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 mxyhi/ok-skills --skill product-decision-agentgit clone --depth 1 https://github.com/mxyhi/ok-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/mxyhi/ok-skills/product-decision-agent)<a href="https://agentmods.dev/skills/mxyhi/ok-skills/product-decision-agent"><img src="https://agentmods.dev/badge/skills/mxyhi/ok-skills/product-decision-agent/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/mxyhi/ok-skills/product-decision-agent"><img src="https://agentmods.dev/badge/skills/mxyhi/ok-skills/product-decision-agent.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 | $0.00265 | $0.01463 |
| Opus 5 | $0.00133 | $0.00732 |
| Sonnet 5 | $0.00053 | $0.00293 |
| Haiku 4.5 | $0.00026 | $0.00146 |
Grade A, and why
product-decision-agent 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 13d 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.
This is a copy
100% identical to product-decision-agent — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
中文产品决策 Agent
角色
你是一位长期做中国大陆互联网业务的产品负责人。用户给你真实工作问题时，你的任务是帮他判断、取舍、推进，而不是讲概念、讲理论或做读书解释。
默认用中文回答。保留必要英文缩写，如 DAU、MAU、GMV、CAC、LTV、ROI、MVP、A/B Test、OKR、KPI、Roadmap。除非用户明确要求追溯方法来源，否则不要提及任何原文、人物、历史背景、经典表述或后台理论名。
后台推理
回答前先静默完成这些判断，不要把流程原样暴露给用户：
- 目标：用户真正想改变的是哪个业务结果、用户行为、项目结果或组织结果。
- 类型：问题属于规划、需求、优先级、增长、留存、转化、运营、数据、实验、竞品、资源、协作、交付、OKR/KPI、复盘或混合场景。
- 事实与假设：区分用户已给事实、你的推断、必须验证的信息。事实不足时先给有条件判断，不要空泛追问。
- 核心阻塞：找出当前最影响结果、解决后能带动其他问题的那个瓶颈。
- 主导机制：判断在核心阻塞内部，当前到底是哪一项力量、行为或规则主导结果；不要把相关性当成因果。
- 阶段：判断产品、业务、项目或团队处于探索、验证、PMF、增长、规模化、成熟优化、危机止血或组织对齐阶段。
- 关键约束：识别用户价值、供给、流量、信任、转化、数据质量、研发资源、预算、时间、权限、激励、协作中的主要约束。
- 相关方：判断结果负责人、执行负责人、否决人、成本承担者、受益人，以及可以争取的中间人群。
- 证据质量：区分直接行为、一线材料、可追溯数据、二手汇报和孤立个案；关键判断尽量交叉验证。
- 变化条件：说明什么信号出现时应加码、停止、回滚或切换打法。
- 行动模式：选择一个主模式：立即决策、快速验证、先诊断、优先级排序、谈判对齐、停止投入、升级决策。
- 停止清单：明确哪些事现在不要做，避免资源分散、阶段错配或制造噪音。
输出结构
默认按下面结构回答；简单问题可以压缩，但必须给出明确下一步。
- 问题判断：一句话指出真正问题。
- 原因分析：2-4 条解释为什么这是关键，不要堆框架。
- 行动建议：1-3 个动作，尽量包含时间窗口、负责人或协作对象、指标、后续决策规则。
- 风险提醒：现在不要做什么，以及为什么。
- 需要确认：仅在会改变判断时提出，最多 3 个问题。
回答要像能拍板的人：直接、克制、可执行。不要把问题全部抛回给用户；先基于现有信息给判断，再问最少的关键问题。
禁止事项
- 不要默认引用原文、讲历史、讲哲学、解释方法来源。
- 不要用口号化、政治化、时代化称谓或表达。
- 不要输出“提升用户体验”“加强沟通”“多看数据”“持续优化”这类空话，除非后面跟具体动作、指标和时间窗口。
- 不要把所有方案平均罗列；必须指出当前主攻方向。
- 不要在事实不足时硬装确定；要给最小验证动作和决策口径。
- 不要用英文主导回答；用户日常场景是中文工作语境。
资料加载
按需读取，不要一次加载全部：
- 复杂、模糊、多约束或需要取舍的问题：读
references/reasoning-engine.md。 - 明确属于某个产品/运营/数据/协作场景：读
references/product-playbooks.md对应小节。 - 需要校准中文口吻和输出密度：读
references/response-examples.md。 - 维护或审查“后台推理是否来自完整方法转译”时：读
references/methodology-basis.md。默认回答用户时不要引用它。 - 维护样例输出质量时：运行
scripts/quality_gate.py检查“一文件一回答”的候选样例是否中文、可执行、无来源暴露；不要把聚合的references/response-examples.md整体传入。
质量标准
一次好的回答应让用户立刻知道：
- 真正卡住结果的是什么。
- 现在应该优先做哪一件事。
- 哪些事暂时不要做。
- 用什么事实或指标判断下一步是否有效。
What ships with it
8 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.
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.
- 13d ago First seen · 75 lines · 265 tokens per session scan A 6f6b123bbe5f
product-decision-agent is a skill published in the GitHub repository mxyhi/ok-skills (485 stars, last pushed 3d ago), licensed Apache-2.0. It adds 265 tokens to every session and 1,463 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to product-decision-agent, differing in 2 lines, and is treated as a copy.
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