TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
基于阈值检测时间序列中的显著异常波动点。当需要找出指标异常波动日期、识别数据异动时调用. An agent skill from agentscope-ai/QwenPaw-Data.
Install Bi Anomaly Detection
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-anomaly-detection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-anomaly-detection --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-anomaly-detection .claude/skills/bi-anomaly-detection && 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-anomaly-detection" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection into .claude/skills/bi-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-anomaly-detection", 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-anomaly-detectionType 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-anomaly-detection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-anomaly-detection --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-anomaly-detection .agents/skills/bi-anomaly-detection && 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-anomaly-detection" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection into .agents/skills/bi-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-anomaly-detection", 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-anomaly-detection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-anomaly-detection --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-anomaly-detection .cursor/skills/bi-anomaly-detection && 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-anomaly-detection" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection into .cursor/skills/bi-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-anomaly-detection", 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-anomaly-detection--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-anomaly-detection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data bi-anomaly-detection --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-anomaly-detection .gemini/skills/bi-anomaly-detection && 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-anomaly-detection" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection into .gemini/skills/bi-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-anomaly-detection", 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-anomaly-detectionInstalls 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-anomaly-detection -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-anomaly-detection .github/skills/bi-anomaly-detection && 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-anomaly-detection" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection into .github/skills/bi-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-anomaly-detection", 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-anomaly-detection -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-anomaly-detection --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-anomaly-detection .opencode/skills/bi-anomaly-detection && 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-anomaly-detection" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection into .opencode/skills/bi-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bi-anomaly-detection", 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-anomaly-detection- GitHub stars
- 127
- Token cost
- ~637 tokens
- SKILL.md length
- 81 words
- Files
- 2 (incl. scripts)
- Skills in repo
- 29
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
基于阈值检测时间序列中的显著异常波动点。当需要找出指标异常波动日期、识别数据异动时调用. An agent skill from agentscope-ai/QwenPaw-Data.
- Tasks that involve Anomaly detection
- Runs Python scripts from its folder; calls python
What it does
Bi Anomaly Detection is an agent skill from agentscope-ai/QwenPaw-Data. 基于阈值检测时间序列中的显著异常波动点。当需要找出指标异常波动日期、识别数据异动时调用。
Its SKILL.md is about 640 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/anomaly_detection.py`).
It sits in Data & Analytics, covering Anomaly detection. 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.
When your agent uses it
- Tasks that involve Anomaly detection
Example prompts
- “/bi-anomaly-detection”
Requirements
- Python 3
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 Anomaly Detection loads about 637 tokens when it runs. Until then it costs about 16 tokens; SKILL.md has 81 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). 81 words, ~637 tokens.
Download SKILL.mdSave it as
.claude/skills/bi-anomaly-detection/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- bi-anomaly-detection
- description
- 基于阈值检测时间序列中的显著异常波动点。当需要找出指标异常波动日期、识别数据异动时调用。
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输出格式：
检查项: 日环比阈值: 10%, 周同比阈值: 15%
数据行数: 90
异常波动点数: 3
异常波动点:
日期 访问用户数 日环比 周同比
2025-01-15 12000 +20.00% +18.00%
2025-02-01 8500 -15.00% -12.00%
2025-02-14 15000 +25.00% +22.00%方式二：自行实现
若脚本不适用于当前场景，参考上述原理自行实现异常检测。
© 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-anomaly-detection of agentscope-ai/QwenPaw-Data.
- SKILL.md
- scripts/anomaly_detection.py
Open the folder on GitHubat commit e0bae36
Compare with similar skills
Bi Anomaly Detection 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 Anomaly Detection this skillagentscope-ai/QwenPaw-Data | 127 | — | ~637 | Automated safety check: Pass | Apache-2.0 | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Anomalib Adding A Modelopen-edge-platform/anomalib | 6.2k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Anomalib Tiled Ensembleopen-edge-platform/anomalib | 6.2k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Kqlmicrosoft/fabric-rti-mcp | 131 | — | ~6.2k | Automated safety check: Pass | MIT | |
| Time Series Analytics Useropen-edge-platform/edge-ai-libraries | 171 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Bi Anomaly Detection
What does Bi Anomaly Detection do?
基于阈值检测时间序列中的显著异常波动点。当需要找出指标异常波动日期、识别数据异动时调用. An agent skill from agentscope-ai/QwenPaw-Data. Bi Anomaly Detection is an agent skill from agentscope-ai/QwenPaw-Data.
When should I use Bi Anomaly Detection?
Bi Anomaly Detection fits situations like: tasks that involve Anomaly detection.
How do I install Bi Anomaly Detection in Claude Code?
Run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-anomaly-detection -a claude-code`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection in agentscope-ai/QwenPaw-Data) into .claude/skills/bi-anomaly-detection in your project. Claude Code loads it when a task matches its description.
How do I install Bi Anomaly Detection in Codex?
Run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-anomaly-detection -a codex`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/atomic/bi-anomaly-detection in agentscope-ai/QwenPaw-Data) into .agents/skills/bi-anomaly-detection in your project. Codex loads it when a task matches its description.
Can I use Bi Anomaly Detection 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-anomaly-detection -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-anomaly-detection, .gemini/skills/bi-anomaly-detection, .github/skills/bi-anomaly-detection and .opencode/skills/bi-anomaly-detection in your project.
What does Bi Anomaly Detection need to run?
Going by SKILL.md and its folder, Bi Anomaly Detection needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Bi Anomaly Detection 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 Anomaly Detection 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 Anomaly Detection use?
Bi Anomaly Detection 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 Anomaly Detection use?
About 637 tokens (SKILL.md is roughly 2.5k 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 Anomaly Detection?
Skills that share tags, products or a category with Bi Anomaly Detection: TimesFM Forecasting (google-research/timesfm, 34k stars), Anomalib Adding A Model (open-edge-platform/anomalib, 6.2k stars), Anomalib Tiled Ensemble (open-edge-platform/anomalib, 6.2k stars) and Kql (microsoft/fabric-rti-mcp, 131 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Bi Anomaly Detection?
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.