A programming guide for DSPy, a Python framework where typed program definitions and evaluation metrics are used to improve language-model prompts automatically.

Use it to define signatures, build modules such as Predict, ChainOfThought, and ReAct, choose optimizers, compile programs, cache results, and evaluate them.

It helps developers build and measure language-model programs without treating DSPy as a general-purpose chain or retrieval framework.

dspy — Skill for Claude Code and Codex by magnus919

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add magnus919/agent-skills --skill dspy

Clone the repo

git clone --depth 1 https://github.com/magnus919/agent-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin dspy — /plugin install dspy after adding the marketplace above.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/magnus919/agent-skills/dspy/github.svg)](https://agentmods.dev/skills/magnus919/agent-skills/dspy)

Your own site

<a href="https://agentmods.dev/skills/magnus919/agent-skills/dspy"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/dspy/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/magnus919/agent-skills/dspy"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/dspy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,873 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00072	$0.01873
Opus 5	$0.00036	$0.00937
Sonnet 5	$0.00014	$0.00375
Haiku 4.5	$0.00007	$0.00187

Measured 9d ago against content hash 3ee9a82a13ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

dspy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/check-setup.py, templates/classification.py, templates/multi-step.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

dspy/SKILL.md · 146 lines

How it starts

The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DSPy Expert Skill

DSPy is a compiler for prompt programs, not a chain or RAG framework. You write Python programs with typed signatures and DSPy optimizes the prompts automatically.

⚠️ DSPy is NOT a chain framework. It does not use prompt | model | parser. It does not have LCEL. DSPy operates at a different layer: you define a program with Python control flow and typed signatures, then the compiler optimizes the prompts against a metric. If you reach for DSPy expecting LangChain-style composition, you are reaching for the wrong tool.

Think of it as PyTorch for LMs — you define the architecture, the compiler tunes the weights (prompts).

Core Paradigm

Read this first. It is the most important thing to understand about DSPy.

import dspy

# 1. Configure the LM
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

# 2. Define a signature (input/output schema)
class QASignature(dspy.Signature):
    """Answer questions concisely."""
    question: str = dspy.InputField()
    answer: str = dspy.OutputField()

# 3. Build a program using modules
qa = dspy.ChainOfThought(QASignature)

# 4. Compile against a metric
optimizer = dspy.MIPROv2(metric=dspy.answer_exact_match)
compiled_qa = optimizer.compile(qa, trainset=trainset, num_trials=25)

# 5. Use the compiled program (portable artifact)
answer = compiled_qa(question="What is DSPy?").answer

Core Principles

DSPy is a compiler, not a chain framework. You define the program structure with Python control flow and typed signatures. The compiler optimizes the prompts. This is fundamentally different from LangChain's explicit prompt composition.
Signatures define the task. Input/output field pairs with optional descriptions are the task definition. The syntax is input1, input2 -> output1, output2.
Modules are program components. dspy.Predict (direct), dspy.ChainOfThought (reasoning), dspy.ReAct (tool-use), and custom dspy.Module subclasses. Compose them with Python control flow (if/for/while).

Read the full file on GitHub · 146 lines

Files

What ships with it

15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

9d ago Changed · +1 lines · +12 tokens per session 3ee9a82a13ff
13d ago First seen · 145 lines · 60 tokens per session scan A c22f801e7f71

Subscribe to this mod's changes

dspy is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 1,873 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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