dspy

Streamline DSPy experiment setup and iteration with structured outputs.

5|1|Updated May 15, 2026

Search Tags:#workflow #logging #optimization #reproducibility #experiment #dspy

Ready to run

npx skills add https://github.com/SerjSmor/skills --skill dspy-serjsmor

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy helps engineers and researchers coordinate end-to-end experiments for DSPy-based optimization, logging, and reproducibility, reducing ad-hoc scripting and manual setup.

Core Features & Use Cases

Interview-driven task scoping to decide problem type, budget, and optimizer
Deterministic experiment surface layout with dedicated outputs (results.tsv, runs/, programs/)
Clear guidance on using references/ for optimizer choices and accounting
Supports common ML task types (classification, extraction, ranking, generation) with consistent iteration discipline

Quick Start

Ask the agent to start a DSPy experiment with a LabeledFewShot baseline and log outputs to a local results.tsv.

Frequently Asked Questions about dspy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I structure DSPy experiments for reproducibility and logging?▼

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.

What is the best way to set up a DSPy baseline for classification or generation tasks?▼

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.

Can I use configurable budgets for DSPy optimization across different ML tasks?▼

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.

Does this DSPy workflow support optimizer guidance and accounting for experiments?▼

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.

Why do I need an interview-guided workflow for DSPy optimization?▼

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.

What limitations exist when running DSPy experiments with local logging?▼

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.