> Guide red-green-refactor TDD with test generation, coverage-gap analysis, and multi- framework support. Use when writing tests first, analyzing coverage, generating test stubs, or converting tests between Jest, Pytest, JUnit, and Vitest.
npx skills add https://github.com/borghei/Claude-Skills --skill tdd-guide
The agent guides red-green-refactor TDD workflows, generates framework-specific test stubs from requirements, parses coverage reports to identify prioritized gaps, and calculates test quality metrics including smell detection and assertion density. Supports Jest, Pytest, JUnit, Vitest, and Mocha.
Before generating tests or analyzing coverage, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
# Generate test cases from requirements (Python API)
from test_generator import TestGenerator, TestFramework
gen = TestGenerator(framework=TestFramework.PYTEST, language="python")
cases = gen.generate_from_requirements(requirements)
# Analyze coverage gaps from LCOV report
from coverage_analyzer import CoverageAnalyzer
analyzer = CoverageAnalyzer()
analyzer.parse_coverage_report(content, "lcov")
gaps = analyzer.identify_gaps(threshold=80.0)
# Guide TDD cycle
from tdd_workflow import TDDWorkflow
wf = TDDWorkflow()
wf.start_cycle("User can reset password via email")
Load the reference that matches the task — keep this file lean and pull detail on demand:
This skill covers:
This skill does NOT cover:
senior-qa for E2E patterns and senior-devops for load testingsenior-security and senior-secops skills| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| senior-qa | Generated test stubs feed into QA review workflows; QA coverage standards inform threshold settings | test_generator.py output → QA review → approved test suite |
| code-reviewer | Metrics calculator output provides quantitative data for code review checklists | metrics_calculator.py quality report → code review scoring |
| senior-fullstack | Scaffolded projects include test infrastructure; TDD guide generates tests for scaffolded modules | project_scaffolder.py output → test_generator.py input |
| senior-devops | Coverage reports from CI pipelines are parsed by coverage analyzer; recommendations feed back into pipeline gates | CI coverage artifact → coverage_analyzer.py → pass/fail gate |
| senior-security | Edge-case fixtures for auth and API scenarios complement security-focused test plans | fixture_generator.py auth/API edge cases → security test plan |
| tech-stack-evaluator | Framework detection informs stack evaluation; test quality metrics feed into technology assessment | format_detector.py analysis → stack evaluation input |
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take borghei/tdd-guide from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.