CyberStrikeAI is a security operations workspace that turns natural-language plans into governed, auditable actions while recording evidence and results for later reuse. Authorized security teams use it to manage agents, tools, vulnerabilities, knowledge, and attack-chain analysis. Catalogue add-ons provide agent and skill workflows for working with the platform.
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 Ed1s0nZ/CyberStrikeAI --skill pentest-blackboardgit clone --depth 1 https://github.com/Ed1s0nZ/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/skills/ed1s0nz/cyberstrikeai/pentest-blackboard)<a href="https://agentmods.dev/skills/ed1s0nz/cyberstrikeai/pentest-blackboard"><img src="https://agentmods.dev/badge/skills/ed1s0nz/cyberstrikeai/pentest-blackboard/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/ed1s0nz/cyberstrikeai/pentest-blackboard"><img src="https://agentmods.dev/badge/skills/ed1s0nz/cyberstrikeai/pentest-blackboard.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 | $0.00073 | $0.02144 |
| Opus 5 | $0.00036 | $0.01072 |
| Sonnet 5 | $0.00015 | $0.00429 |
| Haiku 4.5 | $0.00007 | $0.00214 |
Grade A, and why
pentest-blackboard scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. **识别→搜**：识别任何组件/框架/版本/中间件 → 暂停利用 → 立即执行 `component-vuln-intel` 全部命令（browser_navigate+terminal curl，7 个步骤全做）→ 结果用 `tentative` Fact 或本轮计划跟踪 → 搜完才继续。不搜就说「无已知漏洞」=幻觉；**验证后**再 `confirmed` / `record_vulnerability`。 How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
项目黑板（与本产品对齐）
状态落在已绑定项目的 SQLite 黑板，不依赖对话上下文，也不使用
.pentest/目录。 系统自动注入「项目黑板索引」（仅fact_key+ summary）；摘要不足必须get_project_fact，禁止凭摘要臆造细节。
原语与分工（产品模型）
| 概念 | 产品落点 | 规则 |
|---|---|---|
| Fact | project_facts（upsert_project_fact） |
同 fact_key 覆盖更新；非正式漏洞条目。confidence: confirmed | tentative | deprecated |
| 关系边 | project_fact_edges（upsert 的 links） |
结构化攻击图；finding/chain/exploit/poc 必须带 links |
| 可交付漏洞 | record_vulnerability |
与 Fact 可各记一次：Fact=复现上下文，漏洞=正式 finding |
| 探索方向 | 本轮计划 / 协调者委派 / plantask（若启用） | 不是独立 Intent 表；待验证方向用 confidence=tentative 的 note/ 或先不落库、验证后再写 |
| 人类注入 | 用户消息 / HITL 审批 | 直接吸收进决策；不必写成 Hint 原语 |
Fact vs 漏洞
- 环境/目标/认证等认知 → 只写 Fact（
target/auth/infra/business/） - 发现与利用上下文 → Fact（
finding/chain/exploit/poc/）+ body 填满攻击链 - 可交付 findings →
record_vulnerability（标题、严重程度、类型、目标、POC、影响、修复）；记前可用list_vulnerabilities查重
强制节奏：边渗透边记录
勿等会话结束再批量写入。
- 每确认一条新认知（开放端口/服务版本、入口路径、认证态或凭据特征、可利用点或攻击面变化）→ 立即
upsert_project_fact（同 key 覆盖）。 - 每验证出一条可复现漏洞（含 POC/影响）→ 立即
record_vulnerability；与事实可各记一次。 - 继续下一步前优先落库，避免上下文压缩丢细节。
- 未绑项目：说明无法写黑板，仍在本轮保留证据摘要。
- 协调者：子任务返回新认知/漏洞时由协调者写入，勿假定子代理已记。
- 子代理无工具时：交付物末尾给「待落库」条目（建议
fact_key、summary、body/POC 要点），供协调者立即写入。
工具速查
| 工具 | 用途 |
|---|---|
upsert_project_fact |
写入/更新事实（含 body、confidence、links） |
get_project_fact |
按 key 取完整 body（索引不够时必调） |
list_project_facts / search_project_facts |
检索黑板 |
deprecate_project_fact / restore_project_fact |
误报废弃 / 恢复 |
record_vulnerability |
可交付漏洞 |
list_vulnerabilities / get_vulnerability |
查重与详情 |
前置：当前对话已绑定项目（否则工具报错）。
写入规范
fact_key / category
- 格式：小写
category/slug（如target/primary_domain、finding/sqli-login） - 环境：
target|auth|infra|business - 发现利用：
finding|chain|exploit|poc（另可用note） - 同一发现保持同一
fact_key覆盖，勿拆成多个 key 导致上下文丢失
summary / body / confidence
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.
- 11d ago First seen · 112 lines · 73 tokens per session scan A d1927dbf6a60
pentest-blackboard is a skill published in the GitHub repository Ed1s0nZ/CyberStrikeAI (6,410 stars, last pushed 16d ago), licensed Apache-2.0. It adds 73 tokens to every session and 2,144 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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