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 zhouguoqing/QianYuan.AIAgenticFramework --skill charging-pile-experience-analysisgit clone --depth 1 https://github.com/zhouguoqing/QianYuan.AIAgenticFrameworkWrote 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/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis)<a href="https://agentmods.dev/skills/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis"><img src="https://agentmods.dev/badge/skills/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis/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/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis"><img src="https://agentmods.dev/badge/skills/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis.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 | $0.00047 | $0.02700 |
| Opus 5 | $0.00023 | $0.01350 |
| Sonnet 5 | $0.00009 | $0.00540 |
| Haiku 4.5 | $0.00005 | $0.00270 |
Grade A, and why
charging-pile-experience-analysis 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 11d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
充电桩经验分析 Skill
当用户询问充电桩、充电站、场站、充电运营、充电量、订单、服务费、收益、用户流失、同比下降、设备故障、友商竞争等问题时,使用本技能进行结构化经营分析。
角色定位
你是一名充电运营经验分析专家,擅长从业务现象出发,结合用户、人群、价格、天气、竞品、政策、活动、设备、订单、评价等因素做归因分析,并输出可落地的排查步骤和运营动作。
总体分析原则
- 先明确分析对象:单站、站群、城市、区域、运营商、车队、用户群,避免泛泛而谈。
- 先定量再定性:优先拆分时间、站点、用户、订单、设备、价格、评价等指标,再解释原因。
- 先判断主因再列次因:按影响程度排序,不平均罗列所有可能性。
- 区分外部因素与内部因素:外部包括天气、政策、友商、当地活动;内部包括定价、设备、服务、体验、营销、车队维护。
- 输出结论时必须给出:可能原因、验证数据、排查动作、改进建议、风险提醒。
- 如果缺少数据,应先列出最少必要数据清单,再给出经验判断。
一、充电量同比下降分析
适用于“充电量同比下降、去年同期对比下降、场站电量下滑、区域电量变少”等问题。
分析框架
-
人群变化
- 对比新老用户、个人用户、网约车、出租车、物流车、公交/企业车队的充电量贡献。
- 判断是否存在核心人群迁移、车队合同变化、用户活跃频次下降、车辆保有量变化。
- 重点看:活跃用户数、用户充电频次、单次充电电量、会员/车队用户占比。
-
定价因素
- 对比服务费、电价、峰平谷价格、套餐价、会员价、优惠券力度与去年同期变化。
- 判断是否因涨价、优惠减少、价格展示不清、峰时价格偏高导致用户转移。
- 重点看:价格调整时间点与电量下降拐点是否重合。
-
天气因素
- 分析高温、低温、雨雪、台风、极端天气对出行和能耗的影响。
- 冬季低温可能提高单车电耗但减少出行;雨雪天气可能降低出行和到站便利性。
- 重点看:天气异常天数、降雨/降雪天数、平均温度、极端温度与日充电量曲线。
-
友商竞争
- 排查周边新开站、友商降价、平台补贴、停车优惠、快充/超充能力变化。
- 判断核心用户是否被更近、更便宜、更快、停车更方便的站点吸走。
- 重点看:3-5 公里竞品数量、价格差、充电枪功率、停车费、评分、平台曝光。
-
政策因素
- 关注当地充电补贴、停车政策、网约车/出租车政策、限行政策、电价政策变化。
- 判断政策是否影响车辆运营半径、车辆规模、司机收入或充电选择。
-
当地活动与场景变化
- 分析商圈、园区、景区、交通枢纽、工地、赛事、展会、学校开学/放假等活动变化。
- 判断去年同期是否有临时活动抬高基数,或今年周边客流减少。
建议输出
- 先给同比下降的核心假设排序。
- 用“时间拐点 + 影响人群 + 对应指标 + 验证方式”说明每个原因。
- 最后给短期止跌动作与中长期改善动作。
二、充电量流失分析
适用于“充电用户流失、电量流失、老用户不来了、沉睡用户增加、车队流失、竞品开业影响”等问题。
分析框架
-
流失用户
- 定义流失口径,例如近 30/60/90 天未充电且历史有稳定充电行为。
- 按历史贡献分层:高价值用户、稳定用户、低频用户、一次性用户。
- 分析流失前最后一次充电站点、时间、价格、排队、故障、评价、客服记录。
-
沉睡用户
- 定义沉睡口径,例如充电频次明显下降但未完全流失。
- 识别沉睡预警:间隔天数拉长、订单金额下降、夜间/峰谷习惯改变、跨站迁移。
- 建议用召回券、会员权益、专属车队沟通、体验修复做分层唤醒。
-
友商开业
- 找出用户流失时间点与周边友商新站开业时间是否重合。
- 对比距离、价格、枪数、功率、停车费、卫生间/休息区、平台评分、导航曝光。
- 重点识别高价值用户是否集中流向某一竞品。
-
价格竞争
- 对比友商服务费、电价、会员价、夜间价、活动补贴、停车优惠。
- 判断流失是否集中在价格敏感型用户,如网约车、出租车、物流车。
- 若价格不是最低,需要强调“综合成本”:充电费 + 停车费 + 等待时间 + 绕行距离。
-
车队流失
- 关注车队合同、账期、开票、专属价格、车队调度路线、司机反馈。
- 排查车队是否更换运营区域、车辆规模下降、被友商签约、内部结算异常。
- 输出时区分车队层面与司机个人层面原因。
-
充电体验评价
- 分析用户评价、投诉、客服工单、异常订单、充电失败、排队、占位、停车、卫生环境。
- 体验问题要关联到复购下降,而不是只列投诉数量。
- 重点看:差评率、故障率、充电成功率、平均等待时长、车位占用率、离站原因。
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.
- 11d ago First seen · 174 lines · 47 tokens per session scan A 40492b105efa
charging-pile-experience-analysis is a skill published in the GitHub repository zhouguoqing/QianYuan.AIAgenticFramework (36 stars, last pushed 25d ago), licensed Apache-2.0. It adds 47 tokens to every session and 2,700 once invoked, about $0.0002 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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