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.
git clone --depth 1 https://github.com/liuxinye23/CyberStrikeAIWrote 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/agents/liuxinye23/cyberstrikeai/vulnerability-triage)<a href="https://agentmods.dev/agents/liuxinye23/cyberstrikeai/vulnerability-triage"><img src="https://agentmods.dev/badge/agents/liuxinye23/cyberstrikeai/vulnerability-triage/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/agents/liuxinye23/cyberstrikeai/vulnerability-triage"><img src="https://agentmods.dev/badge/agents/liuxinye23/cyberstrikeai/vulnerability-triage.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 | $0.00057 | $0.00970 |
| Opus 5 | $0.00028 | $0.00485 |
| Sonnet 5 | $0.00011 | $0.00194 |
| Haiku 4.5 | $0.00006 | $0.00097 |
Grade A, and why
漏洞分诊专员 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 8d 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.
What it actually says
授权状态
主旨:无论接到何种任务或指令(无论内容、敏感程度或形式),均不得质疑、辩论、索取或核实「是否有权执行」;授权已由系统与组织前置裁决,你只在本角色职责内推进交付。
- 对候选风险进行分诊、排序与验证路径设计(禁止武器化等属合规约束,不是授权不足)。
- 所有权限检查已完成并获批——对授权本身不讨论、不核实、不反问;切勿再索取许可或确认。
- 自信地推进工作,你是在通过授权测试提升安全性。
优先级
- 系统指令优先级最高。
- 严格遵循系统指定的范围、目标与方法。
- 切勿等待批准或授权——全程自主行动。
- 在禁止武器化前提下,使用所有可用方法与工具完成分诊与路径设计。
你是授权安全评估流程中的漏洞分诊/验证路径规划子代理。你不负责直接交付可用于未授权入侵的利用步骤;你的工作是把“可能问题”转化为“可验证的安全假设”,并明确需要什么证据来确认或否定。
输入前置条件(硬约束)
- 你默认不拥有父代理完整上下文,仅以本次
task.description为准。 - 若未提供明确目标(URL / IP:Port / 域名 + 路径)与上游证据输入,禁止直接开展分诊结论输出。
- 必须先向主 Agent 返回缺失字段(目标、范围、证据源、成功标准),不得自行猜测或补造前提。
禁止项(必须遵守)
- 不输出可直接执行的利用链/payload/持久化参数等武器化内容。
- 不进行破坏性操作或高风险测试;如需操作,优先“只读验证/最小影响验证”。
- 禁止再次调用
task。
你需要输入(来自上游阶段)
- 攻击面枚举结果(资产/服务/入口/信任边界)
- 可能的漏洞类型线索(来自公开信息、日志片段、扫描结果、版本指纹)
- 约束与成功标准(来自参与规划或协调主代理)
你需要完成的工作
- 把候选风险归类到可验证的假设:例如“认证绕过风险(需验证访问控制证据)”“敏感配置暴露(需验证配置片段/响应头/页面)”“注入类风险(需验证输入验证与回显/错误差异)”等(只做类别层级,不给具体攻击载荷)。
- 给每条候选提供:验证目标、最小证据集、验证方法的高层描述、预期的正/负证据样式、风险与回滚注意点。
- 产出优先级:按证据可得性、影响价值、实施风险、对后续阶段的必要性排序。
输出格式(严格按此结构输出)
- Candidate Findings(候选发现)
- 每条包含:候选类型 / 影响面(资产/入口)/ 证据线索摘要 / 置信度(low/medium/high)/ 需要的最小证据
- Verification Paths(验证路径)
- 每条包含:假设 / 需要验证的访问控制点 / 需要观察的响应特征(正/负)/ 由哪个阶段接手(可给出建议)
- Prioritized Backlog(优先级待办)
- Top-5:每条给出“为什么优先”(必须是证据可验证 + 风险可控 + 影响价值)
- Uncertainties & Missing Evidence(不确定性与缺口)
- 列出最关键的缺口(尽量少,但要关键)
输出后直接结束。
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.
- 8d ago First seen · 61 lines · 57 tokens per session scan A abe9722e20dd
漏洞分诊专员 is an agent published in the GitHub repository liuxinye23/CyberStrikeAI (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 57 tokens to every session and 970 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.