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 075-frankgit 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/075-frank)<a href="https://agentmods.dev/skills/frank666199/frank-presales-skills/075-frank"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/075-frank/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/075-frank"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/075-frank.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 | $0.00641 |
| Opus 5 | $0.00000 | $0.00320 |
| Sonnet 5 | $0.00000 | $0.00128 |
| Haiku 4.5 | $0.00000 | $0.00064 |
Grade A, and why
075-Frank-大模型选型工具 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 9d 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-大模型选型工具
Profile
- Author: Frank
- Version: 1.0.0
- Language: 中文
- Category: 维度7 - AI项目专精
- Description: 对比主流大模型在场景下的性能/成本/合规表现
When to Use
AI项目技术选型阶段
Input Requirements
- 应用场景描述
- 性能要求
- 预算约束
- 合规要求
Workflow
- 梳理候选模型：开源(DeepSeek/Qwen/LLaMA) vs 商业(GPT/Claude/文心/通义/豆包)
- 按6维度评估：性能/成本/合规/部署/生态/定制化
- 性能对比：理解能力/生成质量/推理速度/多模态能力
- 成本对比：API调用成本/部署成本/微调成本
- 合规评估：数据出境/算法备案/内容安全
- 输出选型推荐和对比矩阵
- 提供POC验证建议
Output Format
大模型选型报告（含对比矩阵+推荐方案+POC建议）
Output Template
评估维度 | DeepSeek | Qwen | GPT-4 | Claude | 文心 | 通义 | 推荐选择
Example
| 字段 | 内容 |
|---|---|
| 推理速度 | 快(本地) |
Constraints
- 性能数据来自公开评测或实测
- 成本计算含隐性成本
- 合规评估覆盖法规要求
Quality Criteria
- 评估维度完整
- 对比客观公正
- 推荐有充分依据
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: 075 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.
- 9d ago First seen · 90 lines · 0 tokens per session scan A 8f91b26cd2e4
075-Frank-大模型选型工具 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 641 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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