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Found 1,832 Skills
Configure Harness AI-powered operations (AIDA) via MCP. Set up predictive failure analysis with ML models for memory leaks, disk exhaustion, connection pool saturation, and latency degradation. Configure intelligent alert correlation and noise reduction to reduce alert volume. Use when asked to set up predictive failure analysis, configure AI-powered alerting, reduce alert noise, or enable ML-based anomaly detection. Do NOT use for pipeline debugging (use debug-pipeline instead) or SLO management (use manage-slos instead). Trigger phrases: AIDA, predictive failure, alert correlation, noise reduction, anomaly detection, AI ops, predictive analysis, alert fatigue, ML alerting, intelligent alerting.
Triage and orchestrate code reviews. Analyzes PR intent, identifies touched surfaces, assesses risk, and routes to specialist skills. Does NOT perform detailed review - delegates to specialists. Supports full pipeline with "Review PR <number>" command.
Build end-to-end ETL pipelines with Harvard Art Museums API, SQL analytics, and Streamlit visualization
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrain), Phase 3 AutoML refinement on the DEFT-augmented dataset. This is the default entry point for any "run the AOI workflow", "fine-tune my PCB AOI model end-to-end", "improve my AOI ChangeNet model", or "AOI workflow with AutoML" request — route here instead of tao-run-deft-aoi directly unless the user explicitly asks for the DEFT loop ONLY (e.g. "run JUST the DEFT loop", "skip AutoML, only DEFT"). Also handles the same three-phase pattern for non-AOI DEFT applications — AutoML baseline then DEFT loop warm-started from AutoML's winning HPs then post-DEFT AutoML refinement on the iteration-augmented dataset. Trigger phrases include "run the AOI workflow", "AOI end-to-end", "AutoML + DEFT", "AutoML then DEFT", "tune hyperparameters then DEFT", "DEFT with AutoML at both ends", "warm-start DEFT", "improve my AOI model".
Run a daily content digest pipeline that fetches Podcast RSS feeds and YouTube channels, transcribes audio, generates AI summaries in Traditional Chinese, and optionally sends notifications via Telegram. Use this skill whenever the user wants to: set up or run a daily digest, summarize podcasts or YouTube videos, create a content briefing, aggregate RSS/YouTube content, or build an automated summary system. Also triggers for: "daily digest", "每日摘要", "podcast 摘要", "YouTube 整理", "跑摘要", "內容彙整", "幫我整理今天的 podcast", "summarize my subscriptions", or any request to periodically collect and summarize media content. Even if the user just says "digest" or "摘要", check if this skill applies.
Write game shaders from cross-engine fundamentals — the vertex→fragment pipeline, coordinate spaces, UV math, and common 2D/3D effects (tint, UV scroll, dissolve, outline, fresnel rim, vignette) in GLSL with HLSL equivalents. Use when the user mentions shaders, fragment/pixel shader, vertex shader, UV, GLSL, HLSL, or effects like dissolve, outline, or rim light.
Build Unity games with optimized C# scripts, efficient rendering, and proper asset management. Masters Unity 6 LTS, URP/HDRP pipelines, and cross-platform deployment. Handles gameplay systems, UI implementation, and platform optimization. Use PROACTIVELY for Unity performance issues, game mechanics, or cross-platform builds.
End-to-end retail ETL pipeline using PySpark, SQL Server, and Medallion Architecture (Bronze/Silver/Gold layers) for data warehousing
Use this skill when orchestrating multi-agent work at scale - research swarms, parallel feature builds, wave-based dispatch, build-review-fix pipelines, or any task requiring 3+ agents. Activates on mentions of swarm, parallel agents, multi-agent, orchestrate, fan-out, wave dispatch, research army, unleash, dispatch agents, or parallel work.
Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end.
Exactly-once processing semantics with distributed coordination for file-based data pipelines. Atomic file claiming, status tracking, and automatic retry with in-memory fallback.
Complete CI/CD guide for Capacitor apps covering GitHub Actions, GitLab CI, build automation, app signing, and deployment pipelines. Use this skill when users need to automate their build and release process.