dspy-optimization-not-improving
Sub-skill of dspy: Optimization Not Improving (+2).
Best use case
dspy-optimization-not-improving is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of dspy: Optimization Not Improving (+2).
Teams using dspy-optimization-not-improving should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/optimization-not-improving/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dspy-optimization-not-improving Compares
| Feature / Agent | dspy-optimization-not-improving | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of dspy: Optimization Not Improving (+2).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Optimization Not Improving (+2)
## Optimization Not Improving
```python
# Increase number of training examples
# Ensure diverse, high-quality examples
# Try different optimizer settings
optimizer = BootstrapFewShotWithRandomSearch(
metric=metric,
max_bootstrapped_demos=8, # Increase
num_candidate_programs=20, # More search
num_threads=8
)
```
## Module Too Slow
```python
# Use faster model for compilation
compile_lm = dspy.OpenAI(model="gpt-4.1-mini")
deploy_lm = dspy.OpenAI(model="gpt-4")
with dspy.settings.context(lm=compile_lm):
optimized = optimizer.compile(module, trainset=data)
# Deploy with stronger model
dspy.settings.configure(lm=deploy_lm)
```
## Out of Memory
```python
# Process in batches
batch_size = 10
for i in range(0, len(trainset), batch_size):
batch = trainset[i:i+batch_size]
process_batch(batch)
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