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 AgenticAIPlan/AgenticAISkills --skill amoyalpha-presales-assistantgit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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/agenticaiplan/agenticaiskills/amoyalpha-presales-assistant)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/amoyalpha-presales-assistant"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/amoyalpha-presales-assistant/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/agenticaiplan/agenticaiskills/amoyalpha-presales-assistant"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/amoyalpha-presales-assistant.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 | $0.00133 | $0.02396 |
| Opus 5 | $0.00067 | $0.01198 |
| Sonnet 5 | $0.00027 | $0.00479 |
| Haiku 4.5 | $0.00013 | $0.00240 |
Grade A, and why
amoyalpha-presales-assistant 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.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
售前工程师 AI 助手
面向三类客户（政府、行业头部企业、技术型企业）的全链路售前工作流 Skill。
快速使用
| 命令 | 功能 | 适用时机 |
|---|---|---|
/profile |
对话式客户信息采集，生成画像 | 拜访前 |
/ghost |
Ghost Deck（目录+Action Title） | 方案设计阶段 |
/proposal |
逐章生成方案内容 | Ghost Deck确认后 |
/review |
专家视角自审（GO/NO-GO） | 方案提交前 |
/talking-points |
演讲要点生成 | 拜访前1天 |
/objections |
异议处理准备 | 拜访前 |
/score |
商机 Close Readiness 评分 | 任何阶段 |
/followup |
跟进行动计划 + 会议纪要草稿 | 会后1小时内 |
/presales-flow |
完整流程（串联全部模块） | 全新商机 |
三档模式
/presales-flow --mode quick # 快速报价（15分钟，1页摘要）
/presales-flow --mode standard # 标准方案（2小时，5-10页）
/presales-flow --mode tender # 投标响应（分阶段，20-50页）
客户类型自动识别
- 政府：单位名含"局/委/办/厅/部/管委会"等
- 技术型企业：含"AI/SaaS/初创/API/开发者"等技术关键词
- 行业头部企业：含"集团/上市/行业龙头/500强"等规模关键词
- 模糊情况：我会询问用户确认
重要约束（始终遵守）
[!CAUTION]
- 不编造资质证书、业绩案例、客户名称 → 用**【待补充】**占位
- 不虚构技术参数、Benchmark数据 → 用**【待核实】**占位
- 不编造任何价格/成本/合同数字 → 用**【待填】**占位
- 不直接贬低任何竞争对手
- 方案质量门禁由
utils/gate_check.py程序判断，不口头宣布"通过"
完整工作流详述
阶段一：客户信息采集 /profile
执行脚本：
python3 scripts/collect_client_info.py [初始客户信息]
工作方式（借鉴 csm-ebr-qbr-prep-skill 逐一问答模式）：
- 如有初始信息，先做类型识别并展示判断依据
- 按 A→B→C→D 四区块逐一对话采集：
- A：基本信息（公司名、行业、规模）
- B：决策链（联系人角色、决策者、预算流程）
- C：技术现状（现有系统、技术痛点、信创要求）
- D：竞品与背景（是否评估竞品、接触契机）
- 每个区块完成后，用一句话摘要让用户校正
- 全部完成后输出：
- 结构化客户画像（含决策链分析）
- 信息缺口清单（哪些关键信息未获取）
- 建议摸底问题清单（首次拜访用）
- 初步机会评分（高/中/低，附理由）
根据客户类型加载对应参考文件：
- 政府：参考
references/government_sop.md的摸底维度 - 头部企业：参考
references/enterprise_sop.md - 技术型企业：参考
references/tech_sop.md
阶段二：Ghost Deck 生成 /ghost
执行脚本：
python3 scripts/generate_ghost_deck.py [客户类型]
Ghost Structure First 原则（借鉴 mbb-decks）：
- 基于客户画像和痛点，生成方案目录（每页一个 Action Title）
- 只输出 Action Title，不展开内容
- 展示给用户，确认叙事逻辑：
以上是方案的叙事目录，整体逻辑是否流畅？ 有哪些章节需要调整顺序、删除或增加？ 确认后我将逐章展开内容。 - 用户确认后进入阶段三
Action Title 规范（每页标题必须符合）：
- ✅ 主谓宾完整，有具体数字或因果关系，10-15词
- ✅ "三层数据架构消除孤岛，历史数据迁移周期缩短60%"
- ❌ 禁止：纯名词式（"产品架构介绍"）、问句式（"如何解决？"）
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- config/client_profiles.yaml 4.5 KB
- config/gate_rules.yaml 3.2 KB
- config/objection_database.yaml 12 KB
- config/pain_point_map.yaml 2.6 KB
- evals/evals.json 3.5 KB
- references/enterprise_sop.md 2.8 KB
- references/government_sop.md 2.7 KB
- references/objection_playbooks.md 5.1 KB
- references/tech_sop.md 2.7 KB
- scripts/collect_client_info.py 6.2 KB runs code
- scripts/generate_followup.py 3.5 KB runs code
- scripts/generate_ghost_deck.py 9.4 KB runs code
- scripts/generate_proposal.py 3.8 KB runs code
- scripts/generate_talking_points.py 4.7 KB runs code
- scripts/handle_objections.py 4.2 KB runs code
- scripts/score_opportunity.py 3.5 KB runs code
- scripts/self_review.py 5.4 KB runs code
- utils/gate_check.py 7.2 KB runs code
- utils/input_parser.py 4.4 KB runs code
- utils/output_formatter.py 2.9 KB runs code
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 · 248 lines · 133 tokens per session scan A 8227200841f0
amoyalpha-presales-assistant is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 133 tokens to every session and 2,396 once invoked, about $0.0007 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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