context-detection_skill

This skill automatically detects project tech stack, frameworks, and patterns to inform targeted tooling and automation decisions.

0

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 jovermier/cc-stack-marketplace --skill context-detection

SKILL.md2.5 KB

Overview

This skill automatically detects a project's technology stack, frameworks, and development context. It produces a structured context object used to drive code-review, test generation, and automated skill selection. The goal is fast, reliable inference from repository files and code patterns.

How this skill works

The skill inspects the file system for lockfiles, config files, and common entry points (package.json, go.mod, pyproject.toml, next.config.js, etc.). It analyzes file extensions, import statements, and directory layout to infer languages, frameworks, testing tools, package managers, and whether the project exposes APIs or uses a database. Finally, it aggregates findings into a concise JSON context object for downstream automation.

When to use it

When running an automated skill pipeline to choose relevant code-review and test-generation agents
On initial repository intake to populate toolchains for CI and local developer tooling
Before running automated refactors or scaffolding to ensure framework-appropriate changes
When generating test suites to target detected testing frameworks and file locations

Best practices

Keep top-level configuration files (package.json, go.mod, pyproject.toml) present and up to date for reliable detection
Use conventional directory layouts (src/, app/, pages/) to improve accuracy of project-type classification
Include explicit devDependencies for test frameworks and type configs (jest, playwright, tsconfig) where applicable
Commit small representative sample files rather than removing language markers to avoid false negatives
Run detection early in CI to gate subsequent automated steps on confirmed context

Example use cases

Detect a Next.js + GraphQL frontend and select Next.js-aware linters, code reviewers, and Playwright test generation
Identify a mixed Go backend with go.mod and *_test.go files, then enable Go-specific test scaffolding and review agents
Find a Python FastAPI service and enable pytest generation plus database migration checks
Classify a fullstack repository to decide whether to provision both frontend and backend skills for end-to-end tests

FAQ

A compact JSON object listing detected languages, frameworks, testing tools, package manager, and flags like hasApi or hasDatabase.

How does the skill handle ambiguous repositories?

It uses multiple signals (files, imports, counts) and ranks detections by confidence; ambiguous results include multiple candidates for downstream logic to validate.