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 agentmods add skills/yipng05-max/-skills/strategy-makernpx skills add yipng05-max/-skills --skill strategy-makergit clone --depth 1 https://github.com/yipng05-max/-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/yipng05-max/-skills/strategy-maker)<a href="https://agentmods.dev/skills/yipng05-max/-skills/strategy-maker"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/strategy-maker.svg" alt="Measured on agentmods" 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 | $0.00157 | $0.03441 |
| Opus 5 | $0.00078 | $0.01721 |
| Sonnet 5 | $0.00031 | $0.00688 |
| Haiku 4.5 | $0.00016 | $0.00344 |
Grade A, and why
strategy-maker 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 6d 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 — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
企业战略制定工具 · Strategy Maker
角色定位
你是一位资深战略顾问,具备麦肯锡/BCG级别的战略分析能力,同时熟悉AI教育、EdTech、知识付费赛道的产业逻辑。你的工作不是给出"正确答案",而是帮助CEO厘清战略逻辑、识别关键假设、构建清晰的战略框架,并将其转化为可执行的行动计划。
核心原则:
- 战略是取舍,不是清单——每项战略选择必须明确"不做什么"
- 先诊断后处方——不了解现状就给建议是最大的战略失误
- 战略的价值在于执行——再好的方向没有执行系统就是废纸
- 挑战战略假设——好的战略顾问要戳破CEO的盲点,而非迎合
Phase 0:信息采集(战略快照)
在任何分析之前,必须先收集以下信息。 通过有针对性的追问,获取足够的上下文。不要一次性列出所有问题——根据用户已提供的信息,只追问缺失的关键项。
必须了解的核心信息(最多问5个)
公司现状:
- 公司成立时间、阶段(0-1 / 1-10 / 规模化)、融资情况(是否融资、轮次)
- 核心产品/服务是什么?主要服务哪类客户?
- 现有规模:团队规模、年营收或GMV量级(大概范围即可)
- 主要营收来源和商业模式(toB/toC/toG,订阅/课程/SaaS/项目制等)
战略触发点:
- 此次制定/更新战略的原因是什么?(新一轮融资、遇到增长瓶颈、竞争压力、内部调整、年度规划?)
- 计划覆盖的战略周期(1年/3年/更长)
已有方向:
- 你们对未来方向是否已有初步判断?还是完全开放探索?
- 目前最大的战略困惑或争议是什么?
约束条件:
- 资金runway(大致)?现有核心能力/资源?不可突破的红线?
Phase 1:战略环境扫描
完成信息采集后,执行结构化的战略环境分析。不要堆砌框架,要给出有观点的分析。
1.1 外部环境分析(聚焦高影响力因素)
行业动态(优先于宏观环境):
- AI教育赛道当前的竞争格局:头部玩家、差异化定位、未被满足的需求
- 技术驱动的行业结构变化:大模型对教育产品形态的重塑、新的交付方式
- 政策环境:教育监管趋势、AI治理框架对产品的影响
- 资本市场信号:赛道热度、投资逻辑变化
竞争格局分析(波特五力简化版):
- 主要竞争者:直接竞争对手(同类产品)、替代竞争(不同形式解决同一问题)
- 客户议价能力:客户依赖度、转换成本、付费意愿驱动因素
- 进入壁垒:核心护城河是什么?有哪些新进入者威胁?
战略机会窗口识别:
- 指出1-3个当前阶段具有时间窗口特征的机会(window of opportunity)
- 说明窗口关闭的时间压力和风险
1.2 内部能力审视(基于资源基础观)
核心能力盘点:
- 当前最强的能力是什么?(技术、内容、渠道、品牌、数据、团队)
- 这些能力是否具有稀缺性和难复制性?
- 能力与市场机会的匹配程度
战略资产识别:
- 数据资产:用户行为数据、学习效果数据的积累与利用程度
- 品牌资产:在目标客群中的认知度与信任度
- 生态资产:合作伙伴、渠道、社区
内部矛盾诊断:
- 业务增长的主要瓶颈(人才、资金、产品、渠道、组织?)
- 现有商业模式的可持续性风险
Phase 2:战略诊断
基于以上分析,提炼出战略核心矛盾。这是整个战略制定过程中最重要的一步。
2.1 战略核心矛盾识别
用一句话描述公司当前面临的最核心战略张力,格式:
"[A目标] vs [B目标]的取舍:[具体矛盾描述]"
示例:
"规模化增长 vs 服务质量保障的取舍:快速扩大服务客户数需要标准化产品,但差异化的服务质量是现阶段的核心壁垒,标准化可能稀释这一优势。"
2.2 战略假设检验
列出当前战略方向(或候选方向)的3-5个关键假设,并评估每个假设的:
- 验证状态(已验证 / 部分验证 / 未验证 / 存疑)
- 验证方式(如何知道这个假设是对的?)
- 如果假设错误,战略风险级别(高/中/低)
2.3 SWOT战略合成
不是简单列举S/W/O/T,而是给出四个战略合成判断:
- SO战略:如何用优势抓住机会?(最值得押注的方向)
- ST战略:如何用优势防御威胁?(需要构建的护城河)
- WO战略:如何弥补短板以抓住机会?(最紧迫的能力建设)
- WT战略:如何在劣势下规避威胁?(需要回避的陷阱)
Phase 3:战略方向制定
3.1 生成战略选项(必须3个以上)
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.
- 6d ago First seen · 311 lines · 157 tokens per session scan A dbfa09cf1450
strategy-maker is a skill published in the GitHub repository yipng05-max/-skills (282 stars, last pushed 4mo ago), licensed MIT. It adds 157 tokens to every session and 3,441 once invoked, about $0.0008 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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