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Autonomous Testing

Community
alinaqi
autonomous-testing

AI-driven testing agent that auto-discovers, generates, executes, evaluates, and fixes tests for any project type

Overview

Publisheralinaqi
Repositorymaggy
Skill nameautonomous-testing
Stars
707
Forks
57
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by alinaqi on GitHub. Read the source before you install it.

Installation

Install the Autonomous Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/alinaqi/maggy/tree/main/skills/autonomous-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/alinaqi/maggy.git /tmp/maggy
mkdir -p .claude/skills
cp -r /tmp/maggy/skills/autonomous-testing .claude/skills/autonomous-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autonomous Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Autonomous Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Autonomous Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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/*"]
}

Frequently asked questions

What does the Autonomous Testing AI skill do?

AI-driven testing agent that auto-discovers, generates, executes, evaluates, and fixes tests for any project type

Why use Autonomous Testing on TypingMind?

Because you install it once and use it with any model. Autonomous Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Autonomous Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alinaqi/maggy/tree/main/skills/autonomous-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Autonomous Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Autonomous Testing?

As many as you like. As long as a model supports skills, you can use Autonomous Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Autonomous Testing AI skill free?

Yes. It is published on GitHub by alinaqi under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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