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Get Started Free →中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景:产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B Test、用户反馈、客服/销售反馈、竞品冲击、资源不足、项目延期、需求反复、老板临时插需求、跨部门协作、团队冲突、OKR/KPI、目标拆解、复盘等。触发时像资深互联网产品负责人一样,先判断真实问题、当前阶段、核心阻塞、关键约束、相关方和证据充分性,再给出最值得执行的下一步。默认中文回答,不讲理论、不引用原文、不解释历史、不暴露后台方法来源。
.claude/skills/mxyhi-product-decision-agent/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-22 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 25% | 0% |
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。
回答前先静默完成这些判断,不要把流程原样暴露给用户:
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。
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。
按需读取,不要一次加载全部:
references/reasoning-engine.md。references/product-playbooks.md 对应小节。references/response-examples.md。references/methodology-basis.md。默认回答用户时不要引用它。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。一次好的回答应让用户立刻知道:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-22 | fail→pass | 17,762 | 15,555 | -12% | 1 | 1 | 0% | 2,587 | 3,542 | +37% | 0 | 0 | — |
case-01 | fail→fail | 23,348 | 20,615 | -12% | 1 | 1 | 0% | 3,410 | 4,171 | +22% | 0 | 0 | — |
case-02 | fail→pass | 30,610 | 17,112 | -44% | 1 | 1 | 0% | 2,751 | 3,367 | +22% | 0 | 0 | — |
case-16 | pass→pass | 20,504 | 15,978 | -22% | 1 | 1 | 0% | 2,713 | 3,123 | +15% | 0 | 0 | — |
case-17 | pass→pass | 17,235 | 13,989 | -19% | 1 | 1 | 0% | 2,590 | 3,175 | +23% | 0 | 0 | — |
case-03 | pass→pass | 18,500 | 13,286 | -28% | 1 | 1 | 0% | 2,790 | 3,011 | +8% | 0 | 0 | — |
case-04 | pass→pass | 25,698 | 19,695 | -23% | 1 | 1 | 0% | 3,894 | 3,914 | +1% | 0 | 0 | — |
case-05 | pass→pass | 21,016 | 17,096 | -19% | 1 | 1 | 0% | 3,107 | 3,662 | +18% | 0 | 0 | — |
case-06 | pass→pass | 21,102 | 16,335 | -23% | 1 | 1 | 0% | 3,100 | 3,570 | +15% | 0 | 0 | — |
case-07 | pass→pass | 21,809 | 15,259 | -30% | 1 | 1 | 0% | 3,139 | 3,205 | +2% | 0 | 0 | — |
case-08 | pass→pass | 11,375 | 12,034 | +6% | 1 | 1 | 0% | 2,350 | 3,459 | +47% | 0 | 0 | — |
case-09 | pass→pass | 19,676 | 17,693 | -10% | 1 | 1 | 0% | 2,826 | 3,466 | +23% | 0 | 0 | — |
case-10 | pass→pass | 17,998 | 14,328 | -20% | 1 | 1 | 0% | 2,653 | 3,212 | +21% | 0 | 0 | — |
case-11 | pass→pass | 18,274 | 14,248 | -22% | 1 | 1 | 0% | 2,725 | 3,064 | +12% | 0 | 0 | — |
case-12 | pass→pass | 19,716 | 21,169 | +7% | 1 | 1 | 0% | 3,832 | 4,736 | +24% | 0 | 0 | — |
case-13 | fail→pass | 16,764 | 11,395 | -32% | 1 | 1 | 0% | 2,528 | 2,895 | +15% | 0 | 0 | — |
case-14 | pass→pass | 21,626 | 13,095 | -39% | 1 | 1 | 0% | 2,958 | 2,931 | -1% | 0 | 0 | — |
case-15 | pass→fail | 32,311 | 16,452 | -49% | 1 | 1 | 0% | 3,286 | 3,523 | +7% | 0 | 0 | — |
case-18 | fail→pass | 19,043 | 13,978 | -27% | 1 | 1 | 0% | 2,928 | 3,165 | +8% | 0 | 0 | — |
case-19 | fail→pass | 16,394 | 14,118 | -14% | 1 | 1 | 0% | 2,428 | 3,029 | +25% | 0 | 0 | — |
case-20 | pass→pass | 16,641 | 17,390 | +5% | 1 | 1 | 0% | 2,582 | 3,388 | +31% | 0 | 0 | — |
case-21 | pass→pass | 19,238 | 16,255 | -16% | 1 | 1 | 0% | 2,784 | 3,348 | +20% | 0 | 0 | — |
case-23 | fail→fail | 28,706 | 13,204 | -54% | 1 | 1 | 0% | 2,794 | 2,944 | +5% | 0 | 0 | — |
case-24 | pass→pass | 16,962 | 14,340 | -15% | 1 | 1 | 0% | 2,742 | 3,343 | +22% | 0 | 0 | — |
case-25 | pass→pass | 15,672 | 15,832 | +1% | 1 | 1 | 0% | 2,369 | 3,245 | +37% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +16 percentage points is the difference between those two pass rates over the 25 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.