2026 年 6 月就随博客文章开源了这个技能。
它采用 MIT 许可证免费供商业使用，并在 2026 年 9 月重构了工作流、结果格式和验证器。
这个最初只有约 450 行的代码，在 Cloudflare 内部大约六周内，发展成了持续扫描其 128 个代码库的漏洞发现框架。
虽然完整的集群级框架尚未公开。
到 10 月初它的 GitHub 星标数为 25.6k，10 月 20 日突破 33,000 星。
截至 10 月 22 日已经达到 37,000 星，以每日 2,000 星的速度增长，位列全球趋势榜第 2 名。
但在你跟风把它全盘塞进自己的代码库之前，我必须先给出一句限定性前置声明。
explainx.ai 在 10 月 7 日拆解过，Cloudflare 官方至今未公布该技能的准确率、误报率以及单次运行的 Token 成本。
官方也没说明哪些具体模型表现最佳，仅提及在发现和验证阶段会切换使用不同模型。
把大厂扫 128 个仓库的重型武器原封不动搬进一人公司的项目里，你首先面对的不是抓出多少漏洞。
你面对的是实打实的算账问题。
六阶段工作流到底贵在哪
为什么我说无脑全量运行会烧穿预算？
这得看 security-audit-skill 在 2026 年 9 月重构后的底层架构。
它采用了与具体智能体无关的编写方式。
只要你的编码智能体支持工具调用和并行子智能体，比如我重度使用的 Claude Code，就能直接驱动它。
它自带的两个零依赖验证器，本地有 Node.js 环境就能跑起来。
这套技能在运行时，被严格划分为 6 个阶段的工作流。
包括侦察、覆盖率驱动的猎捕、候选验证、结构化输出，以及独立记录验证等环节。
它的审计排查面铺得非常宽，一口气涵盖了 8 个大的安全维度。
- Web 与认证协议（Web and authentication protocols）
- 客户端代码（Client-side code）
- 云与部署（Cloud and deployment）
- 供应链与 CI（Supply chain and CI）
- RPC 与消息传递（RPC and messaging）
- 租户间数据隔离（Inter-tenant data isolation）
- 原生与移动应用（Native and mobile apps）
- 专门针对 AI 和 LLM 特性的文件（AI and LLM-specific files）
我们可以算一笔细账。
假设你要对我那套 3 个月内写完 27 万行代码、近 3000 次提交的项目做一次全量扫描。
或者对字流这种多端分发系统做全盘排查。
当 Claude Code 唤醒并行子智能体去跑「覆盖率驱动的猎捕」阶段时。
每一个子智能体都需要拉取对应模块的上下文。
它们要对照 8 个维度的规则文件做多轮推理。
生成候选漏洞后，还要交给下一阶段的验证器去跑二次验证。
那问题出在哪？
子智能体会在根本没有移动端代码的 Web 项目里，反复检索原生应用配置。
模型会把大量静态样式表和排版代码塞进上下文，去比对 RPC 消息传递漏洞。
候选验证阶段为了一个低风险的依赖项告警，还会连续触发多轮不同模型的交叉复核。
这些消耗不仅琐碎，而且极度昂贵。
工具支持全自动，不等于我们必须全盘运行。
比起工具能覆盖多广的边界，我更在意你的业务交付真正暴露在哪条路径上。
这也是我一直认同的底层逻辑。
正如稻盛和夫所说，销售最大化，经费最小化。
在一人公司的代码流水线里，Token 就是你的直接生产经费。
任何不产生实际安全边际的上下文吞吐，都是纯损耗。
针对字流架构的按需裁剪实操
这是我自己摸索出来的接入与裁剪思路。
不一定适合所有规模的团队。
如果你也是用 Claude Code 维护出海产品或独立项目，可以按下面的步骤在本地做测试。
我们先确认本地环境。
Agent Skills 的文件夹格式与加载界面在捐赠给 AAIF 后完全保持原样。
security-audit-skill 的两个零依赖验证器只需要 Node.js。
接入前只要确保本地 Node 环境就绪，把技能仓库拉取到项目的技能目录即可。
你可以这样在终端里初始化环境，查看它的规则文件结构。
# 1. 确认本地 Node.js 环境（用于运行两个零依赖验证器）
node -v
# 2. 克隆 Cloudflare 的 MIT 开源审计技能到本地目录
git clone https://github.com/cloudflare/security-audit-skill.git .claude/skills/security-audit
# 3. 查看技能目录下的规则分类与验证器脚本
ls -la .claude/skills/security-audit
拉下代码后，千万别让 Claude Code 对着根目录执行全套六阶段审计。
以我们的产品字流以及出海项目为例，系统的核心架构非常明确。
前端是内容编辑与多平台分发界面。
后端处理用户鉴权、租户隔离、AI 提示词调度以及海外支付回调。
底层的 AI Agent 架构，则采用远程 Linux 服务器部署 Hermes，本地通过 Windows 协同调度。
你想想看，在这个架构里，我们根本没有原生与移动应用代码。
系统里也不涉及复杂的内部重型 RPC 集群。
为了让每次代码提交后的安全排查可控、可算账，我梳理了一套排他的审计阶段状态代码。
你可以把这套准入准出标准写进项目的 CLAUDE.md 或自定义指令里。
以此强制编码智能体按状态推进，严禁脑补扩权。
# 安全审计执行规范（写入 CLAUDE.md）
当你调用 `security-audit-skill` 进行安全排查时，必须严格遵守以下四个排他状态码的准入与准出标准，未满足准入条件严禁触发下一阶段：
- [STATE_01: RECON_SCOPED]（限定边界侦察）
- 准入标准：仅当 `git diff` 涉及鉴权、支付回调、租户数据查询或 LLM 提示词拼接目录时触发。
- 执行边界：仅加载「Web 与认证协议」「租户间数据隔离」「AI 和 LLM 特性」三类规则文件；显式忽略移动端、原生应用及静态排版模块。
- 准出标准：输出受影响的核心文件清单（上限 15 个文件），若无命中则直接终止流程，不进入猎捕阶段。
- [STATE_02: HUNT_TARGETED]（定向漏洞猎捕）
- 准入标准：已获取 `STATE_01` 输出的受影响文件清单。
- 执行边界：并发子智能体数量上限设为 2，仅针对清单内文件执行静态路径追踪。
- 准出标准：生成带有具体文件行号与触发路径的候选漏洞列表（Candidate List）。
- [STATE_03: VERIFY_LOCAL]（本地零依赖验证）
- 准入标准：候选列表中存在高危或中危级别的权限绕过、注入或跨租户越权项。
- 执行边界：调用技能内置的 Node.js 零依赖验证器对结构化结果与记录进行校验，剔除无法构造实际调用链的理论误报。
- 准出标准：通过 Node.js 验证器校验并生成标准化的结构化输出报告。
- [STATE_04: AUDIT_ABORT]（熔断与豁免）
- 准入标准：单次审计累计上下文读取超过设定阈值，或仅修改纯前端 UI / 飞书转 Markdown 排版逻辑。
- 执行动作：立即挂起子智能体并退出审计工作流，记录跳过原因。
通过这种状态机式的硬性约束，你就可以在 Claude Code 里用明确的指令控制扫描粒度。
比如当我们更新了用户收件箱的私信处理逻辑，或者海外订阅状态同步接口。
我们只针对特定模块拉起审计，并直接运行本地的 Node.js 验证器检查结果格式。
# 在 Claude Code 交互终端中指定范围执行，不跑全仓扫描
claude "请加载 .claude/skills/security-audit，严格按照 CLAUDE.md 中的 [STATE_01] 至 [STATE_03] 标准，仅对 src/auth 和 src/llm-agent 目录执行安全审计，并用内置 Node.js 验证器校验输出结果。"
【待补：在字流或海外项目指定目录运行裁剪版 security-audit-skill 后的终端输出截图与单次 Token 消耗数字】
把这套工具链拆解清楚后，到底谁适合接入？
我的判断完全取决于你的代码暴露面，以及当前的商业交付阶段。
如果你有真实的海外收付款与多租户逻辑，建议立刻接入。
只要系统里接了 Stripe 或 PayPal 的 Webhook，或者存了不同用户的配置数据。
「Web 与认证协议」和「租户间数据隔离」的定向排查，能帮你拦住越权访问的低级错误。
重度集成 LLM 能力的商业应用，也推荐接入。
如果你的产品包含复杂的提示词拼接、外部工具调用，或者用远程 Linux 部署 Hermes 做 Agent 调度。
针对「AI 和 LLM 特性」的规则，能帮你提前排查提示词注入与未授权工具执行风险。
高频迭代的 AI 协同开发项目，同样适用。
当你用 Codex 和 Claude Code 每天高频生成代码时，AI 经常能跑通功能，却漏掉边界校验。
在关键提交节点做一次定向复核，比人工一行行看 diff 稳妥得多。
但纯静态站点、本地脚本或纯排版工具，我不建议跟风安装。
比如我之前开源的那款飞书转 Markdown 排版工具，不涉及多租户数据库隔离和复杂的服务端鉴权。
硬跑一套六阶段企业级安全审计，纯属浪费 Token。
习惯不对工作流做任何裁剪、指望一键全自动的团队，也最好别碰。
如果你不打算根据业务删减那 8 个维度，也不限制子智能体的扫描目录。
在官方没有给出成本基线和误报率指标的前提下，全量扫描带来的账单惊吓，大概率会超过它找出的有效问题。
我已经毫无保留地把这套评估标准和配置逻辑拆解出来了，有用就拿走。
回到刚才那个问题，今天 AI 编程领域的演进轨迹，和二十多年前开源基础设施的过程高度重合。
当年大厂把私有组件捐出来变成中立标准，并不是为了让小团队把所有内核模块全开一遍。
那是为了降低工具的迁移成本。
Anthropic 把安装量近 2.8 亿次的 Agent Skills 捐给 AAIF。
Cloudflare 把 3.7 万星的安全审计能力做成通用的 MIT 开源技能。
本质上，都是把过去大厂安全部门才用得起的武器变成了公共积木。
积木越是唾手可得，掌舵人就越要保持克制。
全年 1.7 万元的 AI 订阅成本看似不高。
但它是靠每一次克制的调用、每一次对无效上下文的减法省出来的。
下一阶段，我依然会专注在「ShipSolo AI 编程出海领航计划」（shipsolo.cn）与自身出海产品的商业闭环打磨上。
持续把这些带着真实账单与安全边际的工程实践沉淀下来。
今天你可以直接打开自己项目的代码库。
对照上面那 8 个排查维度，删掉与你业务无关的规则文件。
只挑出最核心的鉴权或支付目录，跑一次定向测试。
如果这篇实操拆解帮你省下了不必要的测试额度，欢迎点赞收藏。
关于 Claude Code 的成本控制与出海项目的安全避坑，如果你有更好的裁剪方案，或者想获取我整理的出海避坑与防封实操清单，我们在评论区交流。
👋 我是孟健，前腾讯 T11、前字节技术 Leader，现在全职做 AI 编程。
🔥 更多 AI 编程实战：
- GitHub：@mengjian-github
- 专栏：AI编程实战
觉得有用？点赞+收藏 就是最大支持 🙏
本文由作者借助 AI 辅助整理，内容经作者实测与审校。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
The multi-platform content distribution tool that I maintain has generated 192 code submissions over the past year.
Over 1,300 comments and private letters were included in the inbox.
The AI tool that supports all of this, the year-round subscription cost about $17,000.
If you don't cut it in Claude Code, you'll run a six-stage security audit at the enterprise level, Skill.
The Token bill, which is consumed by AI agent once and in one instance, will go straight through the budget line of one company.
This is not about the grand narrative of the founding ecology.
I'm just dismantling how to access Claude Code's open source security-audit-skill.
workflow is also tailored to the core exposure of offshore products to avoid uncontrolled consumption of quota.
There's been a landmark incident in the tech circle these two days.
Anthropic announced on October 22, 2026 that open standards Agent Skills would be donated to AAF under the umbrella of the Linux Foundation.
FourWeekMBA reported on October 23 that 1 million skills had been acquired by publish and that the cumulative number of installations was nearly 280 million.
It supports over 70 AI agent platforms, including ChatGPT, Cursor, GitHub Copilot and VS Code.
On the same day, AAIF announced the addition of four additional hosting items, including Agent Skills in Anthropic, Open Reponses in Open AI, OpenEnv in Hugging Face and OpenSandbox in Alibaba.
This foundation, created jointly by Anthropic, Block and Open AI at the end of 2025, brought together 10 core hosting projects in its first year.
Many developers saw the Foundation take over, and the first response was to fear that the local configuration would fail.
However, looking closely at the OrcaRouter combo list of 23 October, the four assets handed over were limited to names, symbols, GitHub organization and agentskills.io domain names.
It was accompanied by two chief defenders and three proposed road map projects.
For people who write first-line codes, the way the skills are loaded, the format of the folder and the interface run have not changed.
We can continue to use the standard without joining any organization.
What really deserves one company to do is to take advantage of the neutralization of bottom-level norms by loading the industrial Skill certified by a large plant into its own workflow.
One company can't afford full scanning of its products.
My day-to-day code environment is Codex with Claude Code.
In addition to maintaining the flow of words, they are running overseas products under the umbrella of the American corporate entity Nextfield Labs LLC, Kirkify, and the web generator project ShipSite.
I became more aware of one thing after I officially left full-time entrepreneurship at the end of September.
Making a product does not mean having a business.
Maintaining the bottom line of business is security margin and cost control.
When independent developers make commercial deliveries, they tend to concentrate on functional overlaps or are busy getting through payments from Stripe and PayPal.
The security risks in the code vault depend on the naked eye.
And when the product actually has paid users overseas, even if I get 1,000 people to pay, I often think about the real picture behind the 1,000 numbers.
I guess he's probably sitting in a ten square meters rental room in the middle of a rainy night.
I just ran a bunch of offshore pages with my tools and I'm about to book a credit card to open a subscription.
If, at this point in time, the security interface fails to isolate the tenant, or if there is a gap in the verification of the Webhook signature, it exposes his configuration data and billing information.
It's not a few lines of code that's lost. It's the whole user trust in you.
The question is, what about the little team?
Manual audits were not robust, and the third-party security team was asked to conduct infiltration tests, which were over-budgeted.
The introduction of automated security audits into the daily code AI agent became a logical solution.
This is why Cloudflare/security-udit-skill recently hit the hot list.
Speaking of which, many of the information numbers describe it as a new project that has just opened up.
The story of Fireup.pro, 21 October, is clear: Cloudflare started with blog articles as early as June 2026.
It used MIT licences for commercial use free of charge, and in September 2026 it reconstituted workflow, the results format and the certifier.
This code, which initially had only about 450 lines, developed into a gap detection framework for the continuous scanning of its 128 cod libraries in about six weeks inside Cloudflare.
Although the complete cluster-level framework has not yet been made public.
By the beginning of October, its GitHub marker was 25.6 k, and on October 20, it broke 33,000.
By October 22, it had reached 37,000 stars, growing at 2,000 stars a day, ranking second in the global trend list.
But before you and the wind put it all into your own code library, I must give you a qualified pre-declaration.
Explainx.ai, which was dismantled on October 7, Cloudflare has not yet published the accuracy, misreporting and single-run Token costs of the skills.
Nor did the official state state which specific models performed best, referring only to the replacement of different models at the detection and validation stages.
You're not going to be able to pick up a lot of holes in the projects of one person's company, cleaning up 128 warehouses.
You're facing a real problem.
Where exactly is Phase Six workflow?
Why do I say full brainless runs burn through the budget?
This depends on the bottom structure that was rebuilt in September 2026.
It was prepared in a manner unrelated to the specific AI agent.
As long as your code AI agent supports tool call and the parallel sub-AI agent, like Claude Code, which I use heavily, you can drive it directly.
It carries its own two zero-dependant certifiers, and has a Node.js environment where it can run.
This skill set, when running, is strictly divided into six stages of workflow.
This includes detection, coverage-driven hunting, candidate validation, structured output and independent record validation.
Its audit line is very broad, covering eight major security dimensions.
- Web and authentication protocols
- Client-side code
- Cloud and deployment
- Supply chain and CI
- RPC and messaging
- Inter-tenant data segregation
- Native and mobile applications
- Files specific to AI and LLM features (AI and LLM-specific files)
We can count the details.
Suppose you're going to do a full scan of my 270,000 line codes, nearly 3000 submissions within three months.
Alternatively, the multiple-end distribution system of word stream is thoroughly checked.
When Claude Code awakened the parallel AI agent to the "coverage-driven hunting" phase,
Each sub-item AI agent requires the extraction of the corresponding module context.
They do multi-wheel reasoning against a rule document of 8 dimensions.
Once a candidate loophole has been created, the next stage of the certification will also be handed over to the certification officer for a secondary validation.
So what's the problem?
Sub-AI agent will repeat the original application configuration in a Web project without a moving end code.
The model places a large number of static stylesheets and layout codes into context to compare the transmission gap of the RPC message.
The candidate certification phase is a low-risk, dependent call and triggers successive rounds of cross-review of different models.
Such consumption is not only trivial but also extremely expensive.
Tool support is fully automatic and does not mean we have to run it all.
I care more about the path on which your business delivery is really exposed than about the breadth of the borders that tools cover.
That's the bottom logic I've always shared.
As the rice man and husband have said, sales are maximized and funding minimized.
In one company's code stream, Token is your direct production money.
Any context swallowing that does not produce a physical security margin is pure wear and tear.
A needs-based cut for the flow structure
It's the access and tailoring that I found myself.
Not necessarily for teams of all sizes.
If you're also using Claude Code to maintain offshore products or stand-alone projects, you can do the following local tests.
We first confirm the local environment.
The folder format and loading interface for Agent Skills remain the same after donation to AAIF.
Two zero-dependent certifiers only need Node.js.
Prior to access, just ensure that the local Node environment is ready and that the skills warehouse is drawn to the skills catalogue of the project.
So you can initialize the environment in the terminal and look at its rule file structure.
1. Confirm local Node.js environment (for running two zero-dependent certifiers)
node -v
# 2. MIT open-source audit skills for cloning Cloudflare to the local directory
git clone https://github.com/cloudflare/security-audit-skill.git .claude/skills/security-audit
# 3. View rule classification and certifier scripts under skills catalogue
ls -la .claude/skills/security-audit
When the code is pulled, please don't let Claude Code perform a six-stage audit of the root directory.
The core architecture of the system is very clear, for example, in terms of the flow of our products and the offshore project.
The front end is the content editing and multiplatform distribution interface.
Back-end processing of user recognition, tenant segregation, AI prompt movement and overseas payback.
At the bottom of the AIAgent architecture, Hermes is deployed using a remote Linux server, which is locally coordinated through Windows.
You see, in this architecture, we don't have any primary and mobile applications.
Nor does it involve complex internal heavy RPC clusters.
In order to allow security clearance and accounting after each code has been submitted, I have combed an exclusive audit phase status code.
You can write this access to the CLUDE.md project or a custom command.
This is a mandatory code AI agent, which advances in a state-of-the-art manner and prohibits brain reinforcement.
# Security Audit Implementation Standard (in CLAUDE.md)
When you call the `security-audit-skill ' for security clearance, the following four exclusionary status code entry and exit criteria must be strictly adhered to, and failure to meet access requirements triggers the next phase:
- [STATE 01: RECON SCOPED]
- Access criteria: Only when `git diff ' relates to rights of attorney, payment recall, tenant data search or LLM prompt patch catalogue.
- Enforcement of the boundary: only the "Web" and "Certification Agreement" "Segregation of data between tenants" "AI and LLM characteristics" rule files are loaded; visible neglect of moving ends, primary applications and static layout modules is ignored.
- Criteria for clearance: Output of a list of core documents affected (up to a maximum of 15 documents) and direct termination of the process in case of failure, without entering the hunting phase.
- [STATE 02: HUNT TARGETED]
- Access criteria: A list of affected documents from `STATE 01 ' has been obtained.
- Implementation of the boundary: a maximum number of AI agentes is set at 2 and static path tracking is carried out only for the documents in the list.
- Acquiring criteria: Generate a list of candidate holes (Candidate List) with specific file line numbers and trigger paths.
- [STATE 03: VERIFY LOCAL]
- Access criteria: permission, which is high or medium-risk, bypasses, injects or cross-tenant power items in the list of candidates.
- Implementation of the boundary: Node.js zero relys on the skills embedded in the caller to verify the results and records of the structuralization and remove the theoretical misstatement of the inability to construct the actual call chain.
- Identification criteria: Verify and generate standardized structured output reports through the Node.js certifier.
- [STATE 04: AUDT ABORT]
- Access criteria: The cumulative context reading of a single audit exceeds the set threshold or changes the purely front-end UI/ Flying Book to Markdown layout logic.
- Execute action: immediately hang the AI agent and withdraw from the audit of workflow, recording the cause of the skip.
With this state-of-the-art rigidity, you can control the scanned particles with clear instructions in Claude Code.
For example, when we update the private mail processing logic of user inboxes, or the Subscription Status Sync interface overseas.
We pull up audits only for specific modules and directly run the local Node.js check results format.
# Specify range in Claude Code interactive terminal, not full-scan
claude " Please load.claude/skills/security-udit, strictly in accordance with [STATE 01] to [STATE 03] standards in CLUDE.md, perform security audits only for src/auth and src/llm-agent directories and verify the output with the built-in Node.js certifier."
[To be completed: terminal clipping and single Token consumption figures after running the tailoring of the script or overseas project specified directory]
Who's going to be able to access this?
My judgment depends entirely on your code exposure and on the current stage of commercial delivery.
If you have a real overseas payment and multi-tenant logic, access is recommended immediately.
As long as the system is connected to Stripe or PayPal's Webbook, or different user profiles are stored.
"Web and the Certification Agreement" and "Segregation of data between tenants" are targeted checks that can help you stop the low-level errors of over-censorship.
Commercial applications of heavy integration LLM capabilities are also recommended for access.
If your product consists of a complex prompt coupling, an external tool call, or a remote Linux deployment of Hermes for the Agent movement.
The rules for "AI and LLM characteristics" help you pre-screen the risk of prompt being injected into unauthorized tools for implementation.
HF iterative AI co-development project, also applicable.
When you generate code with Codex and Claude Code, AI often runs through the functionality, but leaves the border check.
A targeted review at the key submission nodes is much more secure than a manual line of diff.
However, pure static sites, local scripts or pure layout tools are not recommended for installation.
For example, the flying book to the Markdown layout tool that I used to open up didn't involve multiple tenant database isolation and complex service-end assurance.
A six-stage enterprise-level security audit was a waste of time, Token.
It's a habit not to cut any workflow, to expect a single-key team, and not to touch it.
If you do not intend to delete that 8 dimensions according to business, there is no limit to the scanning directory of sub-AI agent.
In the absence of official cost baselines and misreporting targets, the full-scale scan would shock the billing and probably exceed the effective problems it identifies.
I have unreservedly disassembled this set of assessment criteria and configuration logic and taken them as useful as possible.
Back to that question, today the evolution trajectory of the AI programming field is highly resonated with the process of open source infrastructure more than 20 years ago.
The donation of privately owned components to neutral standards was not intended to allow small teams to open all kernel modules.
That's to reduce the cost of moving the tools.
Anthropic donated the installation of Agent Skills nearly 280 million times to AAIF.
Cloudflare uses 37,000 stars as a common MIT open-source skill.
In essence, weapons that were affordable to the security services of large factories in the past were turned into public blocks.
The more the building blocks are available, the more restraint the rudder will have to exercise.
A $17,000 AI subscription cost for the whole year does not seem high.
But it came out with every call of restraint, every reduction of ineffective context.
In the next phase, I will continue to focus on grinding the "ShipSolo AI Program" (shipsolo.cn) and commercial closed loops of my own offshore products.
These work practices with real bills and security margins continue to sink.
Today you can just open your project code library.
By contrast with the eight Zachvidos above, delete the rule files that have nothing to do with your business.
Select only the core AC or payment catalogue and run a direct test.
If this detachment saved you the unnecessary test quota, welcome to the collection.
With regard to the cost control of Claude Code and safe pits for offshore projects, if you have a better tailoring programme, or if you want to get a list of the pits that I've compiled, we'll talk in the comment area.
I'm Meng Ken, ex-Tencent T11, ex-byte technology Leader, now full time doing AI programming.
🔥 More AI programming field:
- GitHub：@mengjian-github
- Column: AI programming field
You think it works? It's the greatest support for you.
The text is organized by the author with the help of AI, and is measured and revised by the author.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.