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.
Process identify anomalies and outliers in datasets using machine learning algorithms.
Install Detecting Data Anomalies
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace detecting-data-anomalies --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/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/detecting-data-anomalies .claude/skills/detecting-data-anomalies && 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 "detecting-data-anomalies" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/detecting-data-anomalies into .claude/skills/detecting-data-anomalies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-data-anomalies", 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/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/detecting-data-anomaliesType 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 jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace detecting-data-anomalies --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.curated/detecting-data-anomalies .agents/skills/detecting-data-anomalies && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "detecting-data-anomalies" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/detecting-data-anomalies into .agents/skills/detecting-data-anomalies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-data-anomalies", 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 jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace detecting-data-anomalies --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.curated/detecting-data-anomalies .cursor/skills/detecting-data-anomalies && 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 "detecting-data-anomalies" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/detecting-data-anomalies into .cursor/skills/detecting-data-anomalies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-data-anomalies", 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/jeremylongshore/tons-of-skills-marketplace.git --path skills/.curated/detecting-data-anomalies--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 jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace detecting-data-anomalies --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.curated/detecting-data-anomalies .gemini/skills/detecting-data-anomalies && 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 "detecting-data-anomalies" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/detecting-data-anomalies into .gemini/skills/detecting-data-anomalies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-data-anomalies", 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 jeremylongshore/tons-of-skills-marketplace detecting-data-anomaliesInstalls 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 jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.curated/detecting-data-anomalies .github/skills/detecting-data-anomalies && 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 "detecting-data-anomalies" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/detecting-data-anomalies into .github/skills/detecting-data-anomalies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-data-anomalies", 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 jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace detecting-data-anomalies --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.curated/detecting-data-anomalies .opencode/skills/detecting-data-anomalies && 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 "detecting-data-anomalies" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/detecting-data-anomalies into .opencode/skills/detecting-data-anomalies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-data-anomalies", 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
detecting-data-anomalies- GitHub stars
- 2.8k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 600 words
- Files
- 7 (incl. scripts, references, assets)
- Skills in repo
- 3,342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Process identify anomalies and outliers in datasets using machine learning algorithms.
- Works in 10 steps: Load the dataset using the Read tool and… → Profile feature distributions using… → Handle missing values via imputation… → …
- Analyzing data for unusual patterns
- SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
- Calls pip
What it does
Detecting Data Anomalies is an agent skill from jeremylongshore/tons-of-skills-marketplace. Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts, reference files and assets (for example `assets/README.md`, `references/README.md` and `references/errors.md`). Compatibility notes: Designed for Claude Code
It sits in Data & Analytics, covering Anomaly detection. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.
When your agent uses it
- Analyzing data for unusual patterns
- Unexpected deviations from normal behavior
- With phrases like detect anomalies
- Identify unusual patterns
Example prompts
- “detect anomalies”
- “find outliers”
- “identify unusual patterns”
- “/detecting-data-anomalies”
Requirements
- Python 3
- Compatibility (from SKILL.md): Designed for Claude Code
- Pre-approved tools (allowed-tools): Read, Bash(python:*), Grep, Glob
Workflow steps
10 steps, taken from the first numbered list in SKILL.md.
- Load the dataset using the Read tool and verify schema, column types, and row count
- Profile feature distributions using descriptive statistics to understand baseline behavior
- Handle missing values via imputation (median for numeric, mode for categorical) or row exclusion
- Apply StandardScaler or MinMaxScaler to numeric features to normalize magnitude differences
- Select the detection algorithm based on data characteristics
- Set the contamination parameter to the expected anomaly proportion (start with 0.01-0.05)
- Fit the model on the training partition and generate anomaly scores for each data point
- Apply the decision threshold to classify points as normal (-1) or anomalous (1)
- Analyze flagged anomalies for common characteristics, temporal clusters, or feature correlations
- Generate a summary report with detection counts, score distributions, and visualization plots
What it can do on your machine
Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
ReadBash(python:*)GrepGlob
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
scikit-learn.orgpyod.readthedocs.io
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.
Compatibility
Designed for Claude Code
From compatibility in the SKILL.md frontmatter.
Context cost
Detecting Data Anomalies loads about 1.4k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 600 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 jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 600 words, ~1,391 tokens.
Download SKILL.mdSave it as
.claude/skills/detecting-data-anomalies/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- detecting-data-anomalies
- description
- Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".
- allowed-tools
- Read, Bash(python:*), Grep, Glob
- compatibility
- Designed for Claude Code
- version
- 1.26.0
- author
- Jeremy Longshore <jeremy@intentsolutions.io>
- license
- MIT
- tags
- ai, ml, detecting-data
Detecting Data Anomalies
Overview
Identify anomalies and outliers in datasets using statistical and machine learning algorithms including Isolation Forest, One-Class SVM, Local Outlier Factor, and autoencoders. This skill handles the full detection pipeline from data ingestion and feature scaling through algorithm selection, threshold tuning, and result interpretation with anomaly scoring.
Prerequisites
- Python 3.9+ with scikit-learn >= 1.3 (
pip install scikit-learn) - pandas and NumPy for data manipulation (
pip install pandas numpy) - matplotlib or seaborn for anomaly visualizations (
pip install matplotlib seaborn) - Dataset in CSV, JSON, Parquet, or database-queryable format
- Minimum 500 data points for statistical significance (1000+ recommended)
- Optional: PyTorch or TensorFlow for autoencoder-based detection on complex patterns
Instructions
- Load the dataset using the Read tool and verify schema, column types, and row count
- Profile feature distributions using descriptive statistics to understand baseline behavior
- Handle missing values via imputation (median for numeric, mode for categorical) or row exclusion
- Apply StandardScaler or MinMaxScaler to numeric features to normalize magnitude differences
- Select the detection algorithm based on data characteristics:
- Isolation Forest: high-dimensional data, no assumptions on distribution
- One-Class SVM: well-defined normal class with clear decision boundary
- Local Outlier Factor: density-varying data with local anomaly patterns
- Autoencoder: complex temporal or image data with non-linear relationships
- Set the contamination parameter to the expected anomaly proportion (start with 0.01-0.05)
- Fit the model on the training partition and generate anomaly scores for each data point
- Apply the decision threshold to classify points as normal (-1) or anomalous (1)
- Analyze flagged anomalies for common characteristics, temporal clusters, or feature correlations
- Generate a summary report with detection counts, score distributions, and visualization plots
See ${CLAUDE_SKILL_DIR}/references/implementation.md for the detailed implementation guide.
Output
- Anomaly detection summary: total points, anomaly count, contamination rate
- Per-record anomaly scores with classification labels
- Algorithm configuration: model type, contamination, distance metric, threshold
- Feature importance ranking showing which dimensions drive anomaly flags
- Visualization: scatter plot of anomaly scores, distribution histogram, t-SNE cluster plot
- CSV export of flagged records with anomaly scores and contributing features
Show full SKILL.md (265 more words)Show less
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Insufficient data volume | Fewer than 100 data points for model fitting | Collect additional data or switch to simple statistical methods (z-score, IQR) |
| High false positive rate | Contamination parameter set too high or features not scaled | Lower contamination to 0.01; verify StandardScaler applied; refine feature selection |
| Algorithm OOM on large dataset | Isolation Forest or LOF exceeds available memory | Subsample data for training; use max_samples parameter; switch to streaming approach |
| Feature scaling mismatch | Mixed numeric and categorical features without proper encoding | One-hot encode categoricals separately; scale numeric features independently |
| No ground truth for validation | Unlabeled dataset prevents accuracy measurement | Use domain expert review on top-N anomalies; implement feedback loop to refine threshold |
See ${CLAUDE_SKILL_DIR}/references/errors.md for the full error reference.
Examples
Scenario 1: Network Intrusion Detection -- Apply Isolation Forest to 50K network flow records with features: packet count, byte volume, duration, protocol type. Expected contamination: 2%. Target: flag port-scan and DDoS patterns with precision above 0.85.
Scenario 2: Manufacturing Quality Control -- Run LOF on sensor readings (temperature, vibration, pressure) from 10K production cycles. Detect equipment degradation anomalies. Visualize flagged cycles on a time-series plot with normal operating bands.
Scenario 3: Financial Transaction Monitoring -- Train an autoencoder on 100K legitimate transactions. Reconstruct test transactions and flag those with reconstruction error above the 99th percentile. Report flagged transactions with amount, merchant category, and time-of-day features.
Resources
- scikit-learn Anomaly Detection -- Isolation Forest, LOF, One-Class SVM
- PyOD Library -- 40+ outlier detection algorithms with unified API
- Autoencoder anomaly detection: Keras/PyTorch reconstruction-error approach
- Feature scaling: StandardScaler, RobustScaler, MinMaxScaler selection guide
- Evaluation without labels: silhouette analysis, domain expert review protocols
© jeremylongshore, MIT. 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 6 other files (scripts, references, assets) in skills/.curated/detecting-data-anomalies of jeremylongshore/tons-of-skills-marketplace.
- SKILL.md
- assets/README.md
- references/README.md
- references/errors.md
- references/examples.md
- references/implementation.md
- scripts/README.md
Open the folder on GitHubat commit cfae287
Compare with similar skills
Detecting Data Anomalies 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 |
|---|---|---|---|---|---|---|
| Detecting Data Anomalies this skilljeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.4k | 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 | 171 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 |
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Anomalib Tiled Ensemble
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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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Related
Categories
Questions about Detecting Data Anomalies
What does Detecting Data Anomalies do?
Process identify anomalies and outliers in datasets using machine learning algorithms. Detecting Data Anomalies is an agent skill from jeremylongshore/tons-of-skills-marketplace. Process identify anomalies and outliers in datasets using machine learning algorithms.
When should I use Detecting Data Anomalies?
Detecting Data Anomalies fits situations like: analyzing data for unusual patterns; unexpected deviations from normal behavior; with phrases like detect anomalies; identify unusual patterns.
How do I install Detecting Data Anomalies in Claude Code?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a claude-code`. Or copy the skill folder (skills/.curated/detecting-data-anomalies in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/detecting-data-anomalies in your project. Claude Code loads it when a task matches its description.
How do I install Detecting Data Anomalies in Codex?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a codex`. Or copy the skill folder (skills/.curated/detecting-data-anomalies in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/detecting-data-anomalies in your project. Codex loads it when a task matches its description.
Can I use Detecting Data Anomalies 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 jeremylongshore/tons-of-skills-marketplace --skill detecting-data-anomalies -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-data-anomalies, .gemini/skills/detecting-data-anomalies, .github/skills/detecting-data-anomalies and .opencode/skills/detecting-data-anomalies in your project.
What does Detecting Data Anomalies need to run?
Going by SKILL.md and its folder, Detecting Data Anomalies needs the command-line tools its instructions call (pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Bash(python:*), Grep, Glob. Compatibility (from SKILL.md): Designed for Claude Code.
Does Detecting Data Anomalies access the network?
SKILL.md names 2 domains. As links in the text: scikit-learn.org and pyod.readthedocs.io. This is read from the text; nothing was executed.
Is Detecting Data Anomalies 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 Detecting Data Anomalies use?
Detecting Data Anomalies is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Detecting Data Anomalies use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4k tokens, read only when the agent opens those files.
What are the alternatives to Detecting Data Anomalies?
Skills that share tags, products or a category with Detecting Data Anomalies: 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 Detecting Data Anomalies?
jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.
Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.