Borrowing it
Nothing to install: this file belongs to hhhh124hhhh/godot-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hhhh124hhhh/godot-mcp/main/.claude/skills/godot-skills/sub-skills/context7-auto-research/SKILL.mdgit clone --depth 1 https://github.com/hhhh124hhhh/godot-mcpWrote 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/hhhh124hhhh/godot-mcp/context7-auto-research)<a href="https://agentmods.dev/skills/hhhh124hhhh/godot-mcp/context7-auto-research"><img src="https://agentmods.dev/badge/skills/hhhh124hhhh/godot-mcp/context7-auto-research/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/hhhh124hhhh/godot-mcp/context7-auto-research"><img src="https://agentmods.dev/badge/skills/hhhh124hhhh/godot-mcp/context7-auto-research.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 | $0.00036 | $0.01024 |
| Opus 5 | $0.00018 | $0.00512 |
| Sonnet 5 | $0.00007 | $0.00205 |
| Haiku 4.5 | $0.00004 | $0.00102 |
Grade A, and why
context7-auto-research 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 10d 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
Context7 自动化研究技能
指令
当用户输入包含以下关键词时，自动触发Context7文档研究：
中文触发条件
- "如何实现"、"怎么写"、"怎样制作" → 自动查询实现方案和教程
- "配置"、"设置"、"安装" → 自动获取配置指南和最佳实践
- "文档"、"参考"、"资料" → 自动检索官方文档和权威资料
- "最佳实践"、"推荐"、"建议" → 自动收集社区经验和优化建议
- "问题"、"错误"、"故障" → 自动搜索解决方案和故障排除指南
英文触发条件
- "how to implement"、"how to write" → 自动查询实现教程
- "configure"、"setup"、"install" → 自动获取配置指南
- "documentation"、"docs"、"reference" → 自动检索官方文档
- "best practice"、"recommendation" → 自动收集最佳实践
执行步骤
- 检测触发条件 - 分析用户输入是否包含上述关键词
- 生成查询策略 - 根据用户需求自动生成最优的Context7查询
- 执行Context7搜索 - 使用
/skill context7进行文档检索 - 整合信息 - 将Context7返回的信息与项目上下文结合
- 提供解决方案 - 给出具体的代码示例、配置步骤或实施建议
MCP工具使用说明
此技能主要使用以下MCP工具：
- Context7 MCP - 核心文档检索和信息查询工具
- WebFetch MCP - 辅助获取在线文档和资源
- Skill MCP - 用于调用其他相关技能
MCP调用方式
# 直接调用Context7 MCP
/skill context7 "查询内容"
# 在技能内部调用Context7
await useSkill('context7', { query: "用户查询内容" })
关键要素
自动化规则实现
- 严格按照用户设定："Always use context7 when I need code generation, setup or configuration steps, or library/API documentation"
- 无需用户明确请求，自动触发Context7查询
- 确保查询结果的准确性和时效性
查询优化策略
- 中英文关键词智能映射
- 技术栈特定查询优化
- 官方文档优先原则
- 社区最佳实践补充
结果质量保证
- 验证信息来源的权威性
- 确保代码示例的完整性
- 检查配置步骤的准确性
- 提供多层次的解决方案
示例场景
场景1：Godot开发咨询
用户输入: "如何实现Godot 4.x的彩色爆炸粒子效果"
自动执行流程:
- 检测到"如何实现"触发条件
- 自动查询:
/skill context7 "Godot 4.x colored explosion particle effect implementation tutorial" - 整合官方文档和社区最佳实践
- 提供完整的GDScript代码示例和配置说明
场景2：MCP工具配置
用户输入: "配置Chrome DevTools MCP服务器"
自动执行流程:
- 检测到"配置"触发条件
- 自动查询:
/skill context7 "Chrome DevTools MCP server configuration setup guide" - 获取安装步骤和配置示例
- 包含故障排除和常见问题解决方案
输出格式
- 问题理解 - 确认用户需求和上下文
- 研究过程 - 说明使用的Context7查询策略
- 核心发现 - 突出最重要的信息点
- 实施方案 - 提供具体的代码或配置
- 补充资源 - 提供进一步学习的链接
注意事项
- 确保Context7查询的针对性和准确性
- 验证返回信息的时效性和可靠性
- 根据项目技术栈调整建议的适用性
- 提供足够的上下文信息帮助用户理解
- 在技术实现和最佳实践之间找到平衡
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.
- 10d ago First seen · 104 lines · 36 tokens per session scan A 5bfe4b38fc41
context7-auto-research is a skill published in the GitHub repository hhhh124hhhh/godot-mcp (42 stars, last pushed 9mo ago), licensed MIT. It adds 36 tokens to every session and 1,024 once invoked, about $0.0002 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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