Verified:GitHub

techstack-correlation

Cross-validate frontend, backend, and infra signals into a vetted technology list with confidence scores.

3|1|Updated May 26, 2026

Search Tags:#conflict-resolution #tech-stack #osint #repo-analysis #confidence-scoring #technology-detection #signal-correlation

Ready to run

npx skills add https://github.com/LeoWSY-hashblue/-communitytools-custom --skill techstack-correlation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: techstack-correlation
Source: https://github.com/LeoWSY-hashblue/-communitytools-custom/tree/main/skills/techstack-identification/correlation
Command: npx skills add https://github.com/LeoWSY-hashblue/-communitytools-custom --skill techstack-correlation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Cross-validate signals from independent detection domains to produce a finalized technology list with quantified confidence.

Core Features & Use Cases

Cross-validate signals from frontend, backend, infra, and repository sources to corroborate technologies
Score confidence per technology (High/Medium/Low) using multi-factor weighting
Resolve conflicts between signals (version mismatches, mutually exclusive tech, temporal changes) and flag unresolved cases for manual review
Use case: consolidate a live inventory of technologies across a project to guide reporting and remediation

Quick Start

Provide a combined tech-stack signal set from frontend, backend, infra, and repository sources to output a ranked, conflict-aware technology list.

Frequently Asked Questions about techstack-correlation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I cross-validate conflicting tech stack signals from different detection sources?▼

Cross-validate tech stack signals by ingesting detection data from frontend, backend, infra, and repository sources to corroborate technologies and produce a finalized list with quantified confidence scores.

What is tech stack confidence scoring and how does it resolve signal conflicts?▼

Tech stack confidence scoring uses multi-factor weighting across independent detection domains to resolve conflicts like version mismatches or mutually exclusive frameworks, flagging unresolved cases for manual review.

Can I use repo analysis to consolidate a live inventory of project technologies?▼

Yes, you can combine repo analysis with frontend and infra signals to consolidate a live inventory of technologies, outputting a ranked list that includes technology, category, sources, and confidence levels.

What is the best way to resolve version mismatches detected across multiple technology domains?▼

The best way to resolve version mismatches is applying multi-factor signal correlation across detection domains, which scores confidence per technology and flags temporal changes or mutually exclusive tech for manual review.

Why does technology detection output vary between frontend and backend sources?▼

Technology detection varies between sources due to independent signal domains capturing different data, requiring cross-validation to resolve conflicts, corroborate findings, and output a vetted technology list with quantified confidence.

Do I need to provide signals from all detection domains to score technology confidence?▼

You should provide a combined tech stack signal set from frontend, backend, infra, and repository sources to maximize accuracy, though the system applies multi-factor weighting to corroborate whatever signals are provided.