← Back to Skills Marketplace
alirezarezvani

Tech Stack Evaluator

by Alireza Rezvani · GitHub ↗ · v2.1.1 · MIT-0
cross-platform ⚠ suspicious
1547
Downloads
0
Stars
4
Active Installs
2
Versions
Install in OpenClaw
/install tech-stack-evaluator
Description
Technology stack evaluation and comparison with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, evaluating te...
Usage Guidance
This package appears to be a legitimate tech-evaluation tool, but there is a mismatch between the SKILL.md (which shows convenient CLI usage and implies automatic fetching of GitHub/npm/security data) and the provided Python modules (which look like library components that expect structured input). Before using it in production or granting it network/agent privileges: 1) Inspect the remaining scripts (especially security_assessor.py and stack_comparator.py) for any network calls, subprocess usage, or hidden endpoints (look for imports like requests, urllib, subprocess). 2) Confirm whether CLI wrappers or data-fetching code are present or must be added — the examples may be aspirational. 3) Run the scripts in an isolated environment with the provided sample inputs to verify behavior. 4) If you expect live metric collection, ask the author for documentation on authentication and endpoints; do not provide credentials until you confirm what is contacted and why. If you want, I can scan the remaining truncated files for network/subprocess calls and make the assessment more precise.
Capability Analysis
Type: OpenClaw Skill Name: tech-stack-evaluator Version: 2.1.1 The tech-stack-evaluator bundle is a legitimate tool designed for technology comparison, TCO calculation, and migration analysis. All analyzed Python scripts (such as stack_comparator.py, tco_calculator.py, and security_assessor.py) contain pure mathematical and logical implementations for scoring metrics based on provided input data. There is no evidence of network activity, shell execution, data exfiltration, or obfuscation. The SKILL.md instructions are strictly functional and do not attempt to manipulate the AI agent into performing unauthorized or harmful actions.
Capability Assessment
Purpose & Capability
Name/description (tech comparisons, TCO, security assessment) align with the provided scripts (comparator, TCO, migration, ecosystem, security). No required env vars or binaries are declared, which is proportionate to the stated purpose. However, SKILL.md examples imply the scripts will fetch live GitHub/npm metrics or be usable via CLI flags (e.g., `--technology react`), while the visible modules (e.g., ecosystem_analyzer.py, format_detector.py, migration_analyzer.py) are written as library classes/functions that accept data dicts rather than showing a network fetcher or CLI argument parsing — so there is a mild capability mismatch between documentation and code.
Instruction Scope
SKILL.md instructs running scripts with command-line flags and suggests automated retrieval of ecosystem/security metrics. The included source snippets mostly define classes and pure computation functions that expect structured input rather than performing network calls or having CLI entry points. The instructions therefore overstate automation (implied live data collection). This is not directly dangerous, but it is an incoherence: the agent or user may expect the skill to fetch external data automatically when the code appears to require pre-supplied metrics.
Install Mechanism
No install spec is provided and the skill is instruction-only plus local Python scripts. That keeps disk/write risk low. There are no external downloads, URL installs, or package manager installs in the repository metadata.
Credentials
The skill declares no required environment variables, credentials, or config paths — which is appropriate given the documented behavior. No files or variables appear to be requested that would be disproportionate to the task.
Persistence & Privilege
The skill is not set to always:true and does not request elevated persistence. It contains only local scripts and references sample input/data assets; there is no evidence it modifies other skills or global agent settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install tech-stack-evaluator
  3. After installation, invoke the skill by name or use /tech-stack-evaluator
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v2.1.1
v2.1.1: optimization, reference splits
v1.0.0
Initial release of Technology Stack Evaluator. - Supports comprehensive technology comparison with weighted scoring. - Calculates 5-year total cost of ownership (TCO), including hidden costs. - Assesses ecosystem health using GitHub and npm metrics. - Evaluates security and compliance readiness. - Estimates migration effort, risks, and timelines. - Provides multiple input formats: text, YAML, and JSON. - Includes command-line scripts for stack comparison, TCO, ecosystem health, security, and migration analysis.
Metadata
Slug tech-stack-evaluator
Version 2.1.1
License MIT-0
All-time Installs 4
Active Installs 4
Total Versions 2
Frequently Asked Questions

What is Tech Stack Evaluator?

Technology stack evaluation and comparison with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, evaluating te... It is an AI Agent Skill for Claude Code / OpenClaw, with 1547 downloads so far.

How do I install Tech Stack Evaluator?

Run "/install tech-stack-evaluator" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Tech Stack Evaluator free?

Yes, Tech Stack Evaluator is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Tech Stack Evaluator support?

Tech Stack Evaluator is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Tech Stack Evaluator?

It is built and maintained by Alireza Rezvani (@alirezarezvani); the current version is v2.1.1.

💬 Comments