Eigent is an open-source desktop application for creating and running teams of AI agents that automate complex workflows locally. It supports focused tasks and coordinated multi-agent work for people who want a free, customizable AI workforce.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add eigent-ai/eigent --skill skill-security-auditorgit clone --depth 1 https://github.com/eigent-ai/eigentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/eigent-ai/eigent/skill-security-auditor)<a href="https://agentmods.dev/skills/eigent-ai/eigent/skill-security-auditor"><img src="https://agentmods.dev/badge/skills/eigent-ai/eigent/skill-security-auditor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/eigent-ai/eigent/skill-security-auditor"><img src="https://agentmods.dev/badge/skills/eigent-ai/eigent/skill-security-auditor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 149 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00114 | $0.01652 |
| Opus 5.5 | $0.00046 | $0.00661 |
| Sonnet 5.5 | $0.00023 | $0.00330 |
| Haiku 4.5 | $0.00011 | $0.00165 |
Grade A, and why
skill-security-auditor scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
os.system(f"ping {hostname}") How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Auditor Guide
Overview
This guide covers security auditing workflows for source code, dependencies, and configurations. For detailed vulnerability patterns and detection rules, see references/vulnerability-patterns.md. For secrets detection patterns, see references/secrets-patterns.md.
Quick Start
Run the bundled scan script against a project directory:
python scripts/scan_project.py /path/to/project
This performs a lightweight scan for common issues: hardcoded secrets, dangerous function calls, and insecure patterns. For deeper analysis, follow the workflows below.
Testing the scripts
python scripts/scan_project.py /path/to/some/project --format text
python scripts/scan_secrets.py /path/to/some/project --format text
Audit Workflow
1. Reconnaissance
Before auditing, understand the project:
# Identify languages, frameworks, and entry points
find . -type f -name "*.py" -o -name "*.js" -o -name "*.ts" -o -name "*.go" -o -name "*.java" | head -20
cat package.json pyproject.toml requirements.txt go.mod pom.xml 2>/dev/null
Key questions:
- What frameworks are used? (Express, Django, Flask, Spring, etc.)
- Where are the entry points? (routes, controllers, API handlers)
- How is authentication handled?
- What external services are called?
- Is user input accepted? Where?
2. Secrets Detection
Scan for hardcoded credentials, API keys, and tokens. See references/secrets-patterns.md for the full pattern list.
python scripts/scan_secrets.py /path/to/project
Common patterns to check:
- API keys and tokens in source files
- Database connection strings with embedded passwords
- Private keys or certificates committed to the repo
.envfiles or config files with plaintext secrets- Secrets in CI/CD configuration files
3. Vulnerability Scanning
OWASP Top 10 Checklist
| # | Category | What to Look For |
|---|---|---|
| A01 | Broken Access Control | Missing auth checks, IDOR, privilege escalation |
| A02 | Cryptographic Failures | Weak algorithms, plaintext storage, missing TLS |
| A03 | Injection | SQL, NoSQL, OS command, LDAP, XSS |
| A04 | Insecure Design | Missing rate limits, business logic flaws |
| A05 | Security Misconfiguration | Debug mode, default credentials, verbose errors |
| A06 | Vulnerable Components | Outdated dependencies with known CVEs |
| A07 | Auth Failures | Weak passwords, missing MFA, session issues |
| A08 | Data Integrity Failures | Insecure deserialization, unsigned updates |
| A09 | Logging Failures | Missing audit logs, sensitive data in logs |
| A10 | SSRF | Unvalidated URLs in server-side requests |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 219 lines · 114 tokens per session scan A 8fb34b36db5e
skill-security-auditor is a skill published in the GitHub repository eigent-ai/eigent (15,622 stars, last pushed today), licensed Apache-2.0. It adds 114 tokens to every session and 1,652 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
goga-cookbook
Principles for applying DSL specification in cell and CODEMANIFEST design.
goga-plan-by-design
Compile a design document into a ralphex execution plan.
goga-define-experience
Define the user experience required to achieve the established product goals and solve the identified problem.
goga-review-plan
Verify execution plan completeness and correctness.
goga-task-by-specifying
Interactive task formulation from a raw request.
goga-accept-manifest-review
Verify each Cell's CODEMANIFEST against the implementation.