dspy-skills

Use this skill when you need to QA audit and fix a plugin skill file. Provides a methodology for verifying skill content against official documentation, fixing issues in-place, and producing verification reports.

This skill should be used when the user asks to "choose a DSPy adapter", "use JSONAdapter", "use XMLAdapter", "enable native function calling", "send images, audio, or files to DSPy", mentions `dspy.ChatAdapter`, `dspy.JSONAdapter`, `dspy.XMLAdapter`, `dspy.Image`, `dspy.Audio`, `dspy.File`, structured outputs, or multimodal DSPy signatures.

This skill should be used when the user asks to "compose DSPy modules", "use Ensemble optimizer", "combine multiple programs", "use dspy.MultiChainComparison", mentions "ensemble voting", "module composition", "sequential pipelines", or needs to build complex multi-module DSPy programs with ensemble patterns or multi-chain comparison.

This skill should be used when the user asks to "use BetterTogether", "combine prompt optimization and fine-tuning", "sequence DSPy optimizers", "run prompt then weight optimization", mentions `dspy.BetterTogether`, strategy strings such as "p -> w -> p", or needs to compose multiple DSPy teleprompters into an evaluated optimization sequence.

This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.

This skill should be used when the user asks to "create custom DSPy module", "design a DSPy module", "extend dspy.Module", "build reusable DSPy component", mentions "custom module patterns", "module serialization", "stateful modules", "module testing", or needs to design production-quality custom DSPy modules with proper architecture, state management, and testing.

This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.

This skill should be used when the user asks to "build local DSPy retrieval", "use dspy.Embedder", "use dspy.Embeddings", "save an embeddings index", "add FAISS retrieval", mentions semantic search, hosted embeddings, local embedding models, `EmbeddingsWithScores`, or needs a DSPy retriever over an application-owned text corpus.

This skill should be used when the user asks to "evaluate a DSPy program", "test my DSPy module", "measure performance", "create evaluation metrics", "use answer_exact_match or SemanticF1", mentions "Evaluate class", "comparing programs", "establishing baselines", or needs to systematically test and measure DSPy program quality with custom or built-in metrics.

This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inference costs with fine-tuning", mentions "model distillation", "teacher-student training", or wants to deploy a DSPy program as fine-tuned weights for production efficiency.

This skill should be used when the user asks to "optimize an agent with GEPA", "use reflective optimization", "optimize ReAct agents", "provide feedback metrics", mentions "GEPA optimizer", "LLM reflection", "execution trajectories", "agentic systems optimization", or needs to optimize complex multi-step agents using textual feedback on execution traces.

This skill should be used when the user asks to "integrate DSPy with Haystack", "optimize Haystack prompts using DSPy", "use DSPy to improve Haystack pipeline", mentions "Haystack pipeline optimization", "combining DSPy and Haystack", "extract DSPy prompt for Haystack", or wants to use DSPy's optimization capabilities to automatically improve prompts in existing Haystack pipelines.

This skill should be used when the user asks to "use MCP tools with DSPy", "connect an MCP server to ReAct", "convert MCP tools to DSPy tools", mentions Model Context Protocol, `dspy.Tool.from_mcp_tool`, streamable HTTP MCP transport, stdio MCP servers, or needs to expose MCP-compatible tools to a DSPy agent.

This skill should be used when the user asks to "optimize a DSPy program", "use MIPROv2", "tune instructions and demos", "get best DSPy performance", "run Bayesian optimization", mentions "state-of-the-art DSPy optimizer", "joint instruction tuning", or needs maximum performance from a DSPy program with substantial training data (200+ examples).

Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more

This skill should be used when the user asks to "choose a DSPy optimizer", "compare DSPy optimizers", "which teleprompter should I use", "optimize prompts or weights", mentions LabeledFewShot, BootstrapFewShotWithRandomSearch, KNNFewShot, COPRO, MIPROv2, SIMBA, GEPA, BootstrapFinetune, Ensemble, or BetterTogether, or needs a cost-aware DSPy optimization plan.

This skill should be used when the user asks to "refine DSPy outputs", "enforce constraints", "use dspy.Refine", "select best output", "use dspy.BestOfN", mentions "output validation", "constraint checking", "multi-attempt generation", "reward function", or needs to improve output quality through iterative refinement or best-of-N selection with custom constraints.

This skill should be used when the user asks to "deploy DSPy", "save and load a DSPy program", "configure DSPy cache", "harden pickle cache", "track DSPy token usage", "run DSPy asynchronously", "stream DSPy output", mentions `configure_cache`, `restrict_pickle`, `track_usage`, `acall`, `asyncify`, `streamify`, `StreamListener`, MLflow deployment, or needs production runtime guidance for a DSPy application.

This skill should be used when the user asks to "build a RAG pipeline", "create retrieval augmented generation", "use ColBERTv2 in DSPy", "set up a retriever in DSPy", mentions "RAG with DSPy", "context retrieval", "multi-hop RAG", or needs to build a DSPy system that retrieves external knowledge to answer questions with grounded, factual responses.

This skill should be used when the user asks to "create a ReAct agent", "build an agent with tools", "implement tool-calling agent", "use dspy.ReAct", mentions "agent with tools", "reasoning and acting", "multi-step agent", "agent optimization with GEPA", or needs to build production agents that use tools to solve complex tasks.

This skill should be used when the user asks to "use DSPy RLM", "process a very long context", "use ProgramOfThought", "use CodeAct", "run DSPy modules in parallel", mentions Recursive Language Models, sandboxed Python execution, Deno, `dspy.RLM`, `dspy.ProgramOfThought`, `dspy.CodeAct`, or `dspy.Parallel`, or needs to choose a DSPy reasoning module beyond Predict, ChainOfThought, and ReAct.

This skill should be used when the user asks to "create a DSPy signature", "define inputs and outputs", "design a signature", "use InputField or OutputField", "add type hints to DSPy", mentions "signature class", "type-safe DSPy", "Pydantic models in DSPy", or needs to define what a DSPy module should do with structured inputs and outputs.

This skill should be used when the user asks to "optimize with SIMBA", "use mini-batch introspective optimization", "generate self-reflective rules", mentions "SIMBA optimizer", "stochastic mini-batch ascent", "output variability", or needs an alternative to MIPROv2/GEPA that evolves rules and demonstrations from numeric metrics.