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 agentmods add commands/modelengine-group/fit-framework/analyze-dependabotgit clone --depth 1 https://github.com/ModelEngine-Group/fit-frameworkWhat 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 | $0.00019 | $0.02153 |
| Opus 5 | $0.00010 | $0.01077 |
| Sonnet 5 | $0.00004 | $0.00431 |
| Haiku 4.5 | $0.00002 | $0.00215 |
Grade A, and why
analyze-dependabot 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 2d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Dependabot Command
功能说明
分析指定的 Dependabot 安全告警,评估安全风险并创建修复任务,输出安全分析文档。
执行流程
1. 获取安全告警信息
gh api repos/{owner}/{repo}/dependabot/alerts/<alert-number>
提取关键信息:
number: 告警编号state: 状态(open/dismissed/fixed)security_advisory: 安全公告详情ghsa_id: GHSA IDcve_id: CVE ID(如果有)severity: 严重程度(critical/high/medium/low)summary: 漏洞摘要description: 详细描述vulnerabilities: 受影响的版本范围
dependency: 受影响的依赖package.name: 包名package.ecosystem: 生态系统(maven/pip/npm等)manifest_path: 依赖文件路径
security_vulnerability.first_patched_version: 首个修复版本security_vulnerability.vulnerable_version_range: 受影响版本范围
2. 创建任务目录和文件
检查是否已存在该安全告警的任务:
- 在
.ai-workspace/active/中搜索相关任务 - 如果找到,询问是否重新分析
- 如果没有,创建新任务
任务目录结构:
.ai-workspace/active/TASK-{yyyyMMdd-HHmmss}/
├── task.md ← 使用 .agents/templates/task.md 模板创建
└── analysis.md ← 本命令将创建此文件
⚠️ 重要:
- 任务目录命名:
TASK-{yyyyMMdd-HHmmss}(必须包含TASK-前缀) - 示例:
TASK-20260205-202013 - 任务ID(
{task-id})即为目录名:TASK-{yyyyMMdd-HHmmss}
任务元数据(在 task.md 的 YAML front matter 中)需包含:
id: TASK-{yyyyMMdd-HHmmss}
security_alert_number: <alert-number>
severity: <critical/high/medium/low>
cve_id: <CVE-ID> # 如果有
ghsa_id: <GHSA-ID>
3. 分析受影响范围
必须完成的分析:
- 识别受影响的依赖包和版本
- 搜索项目中使用该依赖的所有位置(使用 Grep 工具)
- 检查依赖文件(pom.xml, requirements.txt, package.json 等)
- 分析是否直接使用了漏洞代码路径
- 识别依赖关系(直接依赖 vs 传递依赖)
- 定位受影响的代码模块和文件
4. 评估安全风险
必须完成的风险评估:
- 评估漏洞的实际影响(是否可被利用)
- 分析漏洞触发条件和场景
- 评估对系统安全性的影响程度
- 识别潜在的安全威胁
- 确定修复的紧急程度
- 查找是否有已知的攻击案例
5. 输出分析文档
创建 .ai-workspace/active/{task-id}/analysis.md,必须包含以下章节:
注意:{task-id} 格式为 TASK-{yyyyMMdd-HHmmss},例如 TASK-20260205-202013
# 安全告警分析报告
## 告警基本信息
- **告警编号**: #{alert-number}
- **严重程度**: {critical/high/medium/low} 🔴/🟠/🟡/🟢
- **GHSA ID**: {ghsa-id}
- **CVE ID**: {cve-id}
- **告警状态**: {open/dismissed/fixed}
- **漏洞描述**: {描述}
## 漏洞详情
### 受影响的依赖
- **包名**: {package-name}
- **生态系统**: {maven/pip/npm/...}
- **当前版本**: {current-version}
- **受影响版本范围**: {vulnerable-range}
- **首个修复版本**: {patched-version}
### 依赖使用情况
- **依赖文件位置**: `{manifest-path}` - {说明}
- **依赖类型**: {直接依赖/传递依赖}
- **使用模块列表**:
- `{module-1}` - {说明}
- `{module-2}` - {说明}
## 影响范围评估
### 直接影响的代码
- `{file-path}:{line-number}` - {说明}
### 间接影响的功能
- {受影响的功能模块}
## 安全风险评估
### 漏洞可利用性
- [ ] 是否直接使用了漏洞代码路径?
- [ ] 是否有外部输入触发漏洞?
- [ ] 当前配置是否暴露了漏洞?
**结论**: {高/中/低风险 - 说明理由}
### 触发条件
{详细说明漏洞触发的条件和场景}
### 影响程度
{评估对系统安全性、数据完整性、可用性的影响}
### 紧急程度
{根据严重程度和可利用性确定修复的紧急程度}
## 技术依赖和约束
{列出修复时需要考虑的技术依赖和约束条件}
## 参考链接
- GHSA Advisory: https://github.com/advisories/{ghsa-id}
- CVE Details: https://cve.mitre.org/cgi-bin/cvename.cgi?name={cve-id}
- {其他相关文档}
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.
- 2d ago First seen · 249 lines · 19 tokens per session scan A c5c7abcd3cf8
analyze-dependabot is a command published in the GitHub repository ModelEngine-Group/fit-framework (2,117 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 2,153 once invoked, about $0.0001 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.
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