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 echoVic/blade-code --skill github-explorergit clone --depth 1 https://github.com/echoVic/blade-codeWrote 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/echovic/blade-code/github-explorer)<a href="https://agentmods.dev/skills/echovic/blade-code/github-explorer"><img src="https://agentmods.dev/badge/skills/echovic/blade-code/github-explorer/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/echovic/blade-code/github-explorer"><img src="https://agentmods.dev/badge/skills/echovic/blade-code/github-explorer.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 | $0.00035 | $0.00820 |
| Opus 5.5 | $0.00014 | $0.00328 |
| Sonnet 5.5 | $0.00007 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
github-explorer 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.
What it actually says
GitHub 项目探索和总结
Instructions
当用户想要探索和总结 GitHub 上的热门项目时，按照以下步骤执行：
1. 确定搜索目标
- 如果用户提供了具体的项目名称或技术栈，直接搜索相关项目
- 如果用户没有明确目标，搜索当前热门或趋势项目
- 使用 WebSearch 工具查找相关 GitHub 仓库
2. 收集项目信息
使用以下方法收集项目信息：
- WebSearch: 搜索 "trending GitHub repositories" 或 "popular GitHub projects "
- WebFetch: 获取 GitHub 项目页面信息
- GitHub API: 如果需要，可以访问 GitHub API 获取项目详细信息
3. 分析项目内容
对每个项目进行以下分析：
- 项目名称和描述: 获取项目的基本信息
- Star 数量: 评估项目的受欢迎程度
- 主要技术栈: 识别项目使用的主要技术
- 最近更新: 检查项目活跃度
- 贡献者数量: 评估社区活跃度
- 项目特点: 识别项目的独特功能或优势
- 文档质量: 评估项目的文档完整性
4. 生成项目总结
为每个项目生成以下信息：
- 项目名称: 项目完整名称
- 描述: 项目的主要功能和目标
- GitHub 链接: 项目地址
- Stars: Star 数量
- 技术栈: 使用的主要技术
- 最后更新: 最近更新时间
- 项目亮点: 项目的独特功能或优势
- 适用场景: 适合的应用场景
5. 提供综合分析
- 按受欢迎程度排序项目列表
- 提供整体趋势分析
- 推荐最适合用户需求的项目
- 指出项目之间的差异和特点
Output Format
以结构化格式输出项目信息：
## 项目名称
- **描述**: 项目功能描述
- **GitHub**: [链接地址](https://github.com/...)
- **Stars**: 数量
- **技术栈**: 技术列表
- **最后更新**: 日期
- **亮点**: 项目特色
- **适用场景**: 使用场景
Examples
示例 1: 探索前端框架
用户: "帮我找一些流行的前端框架项目"
- 搜索 "popular frontend frameworks GitHub"
- 分析 React, Vue, Angular 等项目
- 提供详细的技术栈和特点对比
示例 2: 探索 AI 相关项目
用户: "找一些热门的 AI 开源项目"
- 搜索 "trending AI open source projects"
- 分析项目的技术栈和应用场景
- 提供项目亮点和适用场景
示例 3: 探索特定技术
用户: "找一些用 TypeScript 写的优秀项目"
- 搜索 "best TypeScript projects GitHub"
- 分析项目质量和技术特点
- 提供综合评价和推荐
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 · 100 lines · 35 tokens per session scan A d28ce000fcbc
github-explorer is a skill published in the GitHub repository echoVic/blade-code (181 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 820 once invoked, about $0.0001 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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