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Found 1,235 Skills
Orchestrate the full ToolUniverse self-improvement cycle: discover APIs, create tools, test with researcher personas, fix issues, optimize skills, and push via git. References and dispatches to all other devtu skills. Use when asked to: run the self-improvement loop, do a debug/test round, expand tool coverage, improve tool quality, or evolve ToolUniverse.
Manage Alibaba Cloud DevOps (Yunxiao 2020) via OpenAPI/SDK. Use for project/repository/pipeline resource discovery, read-only inspection, and safe change planning before mutating operations.
Implemente armazenamento persistente com Docker volumes, bind mounts e estratégias de backup
Verifica a Stack do Kestra. Além disso analisa parâmetros, rotas Traefik, volumes, recursos e conformidade do stack Kestra de Acordo com as Recomendações da Promovaweb.
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
Creates runbook.md for DevOps setup. L3 Worker invoked CONDITIONALLY when hasDocker detected.
Professional DevOps engineering skill for creating CI/CD pipelines, implementing infrastructure as code, managing environments, and establishing monitoring and observability across all deployment stages.
Comprehensive mobile DevOps workflow that orchestrates mobile application development, CI/CD for mobile, app store deployment, and mobile device testing. Handles everything from mobile app build automation and testing to app store submission, monitoring, and mobile-specific DevOps practices.
DevOps, MLOps, DevSecOps practices for cloud environments (GCP, Azure, AWS)
Cloud and DevOps expert including AWS, GCP, Azure, and Terraform
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after idea-tournament), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing idea tournaments or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use idea-tournament).