testing-strategy-composer_skill

This skill crafts a comprehensive testing strategy across unit, integration, and e2e with scaffolding and gap analysis for balanced coverage.

Python

5

GitHub Stars

2

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 williamzujkowski/cognitive-toolworks --skill testing-strategy-composer

CHANGELOG.md2.0 KB
SKILL.md11.4 KB

Overview

This skill composes practical, ROI-focused testing strategies covering unit, integration, end-to-end, and performance testing. It recommends test distributions, highlights priority risk areas, and delivers phased execution plans and framework-specific scaffolding. The output is tailored to your architecture and tech stack to optimize coverage and effort.

How this skill works

The skill parses a system description and tech stack, identifies architecture and components, then applies test-pyramid heuristics to propose a balanced distribution of unit/integration/e2e tests. For extended analysis it maps technologies to testing frameworks, generates templates for unit/integration/e2e/performance tests, computes coverage gaps against targets, and produces a phased execution plan with effort estimates. It enforces pre-checks and abort conditions to avoid vague or incompatible inputs.

When to use it

Starting a new project that needs a comprehensive testing approach
Existing codebase with low or imbalanced test coverage
Remediating technical debt with a systematic testing plan
After architecture changes requiring reassessment of tests
Onboarding a team that needs clear testing guidelines

Best practices

Aim for a test pyramid balance and adjust by architecture (microservices, mobile, API-only)
Prioritize high-risk areas (auth, payments, data-sync) before broad coverage
Use arrange-act-assert and clear setup/teardown patterns in templates
Keep performance baselines for data/ML systems and schedule them in later phases
Validate that scaffolding contains no hardcoded credentials or PII

Example use cases

REST API (Node.js + PostgreSQL): recommend ~65/25/10 split, scaffolding with Jest/Supertest and integration templates
Microservices ecosystem: increase integration share (≈30%) and produce contract-test scaffolding
Mobile app with critical UI flows: increase E2E to ≈20% and supply Playwright/Appium templates
Data pipeline: add performance tests and baseline templates; adjust unit/integration ratios
Quick remediation: T1 fast path for constrained timelines (<10 hours) focusing on high-risk unit tests

FAQ

A concise system_description, a tech_stack list, and optional existing_coverage and constraints (budget, time, team_size).

How are effort estimates calculated?

Effort uses LOC-based heuristics (per 1000 LOC: unit 4–8h, integration 8–16h, e2e 16–24h, perf 20–40h) adjusted by complexity and risk.