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Dspy

Listed
davila7·vlatest·ai-ml·MIT

Dspy captures screenshots of your current web page in real-time, allowing you to easily share or analyze website designs and flows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill dspy -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • cli-tool/components/skills/ai-research/prompt-engineering-dspy/SKILL.md:32pip install straight from a URL
      pip install git+https://github.com/stanfordnlp/dspy.git

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/prompt-engineering-dspy/SKILL.md
Scanned
4 files, 2,122 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to interact with DSPy, a framework designed to optimize LLM applications through programmatic prompt engineering and modular component composition. It allows developers to define, test, and refine language model behaviors using formal specifications rather than manual prompt tweaking.

When to use it

  • You need to systematically improve the accuracy of an LLM application across many different inputs.
  • Your workflow requires swapping out underlying models or prompts without rewriting the entire application logic.
  • You want to leverage automated optimization techniques (like MIPRO) to find better prompts and signatures for your tasks.
  • You are building complex pipelines where components need to be composed and reused consistently.

Key capabilities

  • Integration with the DSPy framework for advanced LLM orchestration.
  • Support for defining modular components and signatures for task specification.
  • Automated optimization of prompts and model configurations based on evaluation metrics.
  • Ability to test and refine applications using formal verification methods.

Example prompts

  • "Optimize this retrieval-augmented generation pipeline using DSPy's MIPRO technique."
  • "Create a modular signature for extracting structured data from unstructured text documents."
  • "Refine the prompt for this classification task to improve F1 score on the validation set."

Tips & gotchas

DSPy requires a solid understanding of Python and LLM internals; it is not a simple no-code tool. Ensure you have sufficient compute resources, as optimization processes can be resource-intensive compared to standard inference calls.