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 080-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/080-frank)<a href="https://agentmods.dev/skills/frank666199/frank-presales-skills/080-frank"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/080-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/080-frank"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/080-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 | $0.00619 |
| Opus 5 | $0.00000 | $0.00309 |
| Sonnet 5 | $0.00000 | $0.00124 |
| Haiku 4.5 | $0.00000 | $0.00062 |
Grade A, and why
080-Frank-行业大模型方案工具 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-行业大模型方案工具
Profile
- Author: Frank
- Version: 1.0.0
- Language: 中文
- Category: 维度7 - AI项目专精
- Description: 生成垂直行业大模型预训练/微调/部署方案
When to Use
行业大模型定制项目
Input Requirements
- 目标行业
- 可用行业数据
- 性能要求
- 预算约束
Workflow
- 分析行业大模型需求：场景/数据/性能/合规
- 选择基座模型：开源大模型(DeepSeek/Qwen/LLaMA)
- 设计预训练方案（如需要）：行业语料+训练策略
- 设计微调方案：SFT+RLHF+DPO
- 规划数据方案：数据采集→清洗→标注→增强
- 设计评估方案：行业Benchmark+人工评估
- 规划部署方案：量化/蒸馏/推理优化
- 输出行业大模型方案
Output Format
行业大模型方案（含基座选型+训练方案+数据方案+评估方案+部署方案）
Output Template
方案环节 | 技术方案 | 数据/资源需求 | 预期效果 | 风险/备注
Example
| 字段 | 内容 |
|---|---|
| 微调 | LoRA微调+SFT |
Constraints
- 数据来源合法合规
- 训练方案有依据
- 评估方案可量化
Quality Criteria
- 方案覆盖全流程
- 技术方案专业
- 成本估算合理
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: 080 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 · 91 lines · 0 tokens per session scan A fdec14261800
080-Frank-行业大模型方案工具 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 619 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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