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Nnsight Remote Interpretability

Checked
davila7·vlatest·ai-ml·MIT

Nnsight Remote Interpretability analyzes model behavior in real-time across deployed environments, revealing insights for debugging and trust.

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 nnsight-remote-interpretability -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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

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

  • 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/mechanistic-interpretability-nnsight/SKILL.md
Scanned
4 files, 1,064 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 allows you to remotely interpret and debug large language models (LLMs). It provides insights into the LLM's reasoning process, highlighting which parts of the input are most influential in generating a specific output. This enables developers to understand and potentially correct unexpected or undesirable behavior within complex AI systems.

When to use it

  • Debugging Unexpected Behavior: When an LLM produces incorrect or nonsensical outputs, this skill can help pinpoint the source of the problem.
  • Understanding Model Reasoning: Gain a deeper understanding of how an LLM arrives at its conclusions, particularly in complex decision-making scenarios.
  • Improving Prompt Engineering: Identify which parts of your prompts are driving specific responses, allowing for more targeted and effective prompt design.
  • Analyzing Bias: Investigate potential biases within the model's reasoning process by examining how different inputs influence outputs.

Key capabilities

  • Remote LLM interpretability
  • Input attribution highlighting
  • Debugging assistance
  • Reasoning process visualization

Example prompts

  • "Analyze this prompt: 'Write a poem about cats.' and explain which words are most influential."
  • "Debug the following conversation with the model, focusing on why it gave an incorrect answer to question 3."
  • “Show me how this input affects the LLM’s output.”

Tips & gotchas

This skill requires access to a compatible LLM and potentially specific configuration depending on the environment. The interpretability visualizations can be complex and may require some familiarity with machine learning concepts to fully understand.