analyzing-time-series
Comprehensive diagnostic analysis of time series data. Use when users provide CSV time series data and want to understand its characteristics before forecasting - stationarity, seasonality, trend, forecastability, and transform recommendations.
Best use case
analyzing-time-series is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Comprehensive diagnostic analysis of time series data. Use when users provide CSV time series data and want to understand its characteristics before forecasting - stationarity, seasonality, trend, forecastability, and transform recommendations.
Teams using analyzing-time-series should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-time-series/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-time-series Compares
| Feature / Agent | analyzing-time-series | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Comprehensive diagnostic analysis of time series data. Use when users provide CSV time series data and want to understand its characteristics before forecasting - stationarity, seasonality, trend, forecastability, and transform recommendations.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Time Series Diagnostics
Comprehensive diagnostic toolkit to analyze time series data characteristics before forecasting.
## Input Format
The input CSV file should have two columns:
- **Date column** - Timestamps or dates (e.g., `date`, `timestamp`, `time`)
- **Value column** - Numeric values to analyze (e.g., `value`, `sales`, `temperature`)
## Workflow
**Step 1: Run diagnostics**
```bash
python scripts/diagnose.py data.csv --output-dir results/
```
This runs all statistical tests and analyses. Outputs `diagnostics.json` with all metrics and `summary.txt` with human-readable findings. Column names are auto-detected, or can be specified with `--date-col` and `--value-col` options.
**Step 2: Generate plots (optional)**
```bash
python scripts/visualize.py data.csv --output-dir results/
```
Creates diagnostic plots in `results/plots/` for visual inspection. Run after `diagnose.py` to ensure ACF/PACF plots are synchronized with stationarity results. Column names are auto-detected, or can be specified with `--date-col` and `--value-col` options.
**Step 3: Report to user**
Summarize findings from `summary.txt` and present relevant plots. See `references/interpretation.md` for guidance on:
- Is the data forecastable?
- Is it stationary? How much differencing is needed?
- Is there seasonality? What period?
- Is there a trend? What direction?
- Is a transform needed?
## Script Options
Both scripts accept:
- `--date-col NAME` - Date column (auto-detected if omitted)
- `--value-col NAME` - Value column (auto-detected if omitted)
- `--output-dir PATH` - Output directory (default: `diagnostics/`)
- `--seasonal-period N` - Seasonal period (auto-detected if omitted)
## Output Files
```
results/
├── diagnostics.json # All test results and statistics
├── summary.txt # Human-readable findings
├── diagnostics_state.json # Internal state for plot synchronization
└── plots/
├── timeseries.png
├── histogram.png
├── rolling_stats.png
├── box_by_dayofweek.png # By day of week (if applicable)
├── box_by_month.png # By month (if applicable)
├── box_by_quarter.png # By quarter (if applicable)
├── acf_pacf.png
├── decomposition.png
└── lag_scatter.png
```
## References
See `references/interpretation.md` for:
- Statistical test thresholds and interpretation
- Seasonal period guidelines by data frequency
- Transform recommendations
## Dependencies
`pandas`, `numpy`, `matplotlib`, `statsmodels`, `scipy`Related Skills
tracking-application-response-times
Track and optimize application response times across API endpoints, database queries, and service calls. Use when monitoring performance or identifying bottlenecks. Trigger with phrases like "track response times", "monitor API performance", or "analyze latency".
timeout-handler
Timeout Handler - Auto-activating skill for API Integration. Triggers on: timeout handler, timeout handler Part of the API Integration skill category.
forecasting-time-series-data
This skill enables Claude to forecast future values based on historical time series data. It analyzes time-dependent data to identify trends, seasonality, and other patterns. Use this skill when the user asks to predict future values of a time series, analyze trends in data over time, or requires insights into time-dependent data. Trigger terms include "forecast," "predict," "time series analysis," "future values," and requests involving temporal data.
time-series-decomposer
Time Series Decomposer - Auto-activating skill for Data Analytics. Triggers on: time series decomposer, time series decomposer Part of the Data Analytics skill category.
analyzing-system-throughput
This skill enables Claude to analyze and optimize system throughput. It is triggered when the user requests throughput analysis, performance improvements, or bottleneck identification. The skill uses the `throughput-analyzer` plugin to assess request throughput, data throughput, concurrency limits, queue processing, and resource saturation. Use this skill when the user mentions "analyze throughput", "optimize performance", "identify bottlenecks", or asks about system capacity. It helps determine limiting factors and evaluate scaling strategies.
analyzing-text-sentiment
This skill enables Claude to analyze the sentiment of text data. It identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. Use this skill when a user requests sentiment analysis, opinion mining, or emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include "sentiment analysis", "analyze sentiment", "opinion mining", "emotion detection", and "polarity".
analyzing-security-headers
This skill analyzes HTTP security headers of a given domain to identify potential vulnerabilities and misconfigurations. It provides a detailed report with a grade, score, and recommendations for improvement. Use this skill when the user asks to "analyze security headers", "check HTTP security", "scan for security vulnerabilities", or requests a "security audit" of a website. It will automatically activate when security-related keywords are used in conjunction with domain names or URLs.
response-time-analyzer
Response Time Analyzer - Auto-activating skill for Performance Testing. Triggers on: response time analyzer, response time analyzer Part of the Performance Testing skill category.
analyzing-query-performance
This skill enables Claude to analyze and optimize database query performance. It activates when the user discusses query performance issues, provides an EXPLAIN plan, or asks for optimization recommendations. The skill leverages the query-performance-analyzer plugin to interpret EXPLAIN plans, identify performance bottlenecks (e.g., slow queries, missing indexes), and suggest specific optimization strategies. It is useful for improving database query execution speed and resource utilization.
analyzing-text-with-nlp
This skill enables Claude to perform natural language processing and text analysis using the nlp-text-analyzer plugin. It should be used when the user requests analysis of text, including sentiment analysis, keyword extraction, topic modeling, or other NLP tasks. The skill is triggered by requests involving "analyze text", "sentiment analysis", "keyword extraction", "topic modeling", or similar phrases related to text processing. It leverages AI/ML techniques to understand and extract insights from textual data.
analyzing-network-latency
This skill enables Claude to analyze network latency and optimize request patterns within an application. It helps identify bottlenecks and suggest improvements for faster and more efficient network communication. Use this skill when the user asks to "analyze network latency", "optimize request patterns", or when facing performance issues related to network requests. It focuses on identifying serial requests that can be parallelized, opportunities for request batching, connection pooling improvements, timeout configuration adjustments, and DNS resolution enhancements. The skill provides concrete suggestions for reducing latency and improving overall network performance.
analyzing-logs
This skill enables Claude to analyze logs for performance insights and issue detection. It is triggered when the user requests log analysis, performance troubleshooting, or debugging assistance. The skill identifies slow requests, error patterns, resource warnings, and other key performance indicators within log files. Use this skill when the user mentions "analyze logs", "performance issues", "error patterns in logs", "slow requests", or requests help with "log aggregation". It helps identify performance bottlenecks and improve application stability by analyzing log data.