Browse published AI skills maintained by rustomax. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
14Repository stars across indexed skills
14Repository forks
14Average repository stars per indexed skill
1Repository stars may repeat across skills from the same repository.
Work with Resource datasets (mutable state tracking) using OPAL temporal joins. Use when you need to enrich Events/Intervals with contextual state information, track resource state changes over time, or navigat...
Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_...
Master OPAL window functions for row-relative calculations, rankings, and moving aggregates. Covers lag(), lead(), row_number(), rank(), dense_rank(), moving averages, first(), and last(). Use when comparing ro...
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier dete...
Extract structured fields from unstructured log data using OPAL parsing functions. Covers extract_regex() for pattern matching with type casting, split() for delimited data, parse_json() for JSON logs, and JSON...
Monitor application performance using the RED methodology (Rate, Errors, Duration) with Observe. Use when analyzing service health, investigating errors, tracking latency, or building APM dashboards. Covers whe...
Analyze percentile metrics (tdigest type) using OPAL for latency analysis and SLO tracking. Use when calculating p50, p95, p99 from pre-aggregated duration or latency metrics. Covers the critical double-combine...
Filter and search event datasets (logs) using OPAL. Use when you need to find specific log events by text search, regex patterns, or field values. Covers contains(), tilda operator ~, field comparisons, boolean...
Work with Interval datasets (time-bounded data) using OPAL. Use when analyzing data with start and end timestamps like distributed traces, batch jobs, or CI/CD pipeline runs. Covers duration calculations, tempo...
Work with Reference Tables (static CSV lookup data) using OPAL to enrich datasets with descriptive information. Use when you need to map IDs to human-readable names, add static metadata from CSV uploads, or per...
Aggregate pre-computed metrics (gauge, counter, delta types) using OPAL. Use when analyzing request counts, error rates, resource utilization, or any numeric metrics over time. Covers align + m() + aggregate pa...
Analyze event datasets (logs) and intervals over time using OPAL timechart. Use when you need to visualize trends, track metrics over time, or create time-series charts. Covers timechart for temporal binning, b...
Extract and analyze recurring patterns from log messages, span names, and event names using punctuation-based template discovery. Use when you need to understand log diversity, identify common message structure...
Use OPAL subquery syntax (@labels) and union operations to combine multiple datasets or time periods. Essential for period-over-period comparisons, multi-dataset analysis, and complex data transformations. Cove...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.