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Use to choose or compare DSPy optimizers including LabeledFewShot, BootstrapFewShot, MIPROv2, SIMBA, GEPA, BootstrapFinetune, Ensemble, and BetterTogether.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use for DSPy adapter selection, JSONAdapter, XMLAdapter, ChatAdapter, native function calling, structured outputs, and multimodal inputs like dspy.Image or dspy.Audio.
Use for composing DSPy modules with Ensemble, MultiChainComparison, ensemble voting, sequential pipelines, and multi-program workflows.
Use for BetterTogether, prompt plus weight optimization, fine-tuning sequences, and strategy chains like p -> w -> p.
Use for BootstrapFewShot, bootstrapped demonstrations, teacher-model demos, and low-data DSPy prompt optimization.
Use for creating custom DSPy modules, extending dspy.Module, reusable components, stateful modules, serialization, and module testing.
Use for debugging DSPy programs, inspect_history, tracing LLM calls, custom callbacks, observability, monitoring, and cost tracking.
| name | dspy-optimizer-selection |
| version | 1.0.0 |
| dspy-compatibility | 3.2.1 |
| tags | ["optimizer"] |
| requires-extras | [] |
| description | Use to choose or compare DSPy optimizers including LabeledFewShot, BootstrapFewShot, MIPROv2, SIMBA, GEPA, BootstrapFinetune, Ensemble, and BetterTogether. |
| allowed-tools | ["Read","Write","Glob","Grep"] |
Choose the smallest DSPy optimizer that matches the data, budget, and artifact being tuned. Establish a baseline before compiling anything.
| Need | Start with | Notes |
|---|---|---|
| Include a few labeled examples | dspy.LabeledFewShot | Random labeled demos; useful as a baseline |
| About 10 examples | dspy.BootstrapFewShot | Teacher-generated demos with metric filtering |
| 50+ examples and stronger demo search | dspy.BootstrapFewShotWithRandomSearch | Searches multiple demo sets; alias: dspy.BootstrapRS |
| Per-input nearest demos | dspy.KNNFewShot | Retrieves nearby examples before bootstrapping |
| Instruction-only hill climbing | dspy.COPRO | Coordinate ascent over instructions |
| Instruction and demo search | dspy.MIPROv2 | Bayesian search; install dspy[optuna] |
| Mini-batch introspective rules or demos | dspy.SIMBA | Uses output variability and self-reflection |
| Rich textual feedback and trace reflection | dspy.GEPA | Metric must accept five arguments |
| Distill prompts into model weights | dspy.BootstrapFinetune | Requires a fine-tunable LM and set_lm() |
| Combine candidate programs | dspy.Ensemble | Trades inference cost for robustness |
| Sequence prompt and weight optimization | dspy.BetterTogether | Meta-optimizer for configurable optimizer chains |
Use dspy-bootstrap-fewshot for the first optimization pass. Move to BootstrapFewShotWithRandomSearch when enough examples are available to search multiple demo sets.
Use dspy-miprov2-optimizer for instruction and demonstration search. Install its optional dependency first:
pip install -U "dspy[optuna]>=3.2.1,<3.3"
Use dspy-gepa-reflective when failures can be described with actionable text. Use dspy-simba-optimizer for a smaller mini-batch introspective loop with numeric metrics.
Use dspy-better-together when a fine-tunable LM is available and prompt optimization alone has plateaued.