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Found 1,653 Skills
Chain multiple AI steps into one reliable pipeline. Use when your AI task is too complex for one prompt, you need to break AI logic into stages, combine classification then generation, do multi-step reasoning, build a compound AI system, orchestrate multiple models, or wire AI components together. Powered by DSPy multi-module pipelines.
Daily compression of time-series data with merge logic for multiple pipeline runs, structured aggregation for dashboards, and storage estimation for capacity planning.
Deterministic AI engineering workflow with multi-agent teams. Triggers: architect mode, consistency sweep, pipeline audit, team workflow
Vertex Ai Pipeline Creator - Auto-activating skill for GCP Skills. Triggers on: vertex ai pipeline creator, vertex ai pipeline creator Part of the GCP Skills skill category.
OWASP Top 10 CI/CD Security Risks - prevention, detection, and remediation for pipeline security. Use when securing or reviewing CI/CD - flow control, IAM, dependency chain, poisoned pipeline execution, PBAC, credential hygiene, system config, third-party services, artifact integrity, logging and visibility.
Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill", "evaluate skill", "skill regression", "ground truth", "GRP pipeline", "skill quality", and "skill metrics".
Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, testing ML pipelines, verifying dependencies, debugging training failures, or ensuring data quality before training.
Call me when CI goes red. Pipeline fire brigade, deploy. Use when user mentions CI failures, build errors, test failures, or pipeline issues. Do NOT load for: local builds, standard implementation work, reviews, or setup.
Use this skill when setting up CI/CD pipelines, configuring GitHub Actions, implementing deployment strategies, or automating build/test/deploy workflows. Triggers on GitHub Actions, CI pipeline, CD pipeline, deployment automation, blue-green deployment, canary release, rolling update, build matrix, artifacts, and any task requiring continuous integration or delivery setup.
Tinybird Python SDK for defining datasources, pipes, and queries in Python. Use when working with tinybird-sdk, Python Tinybird projects, or data ingestion and queries in Python.
Turbo pipeline operations reference — lifecycle commands (pause, resume, restart, delete), pipeline states, checkpoint behavior, streaming vs job-mode differences, CLI syntax for `inspect`/`logs`, TUI shortcuts, and error pattern lookup. Triggers on: 'how do I pause/restart/delete', 'will deleting lose my data', 'what does this error mean', 'inspect TUI shortcuts'. For interactive diagnosis of a broken pipeline, use /turbo-doctor.
Troubleshoot and optimize the performance of Ascend C operators. This skill is applicable when users develop, review or optimize Ascend C kernel operators, or triggered when users mention keywords such as Ascend C performance optimization, operator optimization, tiling, pipeline, data copy, memory optimization, NPU/Ascend.