DSPy-Programming-not-prompting-LMs-skills
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This tool provides a collection of AI skills and commands for coding agents like Claude Code. It helps developers scaffold and build reliable AI features, such as RAG pipelines, text classifiers, and parsers, using the DSPy framework instead of manually written prompts.
Security Assessment
Overall Risk: Low. The automated code scan reviewed 12 files and found no dangerous patterns or hardcoded secrets. The tool does not request any dangerous system permissions. Since it is designed to generate code and project structures for AI features, it will naturally help you write scripts that make network requests or access databases, but the tool itself acts as an assistant rather than executing arbitrary shell commands or accessing sensitive data directly.
Quality Assessment
The project is actively maintained, with its most recent code push happening today. The README is well-documented, clearly outlining a wide variety of use cases and instructions to help developers get started immediately. However, there are two notable concerns regarding maturity and community trust. First, the repository lacks a license file, which means the legal terms for using, modifying, or distributing the code are undefined and restrictive by default. Second, the project has very low visibility with only 5 GitHub stars, indicating a minimal user base and limited peer review from the open-source community.
Verdict
Use with caution: The codebase appears safe and clean, but you should be aware of its limited community testing and the lack of an explicit open-source license.
AI skills for Claude Code, Cursor, and other coding agents. Build reliable AI features with DSPy — classification, RAG, parsing, agents, and more. Just type /ai-do.
AI Skills for Claude Code
Build reliable AI features. Powered by DSPy — a framework that lets you program language models with composable modules instead of hand-writing prompts.
Quick start
The only skill you need out the repo is /ai-do. Describe what you want to build and it tells you which skill to use next.
npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do
Then in Claude Code:
/ai-do I want to build a support ticket classifier
It picks the right skill, generates a ready-to-run prompt, and tells you what to install.
What problem are you solving?
| Your problem | Skill | What it does |
|---|---|---|
| "I want to build something with AI but not sure where to start" | /ai-do |
Describe your problem, get routed to the right skill with a ready-to-use prompt |
| "I'm starting a new AI feature" / "DSPy quickstart" | /ai-kickoff |
Scaffold a complete AI project with the right structure |
| "I need a multi-phase plan for my AI feature" / "what order should I build this in" | /ai-planning |
Create a phased roadmap before you start building |
| "Which DSPy module should I use?" / "Predict vs ChainOfThought" | /ai-choosing-architecture |
Pick the right DSPy pattern and architecture for your task |
| "I need to auto-sort/tag/categorize content" / "classification results are inconsistent" | /ai-sorting |
Build AI that sorts tickets, tags emails, detects sentiment |
| "I need to search docs and answer questions" / "retrieval returns irrelevant results" / "RAG pipeline tutorial" | /ai-searching-docs |
Build AI-powered knowledge base, help center, or doc Q&A |
| "I need AI to answer questions about our database" / "chat with your Postgres" | /ai-querying-databases |
Text-to-SQL: plain English questions over Postgres, MySQL, Snowflake |
| "I need to condense long content into summaries" | /ai-summarizing |
Summarize meetings, articles, threads — with length control |
| "I need to pull structured data from messy text" / "the emails are messy and lack structure" | /ai-parsing-data |
Parse invoices, extract entities, convert text to JSON |
| "I need AI to take actions and call APIs" / "LLM function calling" | /ai-taking-actions |
Build AI that calls APIs, uses tools, and completes tasks |
| "I need AI to write articles, reports, or copy" | /ai-writing-content |
Generate blog posts, product descriptions, newsletters |
| "My AI fails on hard problems that need planning" / "LLM can't do multi-step logic" | /ai-reasoning |
Add multi-step reasoning, Self-Discovery, chain-of-thought |
| "My task needs multiple AI steps" / "LangChain LCEL alternative" | /ai-building-pipelines |
Chain classify, retrieve, generate, verify into one pipeline |
| "I need to verify AI output before users see it" / "LLMs invent data points" | /ai-checking-outputs |
Add guardrails, fact-checking, safety filters, and quality gates |
| "My AI makes stuff up / hallucinates" / "LLM makes up facts" | /ai-stopping-hallucinations |
Ground AI in facts with citations, verification, and source checking |
| "My AI doesn't follow our rules" / "LLM JSON output is unreliable" | /ai-following-rules |
Enforce content policies, format rules, and business constraints |
| "Review my DSPy code" / "is my AI code correct" / "best practices check" | /ai-auditing-code |
Audit DSPy code for correctness, anti-patterns, and best practices |
| "My AI gives wrong answers" / "I spent hours tweaking prompts" | /ai-improving-accuracy |
Measure quality, then systematically improve it |
| "My AI gives different answers every time" / "same prompt, different results every run" | /ai-making-consistent |
Lock down outputs so they're predictable and reliable |
| "My AI is too expensive" / "LLM API costs too high" / "GPT-4 costs too much" | /ai-cutting-costs |
Reduce API costs with smart routing, caching, fine-tuning |
| "Let's fine-tune on our data" / "prompt optimization hit a ceiling" | /ai-fine-tuning |
Train models on your data for max quality or cost savings |
| "Can we switch to a different model?" / "prompt broke after model update" | /ai-switching-models |
Switch providers, compare models, re-optimize automatically |
| "We don't have enough training data" / "no labeled data, need to bootstrap" | /ai-generating-data |
Generate synthetic examples, fill data gaps, bootstrap from scratch |
| "How do I put my AI behind an API?" / "deploy LLM as API" / "productionize my AI" | /ai-serving-apis |
Wrap your AI in FastAPI endpoints for production serving |
| "Is our AI safe to launch?" / "prevent prompt injection" | /ai-testing-safety |
Automatically find vulnerabilities with adversarial testing |
| "We need to moderate user content" | /ai-moderating-content |
Build AI content moderation with severity levels and routing |
| "Is our AI still working in production?" / "silent quality drops, prompt drift" | /ai-monitoring |
Monitor quality, safety, and cost — catch degradation early |
| "Why did my AI give that wrong answer?" | /ai-tracing-requests |
Trace individual requests — see every LM call, retrieval, and step |
| "Which of our optimization experiments was best?" | /ai-tracking-experiments |
Log, compare, and promote optimization runs |
| "I need AI to score, grade, or evaluate things" / "LLM as a judge" | /ai-scoring |
Score essays, audit support quality, rate code reviews against rubrics |
| "My AI works on simple inputs but fails on complex ones" / "works on simple inputs but fails on complex ones" | /ai-decomposing-tasks |
Break unreliable single-step tasks into reliable subtasks |
| "I need a conversational AI assistant" / "how do I build a chatbot" / "Intercom bot alternative" | /ai-building-chatbots |
Build chatbots with memory, state, and doc-grounded responses |
| "I need multiple AI agents working together" / "CrewAI alternative" | /ai-coordinating-agents |
Supervisor agents, specialist handoff, parallel research teams |
| "I need to translate content to other languages" / "localize our app" / "i18n with AI" | /ai-translating-content |
Translate with glossary enforcement, batch i18n, brand voice |
| "I need product recommendations" / "you might also like" / "personalize the feed" | /ai-recommending |
Retrieval + LM re-ranking for personalized recommendations |
| "I need to strip PII before sending to an LLM" / "GDPR compliance" / "anonymize data" | /ai-redacting-data |
Detect and replace PII with regex + LM pipeline |
| "I need to deduplicate contacts" / "entity resolution" / "merge records" | /ai-matching-records |
Block, score pairs, merge duplicates across datasets |
| "I need to normalize messy data" / "standardize company names" / "fix inconsistent formats" | /ai-cleaning-data |
AI-powered data normalization with rule inference |
| "I need to detect fraud or anomalies" / "flag suspicious transactions" / "abuse detection" | /ai-detecting-anomalies |
Score events against baselines with severity and explanations |
| "I need smart notification messages" / "weekly digest" / "incident alerts from logs" | /ai-generating-notifications |
Event-driven notifications with channel constraints and digests |
| "I need to analyze images" / "extract text from screenshots" / "generate alt text" | /ai-understanding-images |
Vision model pipelines with dspy.Image for structured extraction |
| "I need to rewrite text in a different tone" / "simplify legal language" / "adapt for different audience" | /ai-rewriting-text |
Tone, reading level, and audience adaptation with fidelity checks |
| "My AI is broken/erroring" / "Could not parse LLM output" | /ai-fixing-errors |
Diagnose and fix crashes, wrong outputs, and weird behavior |
| "DSPy can do X but there's no skill for it" | /ai-request-skill |
Build a missing skill and submit a PR, or file a GitHub issue requesting it |
Using a specific tool with DSPy?
| Tool | Skill | What it covers |
|---|---|---|
| VizPy (prompt optimizer) | /dspy-vizpy |
Drop-in ContraPrompt/PromptGrad optimizers as alternative to GEPA/MIPROv2 |
| Langtrace | /dspy-langtrace |
Auto-instrument DSPy with one line, cloud + self-hosted tracing |
| Arize Phoenix | /dspy-phoenix |
Open-source trace viewer with built-in evals, local UI at localhost:6006 |
| W&B Weave | /dspy-weave |
Cloud experiment tracking and team dashboards via @weave.op() decorator |
| MLflow | /dspy-mlflow |
Auto-tracing, experiment tracking, and model registry for DSPy |
| LangWatch | /dspy-langwatch |
Auto-tracing + real-time optimizer progress dashboard |
| Langfuse | /dspy-langfuse |
Tracing + scoring + annotation queues + experiment tracking |
| Ragas | /dspy-ragas |
Decomposed RAG evaluation: faithfulness, context precision/recall |
| Qdrant | /dspy-qdrant |
Official vector DB integration + custom retriever pattern for any DB |
| Ollama | /dspy-ollama |
Run DSPy with local models, no API key needed |
| vLLM | /dspy-vllm |
High-throughput production serving for self-hosted models |
Know which DSPy concept you need?
If you already know DSPy and think in its vocabulary, use these API-first skills instead:
| DSPy concept | Skill | What it covers |
|---|---|---|
Signature, InputField, OutputField |
/dspy-signatures |
Inline and class-based signatures, typed fields, Pydantic models |
dspy.LM, dspy.configure |
/dspy-lm |
Provider strings, temperature/max_tokens, per-module LM assignment |
dspy.Assert, dspy.Suggest |
/dspy-assertions |
REMOVED in DSPy 3.x — legacy docs only. Use /dspy-refine or /dspy-best-of-n instead |
dspy.Module, forward() |
/dspy-modules |
Custom modules, composing sub-modules, save/load state |
dspy.Example, Prediction |
/dspy-data |
with_inputs(), train/dev splits, loading from CSV/JSON/HuggingFace |
dspy.Evaluate, metrics |
/dspy-evaluate |
SemanticF1, exact match, LM-as-judge, composite metrics |
dspy.Predict |
/dspy-predict |
Direct LM calls, simplest inference module |
dspy.ChainOfThought |
/dspy-chain-of-thought |
Step-by-step reasoning, reasoning field |
dspy.ProgramOfThought |
/dspy-program-of-thought |
Code generation + execution for math/computation |
dspy.ReAct |
/dspy-react |
Tool-using agents, Reasoning-Action-Observation loop |
dspy.CodeAct |
/dspy-codeact |
Agents that write and execute code to act |
dspy.MultiChainComparison |
/dspy-multi-chain-comparison |
Multiple reasoning chains, pick the best |
dspy.BestOfN |
/dspy-best-of-n |
Rejection sampling with a reward function |
dspy.Parallel |
/dspy-parallel |
Concurrent LM calls, batch processing |
dspy.Refine |
/dspy-refine |
Iterative self-improvement with feedback |
dspy.RLM |
/dspy-rlm |
Reinforcement-learning-style refinement |
dspy.BootstrapFewShot |
/dspy-bootstrap-few-shot |
Auto-generate few-shot demos, first optimizer to try |
dspy.BootstrapFewShotWithRandomSearch |
/dspy-bootstrap-rs |
Random search over candidate demo sets |
dspy.MIPROv2 |
/dspy-miprov2 |
Best prompt optimizer, instructions + demos jointly |
dspy.GEPA |
/dspy-gepa |
Instruction generation and selection |
dspy.BetterTogether |
/dspy-better-together |
Combined prompt + weight tuning |
dspy.BootstrapFinetune |
/dspy-bootstrap-finetune |
Fine-tune weights from bootstrapped data |
dspy.COPRO |
/dspy-copro |
Instruction candidates with breadth search |
dspy.Ensemble |
/dspy-ensemble |
Combine multiple optimized programs |
dspy.InferRules |
/dspy-infer-rules |
Extract decision logic from examples |
dspy.KNN, dspy.KNNFewShot |
/dspy-knn-few-shot |
Embedding-based demo retrieval |
dspy.LabeledFewShot |
/dspy-labeled-few-shot |
Hand-picked demonstrations |
dspy.SIMBA |
/dspy-simba |
Small-step incremental optimization |
ChatAdapter, JSONAdapter, TwoStepAdapter |
/dspy-adapters |
Prompt formatting, structured output (see also /dspy-two-step-adapter) |
dspy.ChatAdapter (deep dive) |
/dspy-chatadapter |
Field delimiters, parse/format internals, JSON fallback, fine-tuning data |
dspy.Tool, PythonInterpreter |
/dspy-tools |
Wrapping functions as tools, code execution |
dspy.Retrieve, ColBERTv2, Embedder |
/dspy-retrieval |
Search, RAG pipelines, embeddings |
dspy.Image, dspy.Audio, dspy.Code, dspy.History |
/dspy-primitives |
Multimodal inputs, conversation history |
dspy.streamify, StreamListener, StreamResponse |
/dspy-streaming |
Real-time token streaming to frontends, SSE, WebSocket |
dspy.Tool.from_mcp_tool, MCP servers |
/dspy-mcp |
Connect agents to MCP tool servers |
dspy.experimental.Citations, Document |
/dspy-citations |
Structured source attribution for RAG |
dspy.TwoStepAdapter |
/dspy-two-step-adapter |
Reasoning models (o1, o3, DeepSeek-R1) structured output |
aforward(), acall(), async patterns |
/dspy-async |
Async execution, FastAPI, concurrent calls |
inspect_history, save/load, configure_cache |
/dspy-utils |
Caching, debugging, persistence |
VizPy (ContraPromptOptimizer, PromptGradOptimizer) |
/dspy-vizpy |
Commercial drop-in prompt optimizer, alternative to GEPA |
Langtrace (langtrace.init) |
/dspy-langtrace |
Auto-instrument DSPy, cloud + self-hosted LLM observability |
Arize Phoenix (DSPyInstrumentor) |
/dspy-phoenix |
Open-source trace viewer with evals, local UI |
W&B Weave (@weave.op()) |
/dspy-weave |
Cloud experiment tracking and team dashboards |
MLflow (mlflow.dspy.autolog()) |
/dspy-mlflow |
Auto-tracing, experiment tracking, model registry |
LangWatch (langwatch.dspy.init) |
/dspy-langwatch |
Auto-tracing and real-time optimizer progress |
Langfuse (DSPyInstrumentor, @observe) |
/dspy-langfuse |
Tracing + scoring + annotation queues + experiments |
Ragas (ragas.evaluate) |
/dspy-ragas |
Decomposed RAG evaluation with LLM-as-judge metrics |
Qdrant (QdrantRM) |
/dspy-qdrant |
Vector DB retriever with hybrid search |
Ollama (ollama_chat/) |
/dspy-ollama |
Local model serving for development |
vLLM (openai/ + local server) |
/dspy-vllm |
Production self-hosted model serving |
Install
Option 1: npx skills (recommended — works with any AI coding agent)
Install all 94 skills in one command. Works with Claude Code, Cursor, Codex, Cline, Windsurf, and 35+ other agents.
npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills
The CLI will prompt you to pick which skills and which agents to install. Or install everything non-interactively:
npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --all -y
To install globally (available in all your projects):
npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills -g
Option 2: Claude Code plugin marketplace
# In Claude Code, run:
/plugin marketplace add lebsral/DSPy-Programming-not-prompting-LMs-skills
Then install a skill group:
/plugin install dspy-build-skills@dspy-skills # Building AI features (17 skills)
/plugin install dspy-quality-skills@dspy-skills # Quality and reliability (8 skills)
/plugin install dspy-ops-skills@dspy-skills # Production operations (7 skills)
/plugin install dspy-api-skills@dspy-skills # DSPy API-first skills (37 skills)
Option 3: Manual (git clone)
git clone https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills.git
Copy skills to your agent's skill directory:
# Claude Code
cp -r DSPy-Programming-not-prompting-LMs-skills/skills/* ~/.claude/skills/
# Cursor
cp -r DSPy-Programming-not-prompting-LMs-skills/skills/* ~/.cursor/skills/
Or symlink to stay in sync with updates:
ln -s "$(pwd)/DSPy-Programming-not-prompting-LMs-skills/skills/"* ~/.claude/skills/
Option 4: Slash command (just /ai-do, no dependencies)
If you only want the router and prefer Claude Code's built-in slash command system:
curl -fsSL https://raw.githubusercontent.com/lebsral/DSPy-Programming-not-prompting-LMs-skills/main/commands/ai-do.md \
-o ~/.claude/commands/ai-do.md
This gives you /ai-do globally in Claude Code. It routes you to the right skill and tells you how to install it. No npx, no plugins — just one file.
Managing skills
npx skills list # See what you have installed
npx skills check # Check for updates
npx skills update # Update all installed skills
npx skills remove # Uninstall skills
Keeping skills up to date
These skills are actively improved. To get the latest versions:
npx skills check # See what's changed
npx skills update # Pull latest versions
For manual/symlink installs, run git pull in your cloned repo.
Migrating from v1.12.1 or earlier? A YAML formatting change in v1.12.2 means
npx skills updatemay fail on some skills. Reinstall to fix:npx skills remove dspy-skills npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills -g -s '*'
Adding new skills after initial install
npx skills update only updates skills you already have. When new skills are added to this repo (like the dspy- API-first skills), re-run the add command to pick them up:
# Interactive — choose which new skills to add
npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills
# Non-interactive — add all new skills automatically
npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --all -y
This won't duplicate skills you already have — it only adds the ones that are missing.
Use a skill
In Claude Code (or any agent that supports the Agent Skills standard):
- Invoke directly:
/ai-sortingor/ai-kickoff my-project - Ask naturally: "Help me sort support tickets into categories" — the agent picks the right skill
How It Works
Each skill is a directory under skills/ containing:
SKILL.md— Main instructions Claude follows (YAML frontmatter + markdown)examples.md— Worked examples (loaded on demand)reference.md— Detailed reference material (loaded on demand)
Under the hood, skills use DSPy — a framework for building AI features with composable modules that compile into optimized prompts. You don't need to know DSPy to use these skills; they guide you through everything.
Skills follow the Claude Code skills format and the Agent Skills open standard.
Reference Docs
docs/dspy-reference.md— DSPy API quick reference (modules, optimizers, patterns)docs/langchain-langgraph-reference.md— LangChain & LangGraph API quick reference (loaders, tools, StateGraph)docs/skills-spec.md— Claude Code skills specification (for contributors)
Contributing
Adding a new skill
- Create
skills/ai-<problem>/SKILL.md— name it after the problem, not the DSPy concept - Add YAML frontmatter with
nameanddescription(include phrases users would naturally say) - Write step-by-step instructions in the markdown body
- Add
examples.mdand/orreference.mdfor supporting content - Update the problem catalog table in this README
- Test with
/ai-<problem>in Claude Code - Bump the patch version in
.claude-plugin/marketplace.json
See docs/skills-spec.md for the full skill format specification.
Links
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