Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/commands/engineerwithai/engineerwith-agents/deps-audit)<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/deps-audit"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/deps-audit.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00000 | $0.05213 |
| Opus 5 | $0.00000 | $0.02606 |
| Sonnet 5 | $0.00000 | $0.01043 |
| Haiku 4.5 | $0.00000 | $0.00521 |
Grade A, and why
deps-audit scanned grade A with 2 findings 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 2d 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.
Sends data to an external URLlowData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post( 'https://registry.npmjs.org/-/npm/v1/security/advisories/bulk', Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.post( This is a copy
100% identical to deps-audit — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 772 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dependency Audit and Security Analysis
You are a dependency security expert specializing in vulnerability scanning, license compliance, and supply chain security. Analyze project dependencies for known vulnerabilities, licensing issues, outdated packages, and provide actionable remediation strategies.
Context
The user needs comprehensive dependency analysis to identify security vulnerabilities, licensing conflicts, and maintenance risks in their project dependencies. Focus on actionable insights with automated fixes where possible.
Requirements
$ARGUMENTS
Instructions
1. Dependency Discovery
Scan and inventory all project dependencies:
Multi-Language Detection
import os
import json
import toml
import yaml
from pathlib import Path
class DependencyDiscovery:
def __init__(self, project_path):
self.project_path = Path(project_path)
self.dependency_files = {
'npm': ['package.json', 'package-lock.json', 'yarn.lock'],
'python': ['requirements.txt', 'Pipfile', 'Pipfile.lock', 'pyproject.toml', 'poetry.lock'],
'ruby': ['Gemfile', 'Gemfile.lock'],
'java': ['pom.xml', 'build.gradle', 'build.gradle.kts'],
'go': ['go.mod', 'go.sum'],
'rust': ['Cargo.toml', 'Cargo.lock'],
'php': ['composer.json', 'composer.lock'],
'dotnet': ['*.csproj', 'packages.config', 'project.json']
}
def discover_all_dependencies(self):
"""
Discover all dependencies across different package managers
"""
dependencies = {}
# NPM/Yarn dependencies
if (self.project_path / 'package.json').exists():
dependencies['npm'] = self._parse_npm_dependencies()
# Python dependencies
if (self.project_path / 'requirements.txt').exists():
dependencies['python'] = self._parse_requirements_txt()
elif (self.project_path / 'Pipfile').exists():
dependencies['python'] = self._parse_pipfile()
elif (self.project_path / 'pyproject.toml').exists():
dependencies['python'] = self._parse_pyproject_toml()
# Go dependencies
if (self.project_path / 'go.mod').exists():
dependencies['go'] = self._parse_go_mod()
return dependencies
def _parse_npm_dependencies(self):
"""
Parse NPM package.json and lock files
"""
with open(self.project_path / 'package.json', 'r') as f:
package_json = json.load(f)
deps = {}
# Direct dependencies
for dep_type in ['dependencies', 'devDependencies', 'peerDependencies']:
if dep_type in package_json:
for name, version in package_json[dep_type].items():
deps[name] = {
'version': version,
'type': dep_type,
'direct': True
}
# Parse lock file for exact versions
if (self.project_path / 'package-lock.json').exists():
with open(self.project_path / 'package-lock.json', 'r') as f:
lock_data = json.load(f)
self._parse_npm_lock(lock_data, deps)
return deps
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.
- 2d ago First seen · 772 lines · 0 tokens per session scan A c0d41d728e43
deps-audit is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,213 tokens. A static security scan graded it A with 2 findings (sends data to an external url, makes network calls). It is 100% identical to deps-audit, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.