frontend_inferencer_skill

This skill infers frontend technologies by analyzing signals from JavaScript, HTML, and CSS to identify frameworks and UI patterns.

Python

42

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill transilienceai/communitytools --skill frontend_inferencer

SKILL.md7.5 KB

Overview

This skill infers frontend technologies by analyzing JavaScript globals, DOM attributes, script and bundle patterns, HTML meta tags, and CSS class usage. It produces ranked technology candidates with signals, weights, implied technologies, and confidence estimates to guide security researchers and bug bounty hunters.

How this skill works

The skill consumes collected signals (javascript_signals, html_signals, http_signals) and scores known patterns for frameworks, meta-frameworks, CSS libraries, UI kits, state management, and build tools. It aggregates evidence from globals, DOM attributes, script URLs, and CSS class matches, computes weights, and returns inferred technologies plus implied stacks and a confidence range.

When to use it

Passive reconnaissance on a target web app during penetration testing or bug bounty triage
Rapid fingerprinting of frontend stacks to identify attack surface or CVE relevance
Prioritizing targets by technology familiarity (e.g., React/Next.js vs. legacy jQuery)
Enriching asset inventories with inferred frontend libraries and build tools
Automating tooling selection for dynamic analysis or exploit chains

Best practices

Collect comprehensive signals: globals, DOM attrs, script URLs, and CSS classes for higher confidence
Treat single indirect matches (one class or bundle fragment) as low confidence until corroborated
Map meta-frameworks to implied frameworks (Next.js => React) to expand the inferred stack
Report conflicting signals rather than discarding them; show weights and supporting evidence
Attempt version detection only when explicit version fields or devtools globals are accessible

Example use cases

Detect Next.js and React on a target to focus template-injection or SSR-related tests
Identify Tailwind CSS or Bootstrap to speed up UI-focused DOM manipulation proofs of concept
Spot legacy jQuery usage to prioritize XSS and unsanitized DOM API vectors
Find build tool markers (Vite, Webpack) to infer bundling patterns and source map likelihood

FAQ

Reliability depends on signal quantity and type: direct globals and version fields yield high confidence, bundle or class-only matches are medium to low.

Does it report implied technologies?

Yes. Meta-framework detections include implied frameworks and runtimes (for example, Next.js implies React and often Node.js).