dsa110-python-development
DSA-110 radio astronomy pipeline development with Python 3.11+, Dagster workflows, CASA integration, FastAPI, GraphQL/Strawberry, and scientific computing best practices. Use for any work on the dsa110-contimg...
Explorez les skills IA publiés de dsa110 et consultez leurs instructions source.
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DSA-110 radio astronomy pipeline development with Python 3.11+, Dagster workflows, CASA integration, FastAPI, GraphQL/Strawberry, and scientific computing best practices. Use for any work on the dsa110-contimg...
DSA-110 radio astronomy pipeline development with Python 3.11+, Dagster workflows, CASA integration, FastAPI, GraphQL/Strawberry, and scientific computing best practices. Use for any work on the dsa110-contimg...
Incident response and troubleshooting for pipeline failures. Use when investigating errors, log anomalies, failed runs, or degraded performance and when preparing recovery steps or mitigations.
Frontend UI/UX changes, dashboards, visualizations, and UI testing. Use when modifying the web UI, improving UX, adjusting visualizations, or running frontend tests.
Quality assurance, validation checks, metrics, and regression verification for pipeline outputs. Use when running validation suites, checking image quality, or defining acceptance criteria.
Documentation updates, run reports, verification summaries, and status notes for the pipeline. Use when creating or updating docs, reports, or change logs.
Development workflows for the pipeline including tests, linting, local dev servers, CI checks, and code organization. Use when implementing features, running tests, or preparing changes for review.
Environment setup, configuration management, ports, and runtime settings for the pipeline. Use when adjusting configs, setting env vars, or diagnosing environment-specific behavior.
Run, monitor, and control the DSA-110 continuum imaging pipeline or its orchestration. Use for starting/stopping runs, backfills, scheduling, retries, runtime state inspection, Dagster/CLI entrypoints, and oper...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.