dspy-gepa

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3 scripts

Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.

10installs
Sourceqredence/skills
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NPX Install

npx skill4agent add qredence/skills dspy-gepa

Tags

Translated version includes tags in frontmatter
dspygepa-loopagent-skills-optimizationscenario-generationprompt-engineering

SKILL.md Content

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DSPy GEPA — Generate, Evaluate, Propose, Apply

GEPA is a DSPy-powered tool for evaluating, optimizing, and generating skill scenarios.

Quick Start

Requires Python 3.10+ with 
dspy
, 
pyyaml
, and 
jsonschema
:
bash
pip install dspy-ai pyyaml jsonschema

Generate New Scenarios

Point GEPA at an existing skill to generate new test scenarios:
bash
python scripts/gepa.py generate \
  --skill-description "Creates FastAPI routers with CRUD endpoints" \
  --skill-name fastapi-router-py \
  --num-scenarios 5 \
  --output tests/scenarios/fastapi-router-py/generated.yaml
Or expand an existing scenario file with more variations:
bash
python scripts/gepa.py generate \
  --scenarios tests/scenarios/fastapi-router-py/scenarios.yaml \
  --num-scenarios 3 \
  --output new-scenarios.yaml

Evaluate Scenarios

Score a DSPy program against scenario patterns:
bash
python scripts/gepa_evaluate.py \
  --scenarios tests/scenarios/fastapi-router-py/scenarios.yaml

Full GEPA Loop

Evaluate baseline → optimize → evaluate optimized → save:
bash
python scripts/gepa.py optimize \
  --scenarios tests/scenarios/fastapi-router-py/scenarios.yaml \
  --output optimized_program.json

Convert Scenarios to Dataset

bash
python scripts/scenario_to_dataset.py \
  --scenarios tests/scenarios/fastapi-router-py/scenarios.yaml \
  --output dataset.json

Architecture

See 
references/gepa-architecture.md
for the full GEPA loop design and DSPy mapping.

Metrics

See 
references/metrics.md
for pattern-matching scoring details.

Example Output

See 
examples/sample-run.md
for a complete CLI session with output.