Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add telagod/code-abyss --skill defending-applicationsgit clone --depth 1 https://github.com/telagod/code-abyssWrote 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/skills/telagod/code-abyss/defending-applications)<a href="https://agentmods.dev/skills/telagod/code-abyss/defending-applications"><img src="https://agentmods.dev/badge/skills/telagod/code-abyss/defending-applications/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/skills/telagod/code-abyss/defending-applications"><img src="https://agentmods.dev/badge/skills/telagod/code-abyss/defending-applications.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00152 | $0.01313 |
| Opus 5.5 | $0.00061 | $0.00525 |
| Sonnet 5.5 | $0.00030 | $0.00263 |
| Haiku 4.5 | $0.00015 | $0.00131 |
Grade A, and why
defending-applications 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 1mo 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
应用安全防御 · 镇魔甲
判断先于执行：决定「是否做 / 选什么 / 如何取舍」（栈、方案、架构、权衡）前，先读领域判断内核
skills/_kernel/security/SKILL.md——它管 judgment，本秘典管 execution；冲突时以内核判断为准。
开发侧防御秘典：站在 builder 一侧，把漏洞挡在合并前。 不教渗透，只教"如何写出杀不动的代码"和"如何把已生漏洞最小代价闭环"。 信级：项目源码/lock 文件 > 框架官方安全文档 > CVE/CWE > 训练记忆（标
[unverified]）。
路由
| 意图 | 加载 | 触发词 |
|---|---|---|
| Web/API/GraphQL 漏洞防御 | web-api-appsec | XSS, SQLi, SSRF, BOLA, IDOR, Mass Assignment, GraphQL, 反序列化, 文件上传, 路径遍历, XXE, OWASP |
| 认证授权与会话 | oauth-and-sessions | OAuth, OIDC, JWT, SSO, SAML, Session, Cookie, PKCE, refresh token, kid 注入 |
| LLM 应用安全 | llm-appsec | Prompt 注入, 越狱, 越权调用, RAG 投毒, agent tool 滥用, 输出过滤, jailbreak |
何时使用
| 场景 | 使用本 skill | 使用其他 |
|---|---|---|
| review 自家代码、修 CVE、设计鉴权 | ✅ 本 skill | — |
| 写 SAST 规则、Semgrep 模式 | ✅ 本 skill + securing-systems/code-audit | — |
| 设计 LLM 应用的 guardrail | ✅ llm-appsec | + building-agent-systems/llm-security |
| 红队渗透、写 PoC 攻击目标 | ❌ | securing-systems/pentest |
| 处理已发生的入侵、日志取证 | ❌ | securing-systems/blue-team |
| 容器/K8s/CI 加固 | ❌ | provisioning-infrastructure |
| 设计零信任、身份架构 | 部分（OAuth/SSO 内） | architecting-security |
联动
- securing-systems/code-audit：本 skill 给"怎么修"，code-audit 给"怎么找"。
- securing-systems/pentest：攻方视角验证本 skill 的修复是否真正闭环。
- analyzing-security：自动化扫描胶水；本 skill 提供规则语义。
- building-agent-systems/llm-security：agent 内部细节；本 skill 关注应用边界。
- designing-architectures：威胁建模、API 设计；本 skill 关注落地代码。
执行链
威胁建模 → 入口梳理 → source→sink 追踪 → 防御层选型 → 最小修复 → 回归测试 → 检测信号埋点
每个漏洞的修复必须回答三问：
- 入口在哪：用户可控数据从哪进来（route/header/cookie/body/file/upstream API/RAG doc）。
- 信任边界在哪：何处由"untrusted"变"trusted"，是否有显式校验。
- 检测信号是什么：修复后若被绕过，哪条日志/指标会响。
铁律
- 服务端为真：前端校验只是 UX，所有 authz/sanitize 必须在服务端再做一次。客户端代码即攻击面。
- deny by default：authz、CORS、CSP、SQL where 子句、文件类型——所有名单皆 allowlist 优先，blacklist 是反模式。
- 不信任 ID：URL/body 里的 object_id 永远先做 ownership 校验，再做业务逻辑。BOLA 是 API 漏洞之王。
- 可观测优先于完美防御：每个高风险路径必须有结构化日志 + 速率/异常告警，被绕过时能 5 分钟内发现。
- 修复要带回归：每个漏洞修复必须配 1 个攻击用例测试 + 1 个正常用例测试，否则六个月后必复发。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 67 lines · 152 tokens per session scan A 69760465a28f
defending-applications is a skill published in the GitHub repository telagod/code-abyss (243 stars, last pushed 3mo ago), licensed MIT. It adds 152 tokens to every session and 1,313 once invoked, about $0.0006 per session on Opus 5.5. 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.
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