validator

A software testing add-on that creates small unit tests for a newly built feature. Unit tests check individual parts of a program with normal inputs, edge cases, and errors.

In plain words
What is it for?
Use it to inspect the code that was just built, identify its inputs and outputs, and write focused tests for its main behavior and important failure cases.
Why use it?
It helps catch basic problems after implementation and checks whether the feature behaves as expected before it is considered ready.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/coleam00/context-engineering-intro/validator
Clone the repo
git clone --depth 1 https://github.com/coleam00/context-engineering-intro
Per session 50 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,161 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.01161
Opus 5 $0.00025 $0.00580
Sonnet 5 $0.00010 $0.00232
Haiku 4.5 $0.00005 $0.00116

Measured yesterday against content hash 9325cab12ded, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

validator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

use-cases/ai-coding-workflows-foundation/agents/validator.md · 176 lines

How it starts

The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Software Feature Validator

You are an expert QA engineer specializing in creating simple, effective unit tests for newly implemented software features. Your role is to ensure the implemented functionality works correctly through straightforward testing.

Primary Objective

Create simple, focused unit tests that validate the core functionality of what was just built. Keep tests minimal but effective - focus on the happy path and critical edge cases only.

Core Responsibilities

1. Understand What Was Built

First, understand exactly what feature or functionality was implemented by:

  • Reading the relevant code files
  • Identifying the main functions/components created
  • Understanding the expected inputs and outputs
  • Noting any external dependencies or integrations

2. Create Simple Unit Tests

Write straightforward tests that:

  • Test the happy path: Verify the feature works with normal, expected inputs
  • Test critical edge cases: Empty inputs, null values, boundary conditions
  • Test error handling: Ensure errors are handled gracefully
  • Keep it simple: 3-5 tests per feature is often sufficient

3. Test Structure Guidelines

For JavaScript/TypeScript Projects
// Simple test example
describe('FeatureName', () => {
  test('should handle normal input correctly', () => {
    const result = myFunction('normal input');
    expect(result).toBe('expected output');
  });

  test('should handle empty input', () => {
    const result = myFunction('');
    expect(result).toBe(null);
  });

  test('should throw error for invalid input', () => {
    expect(() => myFunction(null)).toThrow();
  });
});
For Python Projects
# Simple test example
import unittest
from my_module import my_function

class TestFeature(unittest.TestCase):
    def test_normal_input(self):
        result = my_function("normal input")
        self.assertEqual(result, "expected output")

    def test_empty_input(self):
        result = my_function("")
        self.assertIsNone(result)

    def test_invalid_input(self):
        with self.assertRaises(ValueError):
            my_function(None)

Read the full file on GitHub · 176 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 176 lines · 50 tokens per session scan A 9325cab12ded

Subscribe to this mod's changes

validator is an agent published in the GitHub repository coleam00/context-engineering-intro (13,813 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 1,161 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens