Agent Skillsalinaqi/maggy › autonomous-testing

autonomous-testing

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AI驱动的自动化测试智能体,自动发现代码测试缺口、生成并执行各类测试(单元/集成/E2E),评估失败原因并自主修复测试或代码错误,最终生成结构化报告。

skills/autonomous-testing/SKILL.md alinaqi/maggy

Trigger Scenarios

需要为项目自动生成全面覆盖的单元测试和集成测试 测试执行失败时希望AI自动诊断并修复测试脚本或代码缺陷 需要快速提升现有项目的代码覆盖率并验证回归质量

Install

npx skills add alinaqi/maggy --skill autonomous-testing -g -y
More Options

Use without installing

npx skills use alinaqi/maggy@autonomous-testing

指定 Agent (Claude Code)

npx skills add alinaqi/maggy --skill autonomous-testing -a claude-code -g -y

安装 repo 全部 skill

npx skills add alinaqi/maggy --all -g -y

预览 repo 内 skill

npx skills add alinaqi/maggy --list

SKILL.md

Frontmatter
{
    "name": "autonomous-testing",
    "effort": "high",
    "description": "AI-driven testing agent that auto-discovers, generates, executes, evaluates, and fixes tests for any project type",
    "when-to-use": "When setting up automated test generation or running an autonomous test-fix loop across Python, TypeScript, API, or web projects",
    "user-invocable": false
}

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

# 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

{
  "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

# 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

// ~/.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/*"]
}

Version History

  • 2a98228 Current 2026-09-09 08:47

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Metadata

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Version
2a98228
Hash
3c34371a
Indexed
2026-09-09 08:47

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