This is the DSPy orchestration skill you reach for when you're building a real pipeline from scratch, not just prototyping one predictor. It walks you through the seven-step loop: spec out your task, write the typed signature and module, split train and validation data, write a rich metric that returns feedback (not just a score), baseline it, run GEPA optimization with a reflection LM, then export the artifact. The template is opinionated about details that matter: valset should be separate and representative, the metric must return a dspy.Prediction object or aggregation breaks, and you should always baseline before optimizing so you have a claim to make. If you're saying "end to end" or "from scratch," this is the entry point.
npx -y skills add intertwine/dspy-agent-skills --skill dspy-advanced-workflow --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
sickn33/antigravity-awesome-skills
kubesphere/kubesphere
supercent-io/skills-template