dspy-gepa-optimizer
byintertwine· 11 skills
Optimize DSPy programs with dspy.GEPA — the reflective/evolutionary optimizer that is the 2026 gold standard for DSPy (beats MIPROv2 on complex tasks with far fewer rollouts when the metric returns rich feedback). Use when the user says optimize, compile, GEPA, reflective optimization, or "make this program better" and a DSPy program + metric + trainset exist.
How to install dspy-gepa-optimizer?
intertwine/dspy-agent-skills/dspy-gepa-optimizer
$npx -y skills add intertwine/dspy-agent-skills --skill dspy-gepa-optimizerInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/intertwine/dspy-agent-skills" --skill "intertwine/dspy-agent-skills/dspy-gepa-optimizer"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/intertwine/dspy-agent-skills" that are relevant to the current task. Run `npx skills add "https://github.com/intertwine/dspy-agent-skills"` and select the relevant skills, then follow their instructions.
Related
6 picksType
- azure-aiUse for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR.Jul 2026
- ai-research-exploreRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates.Jul 2026
- ai-research-reproductionRigor Reproduce compatible skill slug for README-first deep learning repository reproduction.Jul 2026
- explore-codeRigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories.Jul 2026
- run-trainRigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup…Jul 2026
- explore-runRigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories.Jul 2026
