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 frank666199/frank-presales-skills --skill 077-frank-raggit clone --depth 1 https://github.com/frank666199/frank-presales-skillsWrote 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/frank666199/frank-presales-skills/077-frank-rag)<a href="https://agentmods.dev/skills/frank666199/frank-presales-skills/077-frank-rag"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/077-frank-rag/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/frank666199/frank-presales-skills/077-frank-rag"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/077-frank-rag.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 | $0.00000 | $0.00664 |
| Opus 5 | $0.00000 | $0.00332 |
| Sonnet 5 | $0.00000 | $0.00133 |
| Haiku 4.5 | $0.00000 | $0.00066 |
Grade A, and why
077-Frank-RAG方案设计工具 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
Skill: Frank-RAG方案设计工具
Profile
- Author: Frank
- Version: 1.0.0
- Language: 中文
- Category: 维度7 - AI项目专精
- Description: 生成企业知识库RAG方案，含分块/嵌入/检索/重排/生成全流程
When to Use
AI项目中需要设计RAG知识库方案
Input Requirements
- 知识库规模
- 文档类型
- 检索精度要求
Workflow
- 设计文档处理流程：加载→清洗→分块→元数据标注
- 选择嵌入模型：开源(BGE/M3E) vs 商业(OpenAI)
- 设计向量数据库方案：Milvus/Pinecone/Weaviate
- 规划检索策略：稠密检索+稀疏检索+混合检索
- 设计重排序方案：Cross-encoder/Cohere Rerank
- 设计生成方案：Prompt模板+上下文组装+答案生成
- 制定评估方案：检索准确率+生成质量+端到端评估
- 输出RAG方案设计文档
Output Format
RAG方案设计文档（含全流程设计+技术选型+评估方案）
Output Template
RAG环节 | 技术方案 | 选型理由 | 参数配置 | 性能指标 | 备选方案
Example
| 字段 | 内容 |
|---|---|
| 分块策略 | 语义分块(512 token) |
Constraints
- 技术选型有对比分析
- 评估方案可量化
- 方案可落地
Quality Criteria
- 全流程覆盖
- 技术选型合理
- 评估方案科学
Applicable Scenarios
- G端政府项目: 部分适用
- B端企业项目: 部分适用
- AI智能项目: 适用
Usage
方式1：Claude Code / Cursor / Codex
将本SKILL.md内容复制到Agent技能配置区，通过技能名触发。
方式2：飞书妙搭 / 扣子
将SKILL.md内容粘贴到Agent提示词配置区，设置触发词为技能名。
方式3：独立使用
直接复制本文件内容到AI对话中，按Workflow步骤执行。
Frank专属售前技能 | 维度7: AI项目专精 | 编号: 077 基于"Frank售前解决方案Skills工具集 v1.0"与实操提示词融合优化生成
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 · 90 lines · 0 tokens per session scan A 1d17108b3264
077-Frank-RAG方案设计工具 is a skill published in the GitHub repository frank666199/frank-presales-skills (11 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 664 tokens. 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 2026-09-03.
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