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Found 1,668 Skills
WebGPU fundamentals for high-performance canvas rendering. Covers device initialization, buffer management, WGSL shaders, render pipelines, compute shaders, and web component integration. Use when building GPU-accelerated graphics, particle systems, or compute-intensive visualizations.
Automate Zoho Bigin tasks via Rube MCP (Composio): pipelines, contacts, companies, products, and small business CRM. Always search tools first for current schemas.
When the user wants to turn content into revenue, build a content-led GTM motion, reverse engineer distribution, or repurpose content across platforms. Also use when the user mentions 'content marketing,' 'content-led growth,' 'content to pipeline,' 'distribution,' 'content repurposing,' 'content strategy,' 'thought leadership,' 'newsletter,' 'content flywheel,' 'organic growth.' This skill covers content-to-revenue systems from creation through pipeline attribution.
Secure 1Password CLI patterns for reading secrets, discovering vaults/items, and piping credentials to other tools. Use when reading from 1Password, rotating secrets, or piping credentials to wrangler/kubectl/etc. Triggers on op CLI, 1Password, secret rotation, or credential piping tasks.
Analyzes clinical trial protocols and generates CDISC-compliant (SDTM/ADaM) data schemas. Use when designing data ingestion pipelines for clinical research or preparing regulatory submissions.
Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.
Use when building revenue analytics on HubSpot — SQL warehouse queries, API enrichment pipelines, lead scoring models, pipeline forecasting, competitive intelligence. Triggers on "hubspot analytics", "revops dashboard", "lead scoring", "pipeline forecast", "ICP analysis", "hubspot SQL".
Observability and monitoring for data pipelines using OpenTelemetry (traces) and Prometheus (metrics). Covers instrumentation, dashboards, and alerting.
Professional DevOps engineering skill for creating CI/CD pipelines, implementing infrastructure as code, managing environments, and establishing monitoring and observability across all deployment stages.
Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。
Walk through omicverse's single-cell preprocessing tutorials to QC PBMC3k data, normalise counts, detect HVGs, and run PCA/embedding pipelines on CPU, CPU–GPU mixed, or GPU stacks.
Use this skill when building real-time or near-real-time data pipelines. Covers Kafka, Flink, Spark Streaming, Snowpipe, BigQuery streaming, materialized views, and batch-vs-streaming decisions. Common phrases: "real-time pipeline", "Kafka consumer", "streaming vs batch", "low latency ingestion". Do NOT use for batch integration patterns (use integration-patterns-skill) or pipeline orchestration (use data-orchestration-skill).