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.
git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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-upgrade)<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/deps-upgrade"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/deps-upgrade.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 | $0.04492 |
| Opus 5 | $0.00000 | $0.02246 |
| Sonnet 5 | $0.00000 | $0.00898 |
| Haiku 4.5 | $0.00000 | $0.00449 |
Grade C, and why
deps-upgrade scanned grade C with 3 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 5d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf node_modules Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -f http://localhost:3000/health || exit 1 Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
npm_output = subprocess.run( This is a copy
100% identical to deps-upgrade — 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 — 751 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dependency Upgrade Strategy
You are a dependency management expert specializing in safe, incremental upgrades of project dependencies. Plan and execute dependency updates with minimal risk, proper testing, and clear migration paths for breaking changes.
Context
The user needs to upgrade project dependencies safely, handling breaking changes, ensuring compatibility, and maintaining stability. Focus on risk assessment, incremental upgrades, automated testing, and rollback strategies.
Requirements
$ARGUMENTS
Instructions
1. Dependency Update Analysis
Assess current dependency state and upgrade needs:
Comprehensive Dependency Audit
import json
import subprocess
from datetime import datetime, timedelta
from packaging import version
class DependencyAnalyzer:
def analyze_update_opportunities(self):
"""
Analyze all dependencies for update opportunities
"""
analysis = {
'dependencies': self._analyze_dependencies(),
'update_strategy': self._determine_strategy(),
'risk_assessment': self._assess_risks(),
'priority_order': self._prioritize_updates()
}
return analysis
def _analyze_dependencies(self):
"""Analyze each dependency"""
deps = {}
# NPM analysis
if self._has_npm():
npm_output = subprocess.run(
['npm', 'outdated', '--json'],
capture_output=True,
text=True
)
if npm_output.stdout:
npm_data = json.loads(npm_output.stdout)
for pkg, info in npm_data.items():
deps[pkg] = {
'current': info['current'],
'wanted': info['wanted'],
'latest': info['latest'],
'type': info.get('type', 'dependencies'),
'ecosystem': 'npm',
'update_type': self._categorize_update(
info['current'],
info['latest']
)
}
# Python analysis
if self._has_python():
pip_output = subprocess.run(
['pip', 'list', '--outdated', '--format=json'],
capture_output=True,
text=True
)
if pip_output.stdout:
pip_data = json.loads(pip_output.stdout)
for pkg_info in pip_data:
deps[pkg_info['name']] = {
'current': pkg_info['version'],
'latest': pkg_info['latest_version'],
'ecosystem': 'pip',
'update_type': self._categorize_update(
pkg_info['version'],
pkg_info['latest_version']
)
}
return deps
def _categorize_update(self, current_ver, latest_ver):
"""Categorize update by semver"""
try:
current = version.parse(current_ver)
latest = version.parse(latest_ver)
if latest.major > current.major:
return 'major'
elif latest.minor > current.minor:
return 'minor'
elif latest.micro > current.micro:
return 'patch'
else:
return 'none'
except:
return 'unknown'
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.
- 5d ago First seen · 751 lines · 0 tokens per session scan C 6c013368f829
deps-upgrade is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,492 tokens. A static security scan graded it C with 3 findings (recursive force delete, makes network calls, runs shell commands). It is 100% identical to deps-upgrade, 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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.