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 agentscope-ai/QwenPaw-Data --skill bi-retention-rategit clone --depth 1 https://github.com/agentscope-ai/QwenPaw-DataWrote 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/agentscope-ai/qwenpaw-data/bi-retention-rate)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-retention-rate"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-retention-rate/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/agentscope-ai/qwenpaw-data/bi-retention-rate"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-retention-rate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00066 | $0.00655 |
| Opus 5 | $0.00033 | $0.00328 |
| Sonnet 5 | $0.00013 | $0.00131 |
| Haiku 4.5 | $0.00007 | $0.00065 |
Grade A, and why
bi-retention-rate 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.
What it actually says
bi-retention-rate
计算用户首次进入产品或首次完成关键行为后，后续回访或持续活跃概率。
执行步骤
Step 0：检查数据以及确定计算指标
- 留存率计算涉及的指标以及数据列已经明确，如次日留存率计算考虑当日用户数与次日用户数，第七日留存率考虑当日留存用户数与第七日留存用户数；
- 数据已经保存为 CSV 文件，且包含留存率计算所需关键数据，如当日用户数、次日用户数等。如
date,当日访问用户数,次日访问用户数
2025-01-01,10000,3000
2025-01-02,10500,1000
2025-01-03,9800,100
Step 1：执行留存率计算
使用 <skill-dir>/scripts/compute_retention.py 脚本，计算用户在后续第 n 天的留存率（计算结果保留小数点后 5 位）。
python <skill-dir>/scripts/compute_retention.py --input_file "<输入数据文件路径 (CSV)>" --metric_name "<指标名称>" --day0_col "<第 0 天用户数列名>" --dayn_col "<第 n 天用户数列名>"
参数说明：
| 参数 | 说明 | 默认值 |
|---|---|---|
| --input_file | 输入数据文件路径 (.csv) | (必填) |
| --metric_name | 计算指标名称（如次日留存率、第七日留存率等） | 留存率 |
| --day0_col | 第 0 天用户数列名 | (必填) |
| --dayn_col | 第 n 天用户数列名 | (必填) |
对于所有需要计算的留存率指标都需调用该脚本计算，不能遗失任何必要的指标计算。
fallback（指引模式）
无脚本环境时按以下步骤手动计算：
- 给定留存率计算所需的初始时间点 day0 以及 n 日后的留存率计算时间点 dayn
- 计算 n 日留存率（保留小数点后 5 位）： $\frac{|{用户 \mid day0新增用户\wedge 用户在dayn活跃 }|}{|{用户 \mid day0新增用户 }|}$
输出要求
输出所有需要计算的留存率指标结果，不要遗失任何计算结果，包括 NaN 值。
What ships with it
1 file 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 · 54 lines · 66 tokens per session scan A 649573545f78
bi-retention-rate is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (73 stars, last pushed 2d ago), licensed Apache-2.0. It adds 66 tokens to every session and 655 once invoked, about $0.0003 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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