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-anomaly-detectiongit 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-anomaly-detection)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-anomaly-detection"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-anomaly-detection/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-anomaly-detection"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-anomaly-detection.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 | $0.00040 | $0.01190 |
| Opus 5 | $0.00020 | $0.00595 |
| Sonnet 5 | $0.00008 | $0.00238 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
bi-anomaly-detection 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bi-anomaly-detection
基于阈值识别时间序列中的显著异常波动点，常见场景：
- 日常分析：在指标分析流程中，对北极星指标进行异常波动点识别
- 监控告警：对实时/定时指标数据进行异常检测，触发告警
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 |
| 指标列 | 需要检测的指标值 | 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。
2：选择对比逻辑
根据业务场景按需选择对比逻辑：
| 对比逻辑 | 适用场景 |
|---|---|
| 日环比 | 日异常检测，反应灵敏，适合实时监控和日报 |
| 周同比 | 日异常检测，稳定性好，消除周末效应 |
| 周环比 | 周异常检测 |
| 月环比 | 月异常检测 |
日异常检测场景建议同时检查日环比和周同比
3：确定阈值
针对每种对比逻辑，确定异常判定阈值：
- 已有阈值：若外部已提供阈值，按以下优先级取值，命中即停：
优先级 来源 示例 1 用户显式指定 用户要求"日环比阈值设为 15%" 2 域知识包（若存在） 域知识包指定量值指标默认 10% 3 语义层接口（若可用） 通过接口查询到的指标阈值配置 - 未提供阈值：若没有任何外部来源提供阈值，尝试从历史数据中自适应计算阈值
4：异常点判断
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/anomaly_detection.py
原理：根据传入的阈值参数计算对应的变化率，将变化率绝对值与阈值对比，超过阈值的数据点标记为异常。传入多个阈值时，异常点取交集。
若脚本适用于当前场景，按以下方式调用：
参数：
| 参数 | 说明 |
|---|---|
| --input-file | 输入数据文件路径（必填） |
| --date-col | 日期列名（必填） |
| --metric-col | 指标列名（必填） |
| --threshold-dod | 日环比阈值，传入则检查日环比 |
| --threshold-wow | 周同比阈值，传入则检查周同比（日数据） |
| --threshold-woq | 周环比阈值，传入则检查周环比（周数据） |
| --threshold-mom | 月环比阈值，传入则检查月环比（月数据） |
| --output-file | 异常波动点输出路径（可选） |
至少传入一个阈值参数。传入多个时，异常点为各检查项的交集。
调用示例：
# 只检查日环比
python scripts/anomaly_detection.py \
--input-file data.csv \
--date-col "日期" \
--metric-col "访问用户数" \
--threshold-dod 0.10
# 同时检查日环比和周同比（异常点取交集）
python scripts/anomaly_detection.py \
--input-file data.csv \
--date-col "日期" \
--metric-col "访问用户数" \
--threshold-dod 0.10 \
--threshold-wow 0.15
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 · 122 lines · 40 tokens per session scan A ebcac4709273
bi-anomaly-detection is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (72 stars, last pushed today), licensed Apache-2.0. It adds 40 tokens to every session and 1,190 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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