GitHub
data-engineer
Build ETL pipelines, data warehouses, and streaming architectures. Implements Spark jobs, Airflow DAGs, and Kafka streams. Use PROACTIVELY for data pipeline design or analytics infrastructure.
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数据库 AI 技能
发现 数据库 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
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GitHub
kleva-business
Specialized knowledge for working with Kleva business operations, including product development, sales sequences, LinkedIn campaigns, financial models, and business documentation. Use when working on any Kleva-...
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GitHub
payment-integration
Integrate Stripe, PayPal, and payment processors. Handles checkout flows, subscriptions, webhooks, and PCI compliance. Use PROACTIVELY when implementing payments, billing, or subscription features.
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GitHub
network-engineer
Debug network connectivity, configure load balancers, and analyze traffic patterns. Handles DNS, SSL/TLS, CDN setup, and network security. Use PROACTIVELY for connectivity issues, network optimization, or proto...
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GitHub
legal-advisor
Draft privacy policies, terms of service, disclaimers, and legal notices. Creates GDPR-compliant texts, cookie policies, and data processing agreements. Use PROACTIVELY for legal documentation, compliance texts...
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GitHub
mobile-developer
Develop React Native or Flutter apps with native integrations. Handles offline sync, push notifications, and app store deployments. Use PROACTIVELY for mobile features, cross-platform code, or app optimization.
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GitHub
customer-support
Handle support tickets, FAQ responses, and customer emails. Creates help docs, troubleshooting guides, and canned responses. Use PROACTIVELY for customer inquiries or support documentation.
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GitHub
cpp-pro
Write idiomatic C++ code with modern features, RAII, smart pointers, and STL algorithms. Handles templates, move semantics, and performance optimization. Use PROACTIVELY for C++ refactoring, memory safety, or c...
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GitHub
context-manager
Manages context across multiple agents and long-running tasks. Use when coordinating complex multi-agent workflows or when context needs to be preserved across sessions. Essential for projects exceeding 10k tok...
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GitHub
working-with-resources
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...
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GitHub
aggregating-event-datasets
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_...
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GitHub
window-functions-deep-dive
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...
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GitHub
detecting-anomalies
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...
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GitHub
field-extraction-parsing
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...
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GitHub
analyzing-apm-data
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...
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GitHub
analyzing-tdigest-metrics
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...
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GitHub
filtering-event-datasets
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...
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GitHub
working-with-intervals
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...
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GitHub
working-with-reference-tables
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...
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GitHub
aggregating-gauge-metrics
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...
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GitHub
time-series-analysis
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...
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GitHub
analyzing-text-patterns
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...
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GitHub
subquery-patterns-and-union
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...
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GitHub
ace-pattern-learning
Search ACE playbook before implementing, building, fixing, debugging, or refactoring code. Capture patterns after completing substantial coding work.
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默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。