Maintainer of this skill?
Automated security scan — not a certification.
by Observal
Observal is self-hosted registry for your coding agent extensions with a built in insight engine. Setup Observal, define the scope and share your Skills, MCPs and Agents with your peers.
# Add to your Claude Code skills
git clone https://github.com/Observal/ObservalGuides for using ai agents skills like Observal.
Last scanned: 6/28/2026
{
"issues": [
{
"file": "README.md",
"line": 83,
"type": "remote-install",
"message": "Install command (remote install script piped to a shell — review the source before running): \"curl -fsSL https://raw.githubusercontent.com/Observal/Observal/main/install-serv\"",
"severity": "low"
}
],
"status": "PASSED",
"scannedAt": "2026-06-28T07:51:16.003Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how Observal compares with popular alternatives.
Observal is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Observal. Observal is self-hosted registry for your coding agent extensions with a built in insight engine. Setup Observal, define the scope and share your Skills, MCPs and Agents with your peers. It has 4,533 GitHub stars.
Yes. Observal passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Observal/Observal" and add it to your Claude Code skills directory (see the Installation section above).
Observal is primarily written in Python. It is open-source under Observal on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Observal against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
Set up once, and let your coding agent use Observal through observal-cli.
If you find Observal useful, please consider giving it a star. It helps others discover the project and keeps development going.
Observal is the control plane and system of record for internal internal coding agent resources. Every tech-forward organization today creates internal Skills, Agents files, MCP clients. Though the creation of these coding agent resources has been prolific, peer adoption and usage is sparse. Coding agent users today end up creating their own version of such resouces without reusing existing packages.
The cause is largely due to two problems:
Lack of a discoverability layer
Organizations store their coding agent resources in siloed git repositories with little to no documentation. Users are not able to locate existing coding agent resources and this results in multiple developers creating the same/similar resources again.
Missing feedback loop
Any software where usage patterns are not understood and the principle of user-centric development is violated tends to fade out. Such is the problem with development of MCP clients, Skills and Agent files. Authors publish and maintain these resources with little visibility into how they're actually used. Additionally, Coding agent failures don't trigger static error codes: they hallucinate or provide subtly incorrect answers. This leaves users clueless about what went wrong compounding the feedback problem.
Observal solves this by providing a centralized discovery layer for coding agent resources alongside useful insights into usage patterns. It turns silent failures into actionable feedback, ensuring internal resources are continuously optimized for the people using them.
Observal supports Claude Code, Cursor, Kiro, Pi, Copilot, Codex, OpenCode, and other tools.
