---
name: Autonomous Testing
slug: autonomous-testing
category: Quality
description: Autonomous Testing generates, runs, evaluates, and fixes tests for a codebase by scanning for coverage gaps and using AI to classify failures. Use it for automated test creation, regression checks, and repair loops across Python, TypeScript, API, and web projects.
github: "https://github.com/alinaqi/maggy/tree/main/skills/autonomous-testing"
language: Python
stars: 704
forks: 57
install: "npx degit https://github.com/alinaqi/maggy/tree/main/skills/autonomous-testing ~/.claude/skills/autonomous-testing"
installs_to: ~/.claude/skills/autonomous-testing
source_path: skills/autonomous-testing/SKILL.md
collection_size: 25
category_size: 1354
collection_url: "https://dirskills.com/collections/alinaqi/maggy"
added: 2026-08-23T05:20:36.444Z
last_synced: 2026-08-23T05:20:36.444Z
canonical_url: "https://dirskills.com/skills/autonomous-testing"
---

# Autonomous Testing

Autonomous Testing generates, runs, evaluates, and fixes tests for a codebase by scanning for coverage gaps and using AI to classify failures. Use it for automated test creation, regression checks, and repair loops across Python, TypeScript, API, and web projects.

**Install:**

```bash
npx degit https://github.com/alinaqi/maggy/tree/main/skills/autonomous-testing ~/.claude/skills/autonomous-testing
```

## README

# Autonomous Testing Agent

## Overview

An AI-driven testing agent that auto-discovers, generates, executes, evaluates, and fixes tests for any project type. Inspired by the edubites autonomous test runner pattern, generalized for Claude Bootstrap + Maggy.

## Pipeline

```
Source Scan → Discover Gaps → Generate Tests → Execute → Evaluate → Report → Fix Loop
```

## Phase 1: Discover — What Needs Testing?

```
Auto-detect project type:
  Python    → scan for *.py files, extract public functions/classes
  TypeScript → scan for *.ts/*.tsx files, extract exports
  API       → scan FastAPI/Express routes, extract endpoints + methods
  Web       → scan React/Vue components, extract user flows

Map existing tests:
  Python    → pytest --collect-only
  TypeScript → vitest --list
  API       → scan tests/ for endpoint coverage

Compute coverage gaps:
  - Functions with 0 tests
  - API endpoints with 0 tests
  - Components with 0 tests
  - Branches with <80% coverage
```

## Phase 2: Generate — AI-Written Tests

```
For each uncovered function/endpoint/component:
  1. Read source code → understand inputs, outputs, edge cases
  2. Generate test scaffold using ~/bin/deepseek --pro
  3. Include: happy path, error cases, edge cases, auth checks
  4. Write to appropriate test directory

Model routing for generation:
  - Simple functions    → ~/bin/deepseek --flash (cheap, fast)
  - Complex logic       → ~/bin/deepseek --pro (thorough)
  - Auth/security tests → ~/bin/deepseek --pro (quality-critical)
```

## Phase 3: Execute — Run Everything

```bash
# Python
pytest -x --cov --cov-report=json

# TypeScript
npx vitest run --coverage

# E2E (if Playwright detected)
npx playwright test

# Parse results → structured TestRun { pass/fail, coverage, duration, failures[] }
```

## Phase 4: Evaluate — AI-Powered Assessment

```
For each test failure:
  1. Capture: test name, error message, stack trace, source code diff
  2. Classify failure:
     - TEST_BUG: test is wrong (outdated expectation, bad mock)
     - CODE_BUG: code is wrong (regression, edge case)
     - ENV_BUG: environment issue (missing dep, config)
  3. AI evaluation: ~/bin/deepseek --pro analyzes failure and classifies

For E2E/web tests:
  - Capture screenshots at failure points
  - ~/bin/gemini --flash evaluates visual state (multimodal)
```

## Phase 5: Fix — Autonomous Repair

```
TEST_BUG → regenerate test with corrected expectation
CODE_BUG → propose fix with ~/bin/deepseek --pro, apply, re-run
ENV_BUG → report to user with fix instructions

Auto-fix loop:
  while test_failures > 0 and attempts < 3:
    for each failure:
      classify → fix → re-run
    if fixed: record as "auto-fixed"
    if not: escalate to CLAUDE tier
```

## Phase 6: Report — Structured Output

```json
{
  "project": "my-app",
  "timestamp": "2026-05-16T12:00:00Z",
  "summary": {
    "tests_run": 247,
    "passed": 231,
    "failed": 12,
    "auto_fixed": 8,
    "needs_manual": 4,
    "coverage": 0.83
  },
  "gaps_found": 15,
  "tests_generated": 15,
  "next_actions": [
    "4 manual fixes needed in auth module",
    "Coverage gap: src/payment.py has 0 tests",
    "3 E2E flows untested: signup, checkout, profile-edit"
  ]
}
```

## Integration with Maggy

```
Maggy Dashboard → Testing tab shows:
  - Coverage trend over time
  - Auto-generated test count
  - Failure classification (TEST_BUG vs CODE_BUG)
  - "Generate tests for gaps" one-click button

Heartbeat job: auto-generate tests weekly for new untested code
Auto-review hook: triggers test generation after significant PR merges
```

## Usage

```bash
# Discover test gaps
maggy test discover

# Generate tests for all gaps
maggy test generate --all

# Generate tests for specific module
maggy test generate --module auth

# Run full test cycle (discover → generate → execute → fix → report)
maggy test autonomous

# Watch mode — auto-test on file changes
maggy test watch
```

## Configuration

```json
// ~/.claude/testing-config.json
{
  "auto_generate": true,
  "auto_fix": true,
  "max_fix_attempts": 3,
  "min_coverage": 0.8,
  "generate_model": "deepseek-pro",
  "evaluate_model": "gemini-flash",
  "fix_model": "deepseek-pro",
  "exclude_patterns": ["*/migrations/*", "*/node_modules/*"]
}
```