Ltv Predictor
liangdabiao/claude-data-analysis-ultra-main
基于RFM模型和回归算法的客户生命周期价值(LTV)预测分析工具，支持电商和零售业务的客户价值预测。使用时需要客户交易数据、订单历史或消费记录，自动进行RFM特征工程、回归建模和价值预测。
评估单个用户或用户群体在生命周期内创造的收益。触发条件：当需要用户生命周期价值计算与分析时触发，如对话中涉及“LTV”、“LTV 分析”、“用户生命周期价值”、“人均收益测算”等等相近词语时调用。
Apache-2.0Auto-check passed
Install Bi Ltv Analysis
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-ltv-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis .claude/skills/bi-ltv-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "bi-ltv-analysis" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis into .claude/skills/bi-ltv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-ltv-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-ltv-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .agents/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis .agents/skills/bi-ltv-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bi-ltv-analysis" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis into .agents/skills/bi-ltv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-ltv-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-ltv-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis .cursor/skills/bi-ltv-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bi-ltv-analysis" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis into .cursor/skills/bi-ltv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-ltv-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agentscope-ai/QwenPaw-Data.git --path packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-ltv-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis .gemini/skills/bi-ltv-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bi-ltv-analysis" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis into .gemini/skills/bi-ltv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-ltv-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agentscope-ai/QwenPaw-Data bi-ltv-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .github/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis .github/skills/bi-ltv-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bi-ltv-analysis" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis into .github/skills/bi-ltv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-ltv-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-ltv-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis .opencode/skills/bi-ltv-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bi-ltv-analysis" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis into .opencode/skills/bi-ltv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-ltv-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
bi-ltv-analysis- GitHub stars
- 127
- Token cost
- ~743 tokens
- SKILL.md length
- 130 words
- Files
- 2 (incl. scripts)
- Skills in repo
- 29
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
评估单个用户或用户群体在生命周期内创造的收益。触发条件：当需要用户生命周期价值计算与分析时触发，如对话中涉及“LTV”、“LTV 分析”、“用户生命周期价值”、“人均收益测算”等等相近词语时调用。
- Works in 2 steps: ：准备数据 → ：计算 LTV
- Runs Python scripts from its folder; calls python
What it does
Bi Ltv Analysis is an agent skill from agentscope-ai/QwenPaw-Data. 评估单个用户或用户群体在生命周期内创造的收益。触发条件：当需要用户生命周期价值计算与分析时触发，如对话中涉及“LTV”、“LTV 分析”、“用户生命周期价值”、“人均收益测算”等等相近词语时调用。
Its SKILL.md is about 740 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/ltv_calc.py`).
The repository describes itself as: Agentic enterprise data analytics: governed facts (DataBridge), reusable methodology (Skill-Hub), and controllable execution (Host). The licence is Apache-2.0.
Example prompts
- “LTV 分析”
- “用户生命周期价值”
- “人均收益测算”
- “/bi-ltv-analysis”
Requirements
- Python 3
Workflow steps
2 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit e0bae36. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Bi Ltv Analysis loads about 743 tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 130 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from agentscope-ai/QwenPaw-Data at commit e0bae36, republished under its Apache-2.0 licence (© agentscope-ai). 130 words, ~743 tokens.
Download SKILL.mdSave it as
.claude/skills/bi-ltv-analysis/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- bi-ltv-analysis
- description
- 评估单个用户或用户群体在生命周期内创造的收益。触发条件：当需要用户生命周期价值计算与分析时触发，如对话中涉及“LTV”、“LTV 分析”、“用户生命周期价值”、“人均收益测算”等等相近词语时调用。
bi-ltv-analysis
评估单个用户或用户群体在整个生命周期内创造的收入（或利润），典型用途。
执行步骤
Step 1：准备数据
根据分析任务需求与所给数据结构，明确 LTV 分析涉及的指标，即分析粒度（用户级 / 群体级）、收益口径（如购买、升级等行为产生的收入、利润等）以及生命周期（如时间窗口、某阶段流失率等），将取数结果保存为 CSV。
整理数据为 CSV 格式，采用“分组（用户/群体） × N天（生命周期与收益数据）”，数据应至少包括分析粒度对应的对象列、收益口径对应的收入数据列以及生命周期对应的数据列。
例如，对于用户级的 LTV 分析，分析数据示例如下，
用户ID,事件时间a,收入金额a,事件时间b,收入金额b
1,2025-04-25,26.0,2025-04-26,6.5
2,2025-04-25,6.5,NaN,NaN对于群体级的 LTV 分析，数据示例如下，
群体类别,a阶段流失率,a阶段收入金额,b阶段流失率,b阶段收入金额
group_1,0.366,26.0,0.121,6.5
group_2,0.253,6.5,0.754,2.0若上游步骤已产出可用汇总表则直接使用，否则按口径从明细聚合（按用户求和、按队列求和等）。
注意：若数据包含留存率、流失率等指标可以用于 LTV 分析，则统一使用流失率进行计算，若数据中仅包含留存率，则转化为流失率进行 LTV 计算和分析，一般情况，流失率=1-留存率。
Step 2：计算 LTV
LTV 计算公式一：
(LTV = ARPU \times 生命周期)
LTV 计算公式二：
(LTV = ARPU \times \frac{1}{流失率})
ARPU（Average Revenue Per User）：单位用户在指定统计范围内的平均收入。若 CSV 中收入列为阶段合计、且另有用户数列，则脚本按 (ARPU = 收入 / 用户数) 计算；若收入列已是人均口径，则无需 --users-col。
群体多阶段（含多列「阶段流失率 + 阶段收入金额」）：按阶段顺序，用存活率对收入加权求和：
[ LTV = \sum_{j} 收入_j \times \prod_{k<j}(1 - 流失率_k) ]
方式一：使用脚本
使用 <skill-dir>/scripts/ltv_calc.py 计算 LTV。结果写入 --output-file 指定的 CSV：第一列为输入中的分析对象列（--object-col），第二列为 LTV（默认列名 ltv）。
用户级（观测窗口内各阶段收入累计）：
python <skill-dir>/scripts/ltv_calc.py \
--input-file "<输入 CSV 路径>" \
--object-col "用户ID" \
--revenue-cols "收入金额a" "收入金额b" \
--output-file "<输出 CSV 路径>"群体级（多阶段流失率 + 收入）：
python <skill-dir>/scripts/ltv_calc.py \
--input-file "<输入 CSV 路径>" \
--object-col "群体类别" \
--revenue-cols "a阶段收入金额" "b阶段收入金额" \
--churn-cols "a阶段流失率" "b阶段流失率" \
--output-file "<输出 CSV 路径>"公式一（单列收入 + 生命周期 T）：
python <skill-dir>/scripts/ltv_calc.py \
--input-file "<输入 CSV 路径>" \
--object-col "群体类别" \
--revenue-col "a阶段收入金额" \
--users-col "用户数" \
--lifecycle 12 \
--formula lifecycle \
--output-file "<输出 CSV 路径>"公式二（单列收入 + 单列流失率）：
python <skill-dir>/scripts/ltv_calc.py \
--input-file "<输入 CSV 路径>" \
--object-col "群体类别" \
--revenue-col "a阶段收入金额" \
--churn-col "a阶段流失率" \
--formula churn \
--output-file "<输出 CSV 路径>"若数据列为留存率而非流失率，增加 --rate-is-retention（按 流失率 = 1 − 留存率 转换）。
参数说明：
| 参数 | 说明 | 默认值 |
|---|---|---|
--input-file | 输入数据文件路径（CSV） | （必填） |
--object-col | 分析对象列名（用户 ID、群体类别等） | （必填） |
--output-file | 输出 CSV 路径（两列：分析对象、LTV） | （必填） |
--ltv-col | 输出中 LTV 列名 | ltv |
--formula | cumulative / lifecycle / churn / stage_weighted；省略时按其它参数自动推断 | 自动推断 |
--revenue-cols | 多阶段收入列名（可多个） | — |
--churn-cols | 多阶段流失率列名，与 --revenue-cols 顺序一一对应 | — |
--revenue-col | 单列收入（公式一、二） | — |
--churn-col | 单列流失率（公式二） | — |
--lifecycle | 生命周期数值 T（公式一） | — |
--users-col | 用户数列（收入为合计时计算 ARPU） | — |
--rate-is-retention | 将流失率列按留存率处理并转换为流失率 | 否 |
© agentscope-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file (scripts) in packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis of agentscope-ai/QwenPaw-Data.
- SKILL.md
- scripts/ltv_calc.py
Open the folder on GitHubat commit e0bae36
Compare with similar skills
Bi Ltv Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bi Ltv Analysis this skillagentscope-ai/QwenPaw-Data | 127 | — | ~743 | Automated safety check: Pass | Apache-2.0 | |
| Ltv Predictorliangdabiao/claude-data-analysis-ultra-main | 290 | — | ~1.4k | Automated safety check: Notes | None | |
| Biz Cac Ltvasgard-ai-platform/skills | 242 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Cac Ltv Viabilityahmadvh/octochains | 377 | — | ~323 | Automated safety check: Pass | Custom licence | |
| Blueprintaffaan-m/ECC | 276k | 2 repos | ~604 | Automated safety check: Pass | MIT | |
| Token Budget Advisoraffaan-m/ECC | 276k | — | ~927 | Automated safety check: Pass | MIT |
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Questions about Bi Ltv Analysis
What does Bi Ltv Analysis do?
评估单个用户或用户群体在生命周期内创造的收益。触发条件：当需要用户生命周期价值计算与分析时触发，如对话中涉及“LTV”、“LTV 分析”、“用户生命周期价值”、“人均收益测算”等等相近词语时调用。. Bi Ltv Analysis is an agent skill from agentscope-ai/QwenPaw-Data.
How do I install Bi Ltv Analysis in Claude Code?
Run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a claude-code`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis in agentscope-ai/QwenPaw-Data) into .claude/skills/bi-ltv-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Bi Ltv Analysis in Codex?
Run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a codex`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/atomic/bi-ltv-analysis in agentscope-ai/QwenPaw-Data) into .agents/skills/bi-ltv-analysis in your project. Codex loads it when a task matches its description.
Can I use Bi Ltv Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-ltv-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bi-ltv-analysis, .gemini/skills/bi-ltv-analysis, .github/skills/bi-ltv-analysis and .opencode/skills/bi-ltv-analysis in your project.
What does Bi Ltv Analysis need to run?
Going by SKILL.md and its folder, Bi Ltv Analysis needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Bi Ltv Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Bi Ltv Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Bi Ltv Analysis use?
Bi Ltv Analysis is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Bi Ltv Analysis use?
About 743 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Bi Ltv Analysis?
Skills that share tags, products or a category with Bi Ltv Analysis: Ltv Predictor (liangdabiao/claude-data-analysis-ultra-main, 290 stars), Biz Cac Ltv (asgard-ai-platform/skills, 242 stars), Cac Ltv Viability (ahmadvh/octochains, 377 stars) and Blueprint (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Bi Ltv Analysis?
agentscope-ai (a GitHub organization) maintains it in agentscope-ai/QwenPaw-Data, which has 127 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 5, 2026.
Source: agentscope-ai/QwenPaw-Data on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.