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SkillAtlasSkill 详情
detecting-data-anomalies
A model-agnostic agent-skills platform.
审核状态：已审核Quality 72Security 70
项目 README
来源文件：README.md
Tons of Skills
A model-agnostic agent-skills platform. The canonical layer is harness-free by construction; Claude Code is currently the verified-native harness. Other harnesses remain engineering candidates until their native-path integration is verified; source research alone is never presented as public support.
Version semantics: the release badge is this marketplace's display version. npm packages, including the
ccpiCLI and publishable plugins, retain their own package versions; they are intentionally not expected to equal the display version. The version-surface checker governs the display surfaces without rewriting package semver.
Install
Inside Claude Code, one command installs the whole marketplace:
/plugin marketplace add jeremylongshore/claude-code-plugins
Or use the CLI:
pnpm add -g @intentsolutionsio/ccpi
ccpi install devops-automation-pack
Browse the marketplace · Explore plugins · Download bundles
Killer Skill of the Week — no-ai-slop by Peter Yang
Strip AI slop from any draft — named-pattern edits that keep the writer's real voice
no-ai-slop does two jobs and refuses to fake a third. In Edit mode it makes the minimum effective edit — cutting throat-clearing, weak verbs, and abstract nouns while deliberately preserving the writer's cadence, bluntness, humor, and honest admissions, so a rough draft still sounds like the same person afterward. In Detect mode it names each AI-slop pattern it finds, quotes the offending line, and gives the fix in a few words — and pointedly does NOT score the draft or guess whether an AI wrote it. That restraint is the whole point: AI detectors guess; named patterns are evidence the reader can check. MIT-licensed, single focused skill, actively maintained by Peter Yang.
"AI detectors guess. Named patterns are evidence the user can check." — Peter Yang
Grade: A | Week of July 22, 2026 (W30) | View on GitHub
Previous picks: tonone, mnemos, databricks-pack, kobiton-automate, skyvern, code-cleanup, web-analytics, token-optimizer, executive-assistant-skills, skill-creator, cursor-pack, crypto-portfolio-tracker. See all at tonsofskills.com.
Scale, labeled
Every number below names the cohort it counts and the command that reproduces it — an unlabeled count is how a corpus ends up with five contradictory answers to "how many skills."
| Count | Cohort | Reproduce with |
|---|---|---|
| 442 | catalog plugins (catalog-entry cohort) | node scripts/generate-readme-toc.mjs over marketplace.extended.json |
| 3,067 | marketplace-visible skills (distinct) | node -e "import('./scripts/corpus-resolver.mjs').then(m=>console.log(m.resolveCorpus('marketplace-visible').length))" |
| 347 | agent definitions in plugins | git ls-files 'plugins/**' | grep '/agents/.*\.md' |
| 19 | plugin categories | ls -d plugins/*/ |
📦 Live npm Downloads
Across 396 published packages in the claude-code-plugins namespace. Updated daily by GitHub Actions.
| Window | All packages | Established (>30d) |
|---|---|---|
| Last 24 hours | 962 | 962 |
| Last 7 days | 2,920 | 2,916 |
| Last 30 days | 12,868 | 12,779 |
"Established" excludes packages first published within the last 30 days, so a bulk-publish event doesn't dominate the headline.
Top 10 by last 30 days:
Last refreshed 2026-08-19T03:03:05.709Z.
Ways in
Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:
- By category — the table below, regenerated from the catalog on every sync.
- By plugin — tonsofskills.com/explore, the full browsable catalog.
- By skill — tonsofskills.com/skills, searchable across the whole corpus.
- By job to be done — tonsofskills.com/cowork, curated bundles as one-click downloads.
- By certification tier — the Certification section below, rendered from the live report.
Browse by category
The 19 categories below link into the live marketplace. Plugin counts are the catalog-entry cohort — regenerated from marketplace.extended.json by this generator; the catalog itself lives on tonsofskills.com, never in this file (§ 6A of the platform blueprint).
| Category | Plugins | |
|---|---|---|
| 🤖 | AI & Machine Learning | 36 |
| 🎭 | AI Agents & Agency | 10 |
| 🔌 | API Development | 26 |
| 💼 | Business Tools | 6 |
| 👥 | Community | 21 |
| ₿ | Crypto & Web3 | 27 |
| 💾 | Database | 26 |
| 🎨 | Design | 2 |
| 🔧 | DevOps & Infrastructure | 36 |
| 📚 | Examples & Templates | 5 |
| 🧩 | MCP Servers | 16 |
| 📦 | Packages | 5 |
| ⚡ | Performance | 25 |
| ✅ | Productivity | 30 |
| 🎁 | SaaS Skill Packs | 106 |
| 🔐 | Security | 27 |
| ✨ | Skill Enhancers | 9 |
| 🧪 | Testing | 28 |
| 📁 | Analytics | 1 |
What the classes mean
Four artifact classes live in this repository, distinguished on sight and never blurred — provenance is a truth requirement here, not a UX nicety:
| Class | What it is | How the reader can tell |
|---|---|---|
| Canonical skill | First-party, harness-free, the source of truth | No .source.json in its plugin directory |
| Generated adapter | A thin, machine-produced harness projection | Lives under a generated path with a "generated — do not edit" header |
| First-party package | An Intent Solutions distribution (npm, cowork zip) | @intentsolutionsio scope, IS-authored license |
| Upstream mirror | Somebody else's work, hosted mirror-by-default | .source.json present — upstream author, license, and pinned commit recorded |
Certification
Not yet certified. The certification program (tiers T0–T4 with retained, hash-matched evidence) is a later epic of the platform blueprint; until its report exists, no artifact on this surface claims a tier. This line is rendered from the absence of certification-report.json — honestly, not cosmetically.
Contribute
Start with the contribution guide, then the intake and review standards every submission passes through:
- Contribution requirements — including the AI-assistance disclosure expectation.
- Skill submission intake standard — the tiered document matrix new plugins ship with.
- External-PR review standard — how maintainers triage and what gets a submission merged.
Governance
- STANDARDS.md — the public spec posture and the canonical-documents index (one owner per fact class).
- The platform master blueprint — CI gates, release, docs governance, and this README's own landing contract.
- GOVERNANCE.md · SECURITY.md · LICENSE
Provenance
External plugins are hosted mirror-by-default: the contributor's repository stays the source of truth, every mirrored source is pinned in a content lockfile, and upstream credit — author, license, resolved commit — is recorded in the mirror itself. Improvements flow by upstreaming to the author's repository, never by silently editing the mirror. The full decision record is the external-sync model.
License
MIT for the repository scaffolding and first-party tooling; each plugin carries its own license in its manifest, and mirrored plugins keep their upstream license verbatim.
数据与 AI
中风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：3 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/detecting-data-anomalies" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/detecting-data-anomalies" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/detecting-data-anomalies" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/detecting-data-anomalies" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
- 将 "skills/.curated/detecting-data-anomalies" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
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
version: 1.26.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
tags:
- ai
- ml
- detecting-data
compatibility: Designed for Claude CodeDetecting 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
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
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