Borrowing it
Nothing to install: this file belongs to ksgisang/AI-Watch-Tester. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ksgisang/AI-Watch-Tester/main/AGENTS.mdgit clone --depth 1 https://github.com/ksgisang/AI-Watch-TesterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/ksgisang/ai-watch-tester/agents-md)<a href="https://agentmods.dev/instructions/ksgisang/ai-watch-tester/agents-md"><img src="https://agentmods.dev/badge/instructions/ksgisang/ai-watch-tester/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/ksgisang/ai-watch-tester/agents-md"><img src="https://agentmods.dev/badge/instructions/ksgisang/ai-watch-tester/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00483 | $0.00483 |
| Opus 5 | $0.00242 | $0.00242 |
| Sonnet 5 | $0.00097 | $0.00097 |
| Haiku 4.5 | $0.00048 | $0.00048 |
Grade A, and why
AI-Watch-Tester AGENTS.md 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 10d ago.
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.
What it actually says
AWT (AI Watch Tester) — AI Agent Rules
This file is for AI coding assistants (Gemini, Copilot, Codex, Cursor, Claude Code, etc.). Follow these rules when the user asks you to use AWT for testing.
⛔ MANDATORY WORKFLOW — 4 Steps, No Exceptions
When the user asks you to test a web application with AWT, you MUST follow these 4 steps in order. Do NOT skip, combine, or automate any step.
Step 1: SCAN
aat scan --url <URL>
Read .aat/scan_result.json and present a summary to the user:
- How many elements were found (inputs, buttons, links)
- Ask: "Should I create a test scenario based on these elements?"
- WAIT for user response.
Step 2: GENERATE + PRESENT
Write a YAML scenario using data from scan_result.json.
Show the full scenario to the user in a readable format.
Ask: "Should I run this test? Or do you want me to change anything?"
- WAIT for user approval.
- If user requests changes → modify and show again.
Step 3: EXECUTE (only after user says "go ahead")
aat run --skill-mode --fast <scenario_file>
If a step fails → STOP immediately. Report:
- Which step failed and why
- What the possible cause is
- Ask: "Should I fix the scenario, or fix the source code?"
- WAIT for user instruction. Do NOT auto-fix.
Step 4: REPORT
When all steps pass, summarize the results to the user.
⛔ BANNED — Never Do These
| Banned | Why |
|---|---|
aat devqa |
Runs entire pipeline without user checkpoints |
-y / --auto-approve |
Bypasses user approval gate |
| Auto-fixing code without asking | User must approve all changes |
| Auto-retrying failed tests | User must decide what to fix first |
headless: true |
User must see the browser |
| Guessing element names | Always scan first, use real data |
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.
- 10d ago First seen · 49 lines · 483 tokens per session scan A 268d2bdab94a
AI-Watch-Tester AGENTS.md is an instructions file published in the GitHub repository ksgisang/AI-Watch-Tester (7 stars, last pushed 4mo ago), licensed MIT. It adds 483 tokens to every session, about $0.0024 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 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.