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 agentmods add skills/yezannnnn/agentgroup/tech-stack-evaluatornpx skills add yezannnnn/agentGroup --skill tech-stack-evaluatorgit clone --depth 1 https://github.com/yezannnnn/agentGroupWrote 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/yezannnnn/agentgroup/tech-stack-evaluator)<a href="https://agentmods.dev/skills/yezannnnn/agentgroup/tech-stack-evaluator"><img src="https://agentmods.dev/badge/skills/yezannnnn/agentgroup/tech-stack-evaluator.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 | $0.00047 | $0.00949 |
| Opus 5 | $0.00023 | $0.00475 |
| Sonnet 5 | $0.00009 | $0.00190 |
| Haiku 4.5 | $0.00005 | $0.00095 |
Grade A, and why
tech-stack-evaluator scanned grade A with 0 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 4d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technology Stack Evaluator
Evaluate and compare technologies, frameworks, and cloud providers with data-driven analysis and actionable recommendations.
Table of Contents
Capabilities
| Capability | Description |
|---|---|
| Technology Comparison | Compare frameworks and libraries with weighted scoring |
| TCO Analysis | Calculate 5-year total cost including hidden costs |
| Ecosystem Health | Assess GitHub metrics, npm adoption, community strength |
| Security Assessment | Evaluate vulnerabilities and compliance readiness |
| Migration Analysis | Estimate effort, risks, and timeline for migrations |
| Cloud Comparison | Compare AWS, Azure, GCP for specific workloads |
Quick Start
Compare Two Technologies
Compare React vs Vue for a SaaS dashboard.
Priorities: developer productivity (40%), ecosystem (30%), performance (30%).
Calculate TCO
Calculate 5-year TCO for Next.js on Vercel.
Team: 8 developers. Hosting: $2500/month. Growth: 40%/year.
Assess Migration
Evaluate migrating from Angular.js to React.
Codebase: 50,000 lines, 200 components. Team: 6 developers.
Input Formats
The evaluator accepts three input formats:
Text - Natural language queries
Compare PostgreSQL vs MongoDB for our e-commerce platform.
YAML - Structured input for automation
comparison:
technologies: ["React", "Vue"]
use_case: "SaaS dashboard"
weights:
ecosystem: 30
performance: 25
developer_experience: 45
JSON - Programmatic integration
{
"technologies": ["React", "Vue"],
"use_case": "SaaS dashboard"
}
Analysis Types
Quick Comparison (200-300 tokens)
- Weighted scores and recommendation
- Top 3 decision factors
- Confidence level
Standard Analysis (500-800 tokens)
- Comparison matrix
- TCO overview
- Security summary
What ships with it
14 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.
- assets/expected_output_comparison.json 1.9 KB
- assets/sample_input_structured.json 1.1 KB
- assets/sample_input_tco.json 1.1 KB
- assets/sample_input_text.json 237 B
- references/examples.md 10 KB
- references/metrics.md 6.5 KB
- references/workflows.md 8.0 KB
- scripts/ecosystem_analyzer.py 16 KB runs code
- scripts/format_detector.py 12 KB runs code
- scripts/migration_analyzer.py 20 KB runs code
- scripts/report_generator.py 16 KB runs code
- scripts/security_assessor.py 17 KB runs code
- scripts/stack_comparator.py 12 KB runs code
- scripts/tco_calculator.py 16 KB runs code
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.
- 4d ago First seen · 185 lines · 47 tokens per session scan A e87d3b2bfb27
tech-stack-evaluator is a skill published in the GitHub repository yezannnnn/agentGroup (149 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 949 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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