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.
npx skills add davekilleen/Dex --skill dspy-rubygit clone --depth 1 https://github.com/davekilleen/DexWrote 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.
[](https://agentmods.dev/skills/davekilleen/dex/dspy-ruby)<a href="https://agentmods.dev/skills/davekilleen/dex/dspy-ruby"><img src="https://agentmods.dev/badge/skills/davekilleen/dex/dspy-ruby/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.
<a href="https://agentmods.dev/skills/davekilleen/dex/dspy-ruby"><img src="https://agentmods.dev/badge/skills/davekilleen/dex/dspy-ruby.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00081 | $0.03839 |
| Opus 5.5 | $0.00032 | $0.01536 |
| Sonnet 5.5 | $0.00016 | $0.00768 |
| Haiku 4.5 | $0.00008 | $0.00384 |
Grade A, and why
dspy-ruby 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 595 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSPy.rb Expert
Overview
DSPy.rb is a Ruby framework that enables developers to program LLMs, not prompt them. Instead of manually crafting prompts, define application requirements through type-safe, composable modules that can be tested, optimized, and version-controlled like regular code.
This skill provides comprehensive guidance on:
- Creating type-safe signatures for LLM operations
- Building composable modules and workflows
- Configuring multiple LLM providers
- Implementing agents with tools
- Testing and optimizing LLM applications
- Production deployment patterns
Core Capabilities
1. Type-Safe Signatures
Create input/output contracts for LLM operations with runtime type checking.
When to use: Defining any LLM task, from simple classification to complex analysis.
Quick reference:
class EmailClassificationSignature < DSPy::Signature
description "Classify customer support emails"
input do
const :email_subject, String
const :email_body, String
end
output do
const :category, T.enum(["Technical", "Billing", "General"])
const :priority, T.enum(["Low", "Medium", "High"])
end
end
Templates: See assets/signature-template.rb for comprehensive examples including:
- Basic signatures with multiple field types
- Vision signatures for multimodal tasks
- Sentiment analysis signatures
- Code generation signatures
Best practices:
- Always provide clear, specific descriptions
- Use enums for constrained outputs
- Include field descriptions with
desc:parameter - Prefer specific types over generic String when possible
Full documentation: See references/core-concepts.md sections on Signatures and Type Safety.
2. Composable Modules
Build reusable, chainable modules that encapsulate LLM operations.
When to use: Implementing any LLM-powered feature, especially complex multi-step workflows.
Quick reference:
class EmailProcessor < DSPy::Module
def initialize
super
@classifier = DSPy::Predict.new(EmailClassificationSignature)
end
def forward(email_subject:, email_body:)
@classifier.forward(
email_subject: email_subject,
email_body: email_body
)
end
end
What ships with it
6 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.
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.
- 1mo ago First seen · 595 lines · 81 tokens per session scan A 303a4eb7783e
dspy-ruby is a skill published in the GitHub repository davekilleen/Dex (500 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 3,839 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
rubyllm
Build and maintain Ruby or Rails applications with the RubyLLM AI framework. Use for chats, agents, tools, MCP servers, structured output, judgments, evaluations, media generation, transcription, OCR, moderation, embeddings, reranking, tracing, Rails integration, and RubyLLM upgrades; not for contributing to the…
telnyx-ai-assistants-ruby
AI voice assistants with custom instructions, knowledge bases, and tool integrations.
telnyx-ai-inference-ruby
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Ruby SDK examples.
pandas-helper
Master pandas operations — DataFrames, Series, groupby, merge, time-series resampling, and memory optimization.
pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
accelerate
Run PyTorch training across GPUs with minimal changes.