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-adaptive-thresholdgit 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-adaptive-threshold)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-adaptive-threshold"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-adaptive-threshold/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-adaptive-threshold"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-adaptive-threshold.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 | $0.00072 | $0.01278 |
| Opus 5 | $0.00036 | $0.00639 |
| Sonnet 5 | $0.00014 | $0.00256 |
| Haiku 4.5 | $0.00007 | $0.00128 |
Grade A, and why
bi-adaptive-threshold 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bi-adaptive-threshold
基于标准差衡量数据的自然波动幅度，从数值序列中自适应计算阈值，常见场景：
- 异常检测：确定指标波动率的异常阈值
- 影响度判定：确定事件影响度的显著性阈值
- 其他需要从数据分布中推导合理边界的场景
适用范围：仅适用于日粒度和周粒度的阈值计算。
执行步骤
1：数据准备
包含时间序列数据的 CSV 文件，至少包含以下两列：
| 列 | 说明 | 示例 |
|---|---|---|
| 日期列 | 时间标识 | 日期 |
| 数值列 | 需要计算阈值的目标数值序列 | 访问用户数 |
示例：
日期,访问用户数
2025-01-01,10000
2025-01-02,10500
2025-01-03,9800
若上游步骤已产出可用数据文件则直接使用，否则自行取数。默认取近 90 天数据，若可用数据不足 90 天则取全部。
2：执行阈值计算
按以下优先级选择计算方式，命中即停：
方式一：使用脚本
路径： scripts/adaptive_threshold.py
原理：
- 检查数据量是否充足（即：数据行数 <
最少数据行数），不足则直接返回默认阈值 - 计算变化率序列，用 IQR 围栏法（四分位距 ×
IQR 倍数）剔除极端异常值 - 通过 ruptures 库检测结构性变化点，将数据划分为若干分段
- 取最后一段平稳区间：
- 若最后一段不足
最少数据行数，说明稳定数据不够，可能处于业务初期，返回默认阈值； - 计算该段数据标准差，若标准差 ≥
阈值上限，说明当前仍处于剧烈波动期，返回默认阈值
- 若最后一段不足
- 最终阈值 = 标准差 ×
标准差倍数，并限制在 [阈值下限,阈值上限] 范围内，避免阈值过于敏感或过于迟钝
若脚本适用于当前场景，按以下方式调用：
参数：
| 参数 | 说明 | 默认值 |
|---|---|---|
| --input-file | 输入数据文件路径 | （必填） |
| --date-col | 日期列名 | （必填） |
| --metric-col | 数值列名 | （必填） |
| --default-threshold | 默认阈值，按指标类型区分，详见下方说明 | （必填） |
| --min-periods | 最少数据行数 | 30 |
| --iqr-multiplier | IQR 倍数 | 1.5 |
| --std-multiplier | 标准差倍数 | 3.0 |
| --threshold-min | 阈值下限 | 0.05 |
| --threshold-max | 阈值上限 | 0.20 |
| --ruptures-method | ruptures 库的变化点检测算法（pelt/binseg/window） | pelt |
| --ruptures-penalty | ruptures 库的变化点检测的惩罚系数，值越大分段越少 | None (自动计算) |
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.
- 11d ago First seen · 106 lines · 72 tokens per session scan A 7eca85c5d124
bi-adaptive-threshold is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (72 stars, last pushed today), licensed Apache-2.0. It adds 72 tokens to every session and 1,278 once invoked, about $0.0004 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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