C

C — Moderate risk signals Graded October 16, 2026Several concerns worth a closer look before you install.Based on public adoption and transparency signals as of October 16, 2026. Our opinion, not a code audit or security guarantee.

AI agent skill · Developer tools, Web & browsing

awesome-ChatGPT-repositories

A curated list of open source GitHub repositories related to ChatGPT, the OpenAI API, and Codex. Searchable via Claude Code and Codex skills.

Bottom line: Signal-based grade (3/7 points): well starred on GitHub, public source code. This reflects adoption and transparency, not a code audit.

Popularity score
40/100
Rank
Not ranked: outside the 500 most popular tools
GitHub stars
3.3k

What it does

Aggregates open-source GitHub repositories related to ChatGPT, OpenAI API, and Codex into a searchable, curated index. Designed to help developers quickly discover community-built tools, libraries, and examples for working with OpenAI models.

Who it’s for

Developers and technical teams building on OpenAI APIs who want a centralized reference for open-source implementations. Enterprises evaluating third-party ChatGPT integrations and AI agent tooling.

Copy-paste setup

Check these steps against the project’s own instructions before running anything.

This is a read-only GitHub repository index, not an npm package or configurable connector. To use: Visit https://github.com/taishi-i/awesome-ChatGPT-repositories (opens external site) and browse the curated list, or fork/clone for local reference. No installation or API keys required. For integration as an MCP connector or agent skill, verify against the repo README for recommended patterns.

Risks

  • Third-party code quality varies widely—repositories are user-submitted with no enforcement of security audits or best practices
  • No guarantee that linked repos are actively maintained or have addressed known vulnerabilities
  • Supply-chain risk if integrated code includes unreviewed dependencies or abandoned libraries
  • Potential for outdated API examples that encourage insecure patterns (e.g., hardcoded keys, overpermissioned scopes)
  • Curation is community-driven with limited formal governance; malicious or low-quality repos could theoretically be added

How to stay secure

Least-privilege permissions

  • Limit API key access: Only generate OpenAI or GitHub API keys with the minimum permissions needed for the task (read-only when possible, not full account access)
  • Restrict repository cloning: Don't automatically download and run code from the list—manually review it first in a sandboxed environment (a separate, isolated computer or virtual machine)
  • Control integration scope: When integrating code into production systems, isolate it in a restricted container or sandbox rather than giving it direct access to your main infrastructure
  • Rotate credentials regularly: Change any API keys or secrets every 90 days, and immediately if a repository is compromised

Network isolation

  • Use a test environment: Run code from unfamiliar repositories on an isolated test network, not connected to production systems or sensitive data
  • Block outbound connections by default: Configure firewalls to prevent repositories from making unexpected network requests to unknown servers
  • Disable external dependencies where possible: Remove or replace code libraries that automatically download additional packages from the internet without your approval
  • Audit before production deployment: Only promote code to your live systems after security review in an isolated environment

Monitoring

  • Track repository activity: Check the GitHub repositories quarterly for updates, abandoned projects (no commits in 12+ months), or sudden suspicious changes
  • Log all API usage: Record which code is calling OpenAI or other APIs, how often, and what data is being processed
  • Set up alerts: Configure notifications if API keys are used abnormally (e.g., unusual locations, spikes in requests, unexpected data access)
  • Scan for vulnerabilities: Use automated tools monthly to check downloaded code for known security issues in its dependencies

Red flags to watch for

  • Unmaintained repositories: Avoid code that hasn't been updated in over a year or has unresolved security issues reported by users
  • Hardcoded secrets: Never use code that contains API keys, passwords, or tokens visible in the actual code files
  • Suspicious dependencies: Be wary of repositories that require many third-party libraries you don't recognize or that download packages from unusual sources
  • No clear license or governance: Avoid projects with no license file, no documentation, or no clear owner—they may not be trustworthy or legally safe to use
  • Excessive permissions requested: Don't integrate code that asks for overpermissioned API scopes (e.g., requesting full account access when it only needs read permissions)

Alternatives

  • OpenAI's official documentation and examples (github.com/openai/openai-cookbook)
  • Anthropic's official resources for Claude and MCP connectors
  • Hugging Face Models Hub for transformer-based alternatives and curated model repos
  • Product Hunt or GitHub Topics for real-time, community-voted AI tool discovery

Sources