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.
Detect anomalies and outliers in research data using statistical methods
Install Data Anomaly Detection
$ npx skills add wentorai/research-plugins --skill data-anomaly-detection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wentorai/research-plugins data-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/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis/statistics/data-anomaly-detection .claude/skills/data-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 "data-anomaly-detection" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/data-anomaly-detection into .claude/skills/data-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-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/wentorai/research-plugins/tree/main/skills/analysis/statistics/data-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 wentorai/research-plugins --skill data-anomaly-detection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wentorai/research-plugins data-anomaly-detection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analysis/statistics/data-anomaly-detection .agents/skills/data-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 "data-anomaly-detection" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/data-anomaly-detection into .agents/skills/data-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-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 wentorai/research-plugins --skill data-anomaly-detection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wentorai/research-plugins data-anomaly-detection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analysis/statistics/data-anomaly-detection .cursor/skills/data-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 "data-anomaly-detection" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/data-anomaly-detection into .cursor/skills/data-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-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/wentorai/research-plugins.git --path skills/analysis/statistics/data-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 wentorai/research-plugins --skill data-anomaly-detection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wentorai/research-plugins data-anomaly-detection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analysis/statistics/data-anomaly-detection .gemini/skills/data-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 "data-anomaly-detection" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/data-anomaly-detection into .gemini/skills/data-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-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 wentorai/research-plugins data-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 wentorai/research-plugins --skill data-anomaly-detection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analysis/statistics/data-anomaly-detection .github/skills/data-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 "data-anomaly-detection" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/data-anomaly-detection into .github/skills/data-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-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 wentorai/research-plugins --skill data-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 wentorai/research-plugins data-anomaly-detection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analysis/statistics/data-anomaly-detection .opencode/skills/data-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 "data-anomaly-detection" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/analysis/statistics/data-anomaly-detection into .opencode/skills/data-anomaly-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-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
data-anomaly-detection- GitHub stars
- 298
- Used in
- 1 other repo
- Token cost
- ~1.7k tokens
- SKILL.md length
- 402 words
- Files
- 1
- Skills in repo
- 405
- Repo updated
- First seen
- Licence
- MIT
At a glance
Detect anomalies and outliers in research data using statistical methods
- Works in 5 steps: State the detection method and its… → Report the number and percentage of… → Describe the disposition: how many were… → …
- Tasks that involve Anomaly detection
- SKILL.md covers Overview, Statistical Detection Methods, Diagnosis Framework and Time-Series Anomaly Detection, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Anomaly Detection is an agent skill from wentorai/research-plugins. Detect anomalies and outliers in research data using statistical methods
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Anomaly detection. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.
When your agent uses it
- Tasks that involve Anomaly detection
Example prompts
- “/data-anomaly-detection”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- State the detection method and its parameters (e.g., "Outliers were identified using the 1.5x IQR rule").
- Report the number and percentage of observations flagged.
- Describe the disposition: how many were removed, corrected, or retained.
- Provide sensitivity analysis: show that main conclusions hold with and without outliers.
- Include in supplementary materials: full list of flagged observations and their disposition.
What it can do on your machine
Read from SKILL.md and the folder at commit bf44b3c. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are 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
Data Anomaly Detection loads about 1.7k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 402 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 402 words, ~1,694 tokens.
Download SKILL.mdSave it as
.claude/skills/data-anomaly-detection/SKILL.md (or your agent's skills folder).- name
- data-anomaly-detection
- description
- Detect anomalies and outliers in research data using statistical methods
Data Anomaly Detection
A skill for identifying anomalies, outliers, and suspicious patterns in research datasets. Combines classical statistical methods with modern machine learning approaches to flag data points that deviate significantly from expected distributions, helping researchers maintain data integrity and uncover genuine scientific findings.
Overview
Anomalous data points in research datasets can arise from measurement errors, instrument malfunction, data entry mistakes, or genuine rare phenomena. Distinguishing between these sources is critical: blindly removing outliers can bias results, while ignoring measurement errors introduces noise. This skill provides a structured framework for detecting, classifying, and handling anomalies in univariate, multivariate, and time-series research data.
The approach follows a three-stage pipeline: detection (flagging candidate anomalies), diagnosis (determining likely cause), and decision (remove, transform, or retain with justification). Every decision is logged for reproducibility and transparent reporting.
Statistical Detection Methods
Univariate Outlier Detection
import numpy as np
from scipy import stats
def detect_univariate_outliers(data: np.ndarray, method: str = 'iqr') -> dict:
"""
Detect outliers using classical univariate methods.
Methods:
'iqr': Interquartile range (1.5x IQR rule)
'zscore': Z-score threshold (|z| > 3)
'mad': Median absolute deviation (robust)
'grubbs': Grubbs' test for single outlier
"""
results = {'method': method, 'n_total': len(data)}
if method == 'iqr':
q1, q3 = np.percentile(data, [25, 75])
iqr = q3 - q1
lower, upper = q1 - 1.5 * iqr, q3 + 1.5 * iqr
mask = (data < lower) | (data > upper)
elif method == 'zscore':
z = np.abs(stats.zscore(data))
mask = z > 3
elif method == 'mad':
median = np.median(data)
mad = np.median(np.abs(data - median))
modified_z = 0.6745 * (data - median) / mad if mad > 0 else np.zeros_like(data)
mask = np.abs(modified_z) > 3.5
elif method == 'grubbs':
# Grubbs' test for the single most extreme value
n = len(data)
mean, sd = np.mean(data), np.std(data, ddof=1)
g = np.max(np.abs(data - mean)) / sd
t_crit = stats.t.ppf(1 - 0.05 / (2 * n), n - 2)
g_crit = ((n - 1) / np.sqrt(n)) * np.sqrt(t_crit**2 / (n - 2 + t_crit**2))
mask = np.abs(data - mean) / sd >= g_crit
results['outlier_indices'] = np.where(mask)[0].tolist()
results['n_outliers'] = int(mask.sum())
results['pct_outliers'] = round(mask.sum() / len(data) * 100, 2)
return resultsMultivariate Outlier Detection
from sklearn.covariance import EllipticEnvelope
from sklearn.ensemble import IsolationForest
def detect_multivariate_outliers(X: np.ndarray, method: str = 'mahalanobis') -> dict:
"""
Detect multivariate outliers using distance-based and model-based methods.
"""
if method == 'mahalanobis':
detector = EllipticEnvelope(contamination=0.05, random_state=42)
labels = detector.fit_predict(X) # -1 = outlier, 1 = inlier
elif method == 'isolation_forest':
detector = IsolationForest(
n_estimators=100, contamination=0.05, random_state=42
)
labels = detector.fit_predict(X)
outlier_mask = labels == -1
return {
'method': method,
'outlier_indices': np.where(outlier_mask)[0].tolist(),
'n_outliers': int(outlier_mask.sum()),
'contamination_assumed': 0.05
}Diagnosis Framework
Once candidate anomalies are flagged, classify each by likely cause:
| Category | Indicators | Action |
|---|---|---|
| Measurement error | Value physically impossible, instrument log shows malfunction | Remove with documentation |
| Data entry error | Obvious typo (e.g., extra digit), inconsistent units | Correct if source available, else remove |
| Sampling artifact | Unusual but plausible value from edge of population | Retain; use robust methods |
| Genuine extreme | Verified measurement, consistent with other variables | Retain; report sensitivity analysis |
| Contamination | Data from wrong population or experimental condition | Remove with justification |
Show full SKILL.md (180 more words)Show less
Diagnostic Checks
- Cross-variable consistency: Does the flagged value make sense given other columns for the same observation?
- Temporal context: For longitudinal data, is the spike consistent with known events?
- Instrument logs: Can the anomaly be traced to a calibration or equipment issue?
- Domain knowledge: Is the value within theoretically possible bounds?
Time-Series Anomaly Detection
def detect_timeseries_anomalies(series: np.ndarray, window: int = 20) -> dict:
"""
Detect anomalies in time-series data using rolling statistics.
"""
rolling_mean = pd.Series(series).rolling(window=window).mean()
rolling_std = pd.Series(series).rolling(window=window).std()
upper_bound = rolling_mean + 3 * rolling_std
lower_bound = rolling_mean - 3 * rolling_std
anomalies = (series > upper_bound) | (series < lower_bound)
return {
'anomaly_indices': np.where(anomalies)[0].tolist(),
'n_anomalies': int(anomalies.sum()),
'window_size': window
}Reporting Anomaly Handling
When reporting anomaly handling in publications:
- State the detection method and its parameters (e.g., "Outliers were identified using the 1.5x IQR rule").
- Report the number and percentage of observations flagged.
- Describe the disposition: how many were removed, corrected, or retained.
- Provide sensitivity analysis: show that main conclusions hold with and without outliers.
- Include in supplementary materials: full list of flagged observations and their disposition.
References
- Rousseeuw, P. J. & Hubert, M. (2011). Robust Statistics for Outlier Detection. WIREs Data Mining and Knowledge Discovery, 1(1), 73-79.
- Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. ICDM 2008.
- Aguinis, H., Gottfredson, R. K., & Joo, H. (2013). Best-Practice Recommendations for Defining, Identifying, and Handling Outliers. Organizational Research Methods, 16(2), 270-301.
© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/analysis/statistics/data-anomaly-detection of wentorai/research-plugins.
Open the folder on GitHubat commit bf44b3c
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Data 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 |
|---|---|---|---|---|---|---|
| Data Anomaly Detection this skillwentorai/research-plugins | 298 | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| 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 | 169 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 |
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Adds a new anomaly-detection model to anomalib under src/anomalib/models/.
Anomalib Tiled Ensemble
open-edge-platform/anomalib
Runs and configures the anomalib tiled-ensemble pipeline, which trains/evaluates one model per image tile and merges results (with optional seam smoothing) for high-resolution anomaly detection.
- Official
Kql
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KQL language expertise for writing correct, efficient Kusto queries using the Fabric RTI MCP tools.
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Related
Categories
Questions about Data Anomaly Detection
What does Data Anomaly Detection do?
Detect anomalies and outliers in research data using statistical methods. Data Anomaly Detection is an agent skill from wentorai/research-plugins.
When should I use Data Anomaly Detection?
Data Anomaly Detection fits situations like: tasks that involve Anomaly detection.
How do I install Data Anomaly Detection in Claude Code?
Run `npx skills add wentorai/research-plugins --skill data-anomaly-detection -a claude-code`. Or copy the skill folder (skills/analysis/statistics/data-anomaly-detection in wentorai/research-plugins) into .claude/skills/data-anomaly-detection in your project. Claude Code loads it when a task matches its description.
How do I install Data Anomaly Detection in Codex?
Run `npx skills add wentorai/research-plugins --skill data-anomaly-detection -a codex`. Or copy the skill folder (skills/analysis/statistics/data-anomaly-detection in wentorai/research-plugins) into .agents/skills/data-anomaly-detection in your project. Codex loads it when a task matches its description.
Can I use Data 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 wentorai/research-plugins --skill data-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/data-anomaly-detection, .gemini/skills/data-anomaly-detection, .github/skills/data-anomaly-detection and .opencode/skills/data-anomaly-detection in your project.
What does Data Anomaly Detection need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Anomaly Detection is instructions for the agent only. Our summary lists: Python 3.
Does Data 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 Data 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. Review the folder before installing.
What licence does Data Anomaly Detection use?
Data Anomaly Detection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Data Anomaly Detection use?
About 1.7k tokens (SKILL.md is roughly 6.8k 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 Data Anomaly Detection?
Skills that share tags, products or a category with Data 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 Data Anomaly Detection?
wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.
Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.