Streamline DSPy experiment setup and iteration with structured outputs.
npx skills add https://github.com/SerjSmor/skills --skill dspy-serjsmor
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/SerjSmor/skills/tree/main/dspy Command: npx skills add https://github.com/SerjSmor/skills --skill dspy-serjsmor
DSPy helps engineers and researchers coordinate end-to-end experiments for DSPy-based optimization, logging, and reproducibility, reducing ad-hoc scripting and manual setup.
Ask the agent to start a DSPy experiment with a LabeledFewShot baseline and log outputs to a local results.tsv.
High-intent search queries and answers about installing and using this skill.
Structure DSPy experiments using an interview-guided workflow that scopes problem type, budget, and optimizer, while outputting deterministic file layouts like results.tsv, runs/, and programs/ to ensure reproducibility and consistent logging.
Set up a DSPy baseline by initiating an interview-guided task scoping process to select your problem type and optimizer, then run a LabeledFewShot baseline and automatically log the structured outputs to a local results.tsv file.
Yes, you can apply configurable budgets for DSPy optimization across classification, extraction, ranking, and generation tasks, using the interview-driven workflow to define constraints and select appropriate optimizers from references.
Yes, the workflow supports optimizer guidance and accounting by referencing a dedicated references/ directory, allowing you to select appropriate optimizers and track experiment costs throughout the iteration process.
An interview-guided workflow is needed for DSPy optimization to systematically determine the problem type, budget constraints, and optimizer choices upfront, reducing ad-hoc scripting and ensuring disciplined end-to-end experiment iteration.
Limitations include relying on local deterministic outputs such as results.tsv and runs/ directories, meaning experiment scale and tracking are bounded by local file system constraints and manual reference checks for optimizer accounting.
surus-latGenerate synthetic benchmark datasets as JSONL input-output pairs from task specifications.
maruakshayIdentify and mitigate security risks in DSPy prompt programs and tool wrappers.
magnus919Compile typed signatures and modules against defined metrics to optimize language model prompt programs.
Supporter09Convert PyTorch models into LightningModule and Trainer workflows.
