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FDEOps

Skill · GitHub · ★ 944 · graded 2026-10-23Source linked

Forward deployed engineering skills for AI coding agents.

No outside reports on file

FDEOps lets AI agents execute real commands on production systems, so it requires strict access controls, careful monitoring, and clear understanding of what your agent is actually doing.

Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee.

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What it can access

  • Local filesNo local folder access notedLOW
  • Command executionRuns shell or system commandsREVIEW
  • Network reachConnects to outside service APINOTED
  • CredentialsNo API token requiredLOW
  • Package sourceGitHub (suboss87/FDEOps)PUBLIC

Inferred from this listing’s public package details and description. Check the project’s source before installing.

Install

No npm or PyPI package is listed. Install from source: github.com/suboss87/FDEOps ↗

All setup options ↓

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Everything known

We found nothing about this project in the sources we read (last checked 2026-10-30). That does not mean it has no problems.

0 incidents · 0 advisories · 0 news articles · 0 community threads · checked 2026-10-30

Popularity grade: C (from public signals such as stars and downloads, not a code review). Graded 2026-10-23.

Sources we read: OSV, GitHub Advisory Database, CISA Known Exploited Vulnerabilities, 44 security news feeds and our incident library. Last checked 2026-10-30.

Check a specific version →

About FDEOps

FDEOps provides engineering skills for AI coding agents to work in deployed environments. It appears designed to help AI systems perform forward-deployed engineering tasks, though specific capabilities are not fully documented in available sources.

Who it’s for

Developers building AI agents that need to operate in production or field environments. Teams looking to extend AI coding assistants with deployment and operations capabilities.

Alternatives

  • Ansible (automation framework with safer, declarative operations)
  • Terraform (infrastructure-as-code tool with audit trails)
  • GitHub Actions (CI/CD platform with built-in access controls)
  • OpenAI Assistants with Code Interpreter (sandboxed code execution environment)

Setup

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

Follow the project's README at https://github.com/suboss87/FDEOps (opens external site) for installation and configuration instructions, as the specific setup process is not detailed in public summaries.

Why this grade

CC — Moderate risk signals

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

Popularity and upkeep

32/100
GitHub stars
944
Listing last refreshed
2026-10-29

These are public popularity signals, not a maintenance check. Look at the repository’s commit history and open issues.

Risks to watch for

  • Code execution access: Gives an AI agent ability to run commands in deployed systems, which could modify or damage production environments if misused
  • Credential exposure: May interact with deployment keys, API tokens, or authentication systems that could be leaked if the agent is compromised
  • Blind command execution: AI agents can make mistakes; malformed commands could affect live systems unintentionally
  • Prompt injection attacks: Malicious user input might trick the agent into running unintended deployment operations
  • Audit trail gaps: May not log all operations clearly, making it hard to trace what the AI did in your systems

How to lower the risk

Least-privilege permissions

  • Grant the agent only the specific deployment actions it needs, not full system access
  • Run deployments in sandbox or staging environments first, never directly in production

Network isolation

  • Restrict the agent's outbound connections to known deployment targets only
  • Use VPC or firewall rules to prevent reaching unintended systems

Monitoring

  • Log every command the agent executes and review logs regularly
  • Set up alerts for unusual operations or failed commands

Red flags to watch for

  • Agent executing commands you didn't explicitly request
  • Attempts to access credentials or configuration files
  • Network traffic to unexpected hosts or services

Questions

How do I install FDEOps?

FDEOps does not publish a standalone npm or PyPI package. Clone or follow the setup instructions in its source repository at github.com/suboss87/FDEOps.

Are there security reports about FDEOps?

No reports found for this package in our research (last checked 2026-10-30). FDEOps currently holds Grade C (Moderate risk signals) based on public adoption and transparency signals. Key risk areas to watch include code execution access, credential exposure, blind command execution.

Is FDEOps actively maintained?

FDEOps has 944 GitHub stars. Our directory data was last refreshed on 2026-10-29. Check recent commit activity, release tags, and open issues at github.com/suboss87/FDEOps before pinning it in production.

What are alternatives to FDEOps?

Notable alternatives to FDEOps include Ansible, Terraform, GitHub Actions, OpenAI Assistants with Code Interpreter. You can also compare FDEOps side by side with other skills in the Related tools section on this page.

README badge and team notes

Maintain or use FDEOps? Show its live grade in your GitHub README, docs site, or run a check from the terminal before you install.

AgentArmorHQ Risk Grade C for FDEOps

GitHub README Markdown:

markdown
[![AgentArmorHQ Risk Grade for FDEOps](https://agentarmorhq.com/api/badge/suboss87-fdeops)](https://agentarmorhq.com/skills/suboss87-fdeops)

HTML embed:

html
<a href="https://agentarmorhq.com/skills/suboss87-fdeops"><img src="https://agentarmorhq.com/api/badge/suboss87-fdeops" alt="AgentArmorHQ Risk Grade for FDEOps" height="20" /></a>

Terminal check:

terminal
curl -fsSL https://agentarmorhq.com/api/cli | node --input-type=module - check "suboss87-fdeops"

Share with your security lead

Need sign-off to use FDEOps? Copy this note for Slack, a ticket or a pull request.

### AgentArmorHQ Security Review Memo: FDEOps
- **Listing URL:** https://agentarmorhq.com/skills/suboss87-fdeops
- **Type:** AI agent skill
- **Risk Grade:** Grade C (Moderate risk signals)
- **Outside Security Advisories on File:** 0
- **Public Adoption:** 944 GitHub stars

#### Permission & Blast-Radius Signals
- **Local files:** No local folder access noted
- **Command execution:** Runs shell or system commands
- **Network reach:** Connects to outside service API
- **Credentials:** No API token required
- **Package source:** GitHub (suboss87/FDEOps)

#### Recommended Hardened Setup
- **Source Repo:** https://github.com/suboss87/FDEOps
- **Controls:** Pin an exact version in `.mcp.json`, scope access to a single workspace folder, and use a least-privilege read-only token where supported.

_Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee._

Key facts

Type
AI agent skill
Publisher
suboss87
Source code
github.com/suboss87/FDEOps
Est. context load
~50 tokens idle · ~450 tokens when active (on-demand skill)
Popularity
944 GitHub stars
Last updated
2026-10-29
Last graded
2026-10-23

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