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 001-frank-aigit 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/001-frank-ai)<a href="https://agentmods.dev/skills/frank666199/frank-presales-skills/001-frank-ai"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/001-frank-ai/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/001-frank-ai"><img src="https://agentmods.dev/badge/skills/frank666199/frank-presales-skills/001-frank-ai.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 | $0.00692 |
| Opus 5 | $0.00000 | $0.00346 |
| Sonnet 5 | $0.00000 | $0.00138 |
| Haiku 4.5 | $0.00000 | $0.00069 |
Grade A, and why
001-Frank-AI需求拆解工具 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 12d 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-AI需求拆解工具
Profile
- Author: Frank
- Version: 1.0.0
- Language: 中文
- Category: 维度1 - AI售前工具应用
- Description: 依托大模型快速拆解政企AI项目需求，输出结构化需求清单
When to Use
初次接触AI项目客户后，获得碎片化需求信息时使用
Input Requirements
- 客户原始需求描述（会议纪要/邮件/聊天记录）
- 项目背景信息（行业/规模/预算区间）
- 已知竞品方案（可选）
Workflow
- 输入客户原始需求素材，AI进行语义解析和意图识别
- 按SPIN框架分类：显性需求（客户明说的）vs 隐性需求（潜在痛点）
- 将需求拆解为业务需求层、功能需求层、技术需求层、非功能需求层
- 对每个需求标注优先级（Must-have/Should-have/Could-have）
- 识别需求间的依赖关系和冲突点
- 输出结构化需求清单，附带需求澄清问题列表
Output Format
Markdown格式的结构化需求清单，包含需求ID、分类、描述、优先级、依赖关系、澄清问题
Output Template
需求ID | 需求分类 | 需求描述 | 优先级 | 来源 | 依赖关系 | 澄清问题
Example
| 字段 | 内容 |
|---|---|
| R001 | 业务需求 |
Constraints
- 不得虚构客户未提及的需求
- 推断的隐性需求必须标注'推断'
- 需求描述使用客户业务语言
Quality Criteria
- 需求分类完整覆盖业务/功能/技术/非功能
- 每条需求可追溯至原始输入
- 澄清问题数量不少于总需求的20%
Applicable Scenarios
- G端政府项目: 适用
- B端企业项目: 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1: AI售前工具应用 | 编号: 001 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.
- 12d ago First seen · 88 lines · 0 tokens per session scan A d47830c67e06
001-Frank-AI需求拆解工具 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 692 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 2026-08-30.
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