charging-pile-experience-analysis

charging-pile-experience-analysis is a skill for Claude Code, Codex from zhouguoqing/QianYuan.AIAgenticFramework. It costs 47 tokens per session (2,700 once invoked), scanned A, original, Apache-2.0.

A framework for analysing why a charging station or charging network gains or loses charging volume and revenue. It examines users, prices, weather, competitors, policies, local activity, equipment, orders, and reviews.

In plain words
What is it for?
It helps diagnose year-over-year volume declines, customer loss, revenue drops, equipment problems, pricing effects, competitor pressure, and operational changes.
Why use it?
It turns a drop in charging activity into a structured investigation with evidence to check, likely causes, actions, and risks.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps diagnose year-over-year volume declines, customer loss, revenue drops, equipment problems, pricing effects, competitor pressure, and operational changes.

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Install with agentmods
npx agentmods add skills/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis
Install

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.

Any agent
npx skills add zhouguoqing/QianYuan.AIAgenticFramework --skill charging-pile-experience-analysis
Clone the repo
git clone --depth 1 https://github.com/zhouguoqing/QianYuan.AIAgenticFramework

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for charging-pile-experience-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis/github.svg)](https://agentmods.dev/skills/zhouguoqing/qianyuan.aiagenticframework/charging-pile-experience-analysis)
Your own site
<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.

agentmods 80×15 button for charging-pile-experience-analysis

Your own site · 80×15
<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>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,700 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 40492b105efa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

artifacts/current-working-snapshot-20260803-110837/skills/charging-pile-experience-analysis/SKILL.md · 174 lines

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

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。
  6. 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

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。
  6. 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。

Read the full file on GitHub · 174 lines

Changes

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.

  1. 11d ago First seen · 174 lines · 47 tokens per session scan A 40492b105efa

Subscribe to this mod's changes

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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