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Github Kb

Checked
lecion·vlatest·backend

Access and summarize relevant information from your team's public GitHub repositories using Lecion’s github-kb skill.

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 lecion/github-kb --skill github-kb -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

lecion

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 lecion/github-kb at 16f7bbeb5a.

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

Commit
16f7bbeb5a
Manifest
SKILL.md
Scanned
2 files, 175 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 directly with GitHub repositories, allowing them to search codebases, retrieve documentation, and manage repository metadata. It bridges the gap between natural language queries and specific GitHub data structures.

When to use it

  • You need an agent to locate specific files or functions within a large open-source project.
  • You want your AI assistant to summarize recent changes or pull requests in a specific repository.
  • Your workflow requires querying repository details like stars, forks, or contributor lists without manual browsing.

Key capabilities

  • Search and navigate GitHub repositories programmatically.
  • Retrieve and parse code documentation and file contents.
  • Access repository metadata such as statistics and commit history.

Example prompts

  • "Find the main configuration file in the lecion/github-kb repository and summarize its settings."
  • "List the top three contributors to this project based on recent commits."
  • "Search the codebase for any instances where the API key is hardcoded."

Tips & gotchas

Ensure the AI agent has appropriate read permissions for the target repositories, as some private data may be inaccessible. Performance may vary depending on the size of the repository being queried; large monorepos might require more time to process.