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dspy-skills

作者 OmidZamani · OmidZamani/dspy-skills

Claude skills pack for DSPy — program language models, optimize prompts, build RAG pipelines systematically.

dspy-skills teaches Claude the DSPy mental model: signatures, modules, predictors, teleprompters, and evaluation loops. Instead of hand-crafting prompts, you describe the task via signatures and let DSPy's optimizers do the work — and Claude writes the DSPy code idiomatically rather than resorting to raw prompt templates.

▶ 观看演示 📦 安装 💡 使用场景

为什么要用

核心特性

实时演示

实际使用效果

dspy-skill.replay ▶ 就绪
0/0

安装

作者仓库 · Claude Skills 官方文档

使用场景

实战用法： dspy-skills

How to build your first DSPy program and optimize it

👤 ML engineers and applied researchers ⏱ ~90 min advanced

何时使用： You have a task where prompt quality matters and want a systematic way to improve it.

步骤
  1. Define the signature
    Task: classify support tickets into {billing, technical, account}. Give me a DSPy signature and a simple Predict module.✓ 已复制
    → Signature + module code
  2. Write an eval
    Add an evaluation set of 50 labeled examples and an accuracy metric.✓ 已复制
    → Eval harness with metric callable
  3. Optimize
    Run BootstrapFewShot to compile the module against the eval set.✓ 已复制
    → Compiled predictor + improved score

结果： A DSPy-optimized predictor that beats a hand-written prompt, with reproducible code.

注意事项
  • Eval too small — optimizer overfits — Minimum 100–200 examples; hold out a true test set
  • Metric doesn't capture what you care about — Spend on metric design before on model choice
搭配使用： filesystem

Build a RAG pipeline with DSPy

👤 Engineers building retrieval-augmented systems ⏱ ~120 min advanced

何时使用： You want modular, optimizable RAG rather than a hand-wired chain.

步骤
  1. Define the modules
    Create a DSPy RAG pipeline: RetrieveThenRead with ColBERTv2 or a local retriever.✓ 已复制
    → Modular pipeline with separate retrieval and generation
  2. Optimize end-to-end
    Write an eval on our QA set and run MIPRO to improve.✓ 已复制
    → Compiled pipeline with score delta

结果： A RAG pipeline you can improve by changing the eval, not by rewriting prompts.

注意事项
  • Retriever quality caps end-to-end quality — Evaluate retrieval separately (recall@k) before optimizing generation
搭配使用： local-rag

组合

与其他 MCP 搭配，撬动十倍杠杆

dspy-skill + local-rag

Plug a local retriever into DSPy's RAG modules

Swap the ColBERT retriever for a local-rag MCP as the retrieval source.✓ 已复制
dspy-skill + filesystem

Organize DSPy programs, evals, and artifacts in a repo

Lay out a DSPy project with programs/, evals/, and artifacts/ directories.✓ 已复制

工具

此 MCP 暴露的能力

工具输入参数何时调用成本
signature-design task spec Start of any DSPy program 0
module-authoring signatures + flow After signatures 0
teleprompter-optimization module + eval After eval is ready LLM tokens during optimization
evaluation-harness task data Before optimizing 0

成本与限制

运行它的成本

API 配额
LLM tokens dominate during optimization runs
每次调用 Token 数
Can be high — optimizations may invoke the LLM hundreds of times
费用
depends on provider
提示
Use cheap models during teleprompter runs, upgrade only for final evaluation

安全

权限、密钥、影响范围

凭据存储： LLM provider keys in env vars
数据出站： LLM provider endpoints

故障排查

常见错误与修复

Teleprompter seems to make things worse

Check metric correctness; use a held-out set; widen the example pool.

Optimization eats your budget

Cap max_bootstrapped_demos and use cheaper models during search.

替代方案

dspy-skills 对比其他方案

替代方案何时用它替代权衡
prompt-architect-skillYou want prompt-level craft, not DSPy's programmatic approachHand-crafted vs optimized

更多

资源

📖 阅读 GitHub 上的官方 README

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