Results for “aero-workflow”

Provides guidance and best practices for working with Apache Airflow, covering setup, development, deployment, and operations.

Collects launch inputs and runs preflight checks before executing TAO workflows such as AutoML, training, evaluation, inference, export, TensorRT engine generation, or DEFT jobs on supported platforms.

Routes user requests to specialist skills for AEM 6.5 LTS workflow model design, custom step development, launcher configuration, triggering, debugging, and triaging.

Start AEM Workflows on AEM 6.5 LTS using all available triggering mechanisms: Timeline UI, Manage Publication, WorkflowSession API, HTTP Workflow API, and replication-linked triggers.

Use when planning an IROS project timeline from venue fit through the spring paper deadline, the separate video deadline, the summer review silence, the June notification, the July camera-ready, IEEE Xplore publication, registration, and presentation, with backward-planning offsets for a real-robot paper on the fall IEEE/RSJ calendar.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Workflow Implementation

Runs Google Workspace cross-service workflows via the gws CLI, with helpers for standup reports, meeting prep, email-to-task conversion, weekly digests, and file announcements.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Coordinates a goal lifecycle: define, decompose, activate, track, and execute goals using a set of system skills that write markdown files.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Terraform infrastructure-as-code workflow patterns: state and environments, module design, safe plan/apply, drift control, and CI guardrails

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

codex-task-workspace

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Use when deciding which AER-skills sub-skill to use next, before choosing a specialized skill or after a user asks how to sequence manuscript work from topic selection through rebuttal for AER, AER:Insights, or AEJ journals. Routes — does not replace — the specialized skills.

Agent Workflow Designer

runtime-and-process

Generates detailed, technology-agnostic implementation blueprints by analyzing project architecture, technology stacks, and data flow patterns.

Runs a three-phase AOI training pipeline: AutoML HPO baseline, DEFT iterative data improvement, and AutoML refinement on the augmented dataset.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Defines, validates, runs, resumes, and audits repo-local multi-step automations using the `asc workflow` CLI and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Threat modeling workflow for software systems: scope, data flow diagrams, STRIDE analysis, risk scoring, and turning mitigations into backlog and tests. Use when designing new features, reviewing architecture changes, handling sensitive data, or hardening auth/payment/multi-tenant flows.

Universal project workflow with built-in quality auditing. Quick mode (fast brainstorm, ~5 min) or Deep mode (full plan with audits, ~30 min). Auto-picks based on task size. Trigger: 'build', 'create', 'plan', 'new project', 'brainstorm', 'design', 'UI', 'website', 'dashboard', 'app', 'help me build', or any project-level task.

Quick Flow

Design Security Operations Centers with processes. TRIGGERS - Use when user needs help with soc-design related tasks.

Designs efficient design team workflows covering task management, collaboration rituals, communication norms, tooling, and design-development collaboration.

Use when planning a USENIX FAST project timeline from venue fit through choosing a Spring or Fall deadline, double-blind submission, the author-response period, shepherding or a one-shot revision, artifact evaluation, and the open-access camera-ready, with backward-planning offsets tuned to storage evaluation and honest handling of the two-deadline cycle.

Design, build, and manage AWS Step Functions state machines for serverless workflow orchestration, including error handling, parallel execution, and integration with AWS services.

Workflow 1: Full idea discovery pipeline. Orchestrates research-lit → idea-creator → novelty-check → research-review to go from a broad research direction to validated, pilot-tested ideas. Use when user says "找idea全流程", "idea discovery pipeline", "从零开始找方向", or wants the complete idea exploration workflow.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.