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.
git clone --depth 1 https://github.com/MantisWare/peepitWrote 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/rules/mantisware/peepit/agent)<a href="https://agentmods.dev/rules/mantisware/peepit/agent"><img src="https://agentmods.dev/badge/rules/mantisware/peepit/agent/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/rules/mantisware/peepit/agent"><img src="https://agentmods.dev/badge/rules/mantisware/peepit/agent.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 | $0.00000 | $0.01357 |
| Opus 5.5 | $0.00000 | $0.00543 |
| Sonnet 5.5 | $0.00000 | $0.00271 |
| Haiku 4.5 | $0.00000 | $0.00136 |
Grade A, and why
agent 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 1mo 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.
This is a copy
100% identical to agent — 108 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Instructions
This file provides guidance to AI assistants when working with code in this repository.
Project Overview
This is the peepit project, which provides a Model Context Protocol (MCP) server that enables executing AppleScript and JavaScript for Automation (JXA) scripts on macOS. The server features a knowledge base of pre-defined scripts accessible by ID and supports inline scripts, script files, and argument passing.
Architecture
- Server Configuration: The server reads configuration from environment variables like
LOG_LEVELandKB_PARSING. - MCP Tools: Two main tools are provided:
execute_script: Executes AppleScript/JXA from inline content, file path, or knowledge base IDget_scripting_tips: Retrieves information from the knowledge base
- Knowledge Base: A collection of pre-defined scripts stored as Markdown files in
knowledge_base/directory with YAML frontmatter - ScriptExecutor: Core component that executes scripts via
osascriptcommand
Knowledge Base System
The knowledge base (knowledge_base/ directory) contains numerous Markdown files organized by category:
- Each file has YAML frontmatter with metadata:
id,title,description,language, etc. - The actual script code is contained in the Markdown body in a fenced code block
- Scripts can use placeholders like
--MCP_INPUT:keyNameand--MCP_ARG_Nfor parameter substitution
Common Development Commands
# Install dependencies
npm install
# Run the server in development mode with hot reloading
npm run dev
# Build the TypeScript project
npm run build
# Start the compiled server
npm run start
# Lint the codebase
npm run lint
# Format the codebase
npm run format
# Validate the knowledge base
npm run validate
Environment Variables
LOG_LEVEL: Set logging level (DEBUG,INFO,WARN,ERROR) - default isINFOKB_PARSING: Controls when knowledge base is parsed:lazy(default): Parsed on first requesteager: Parsed when server starts
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.
- 1mo ago First seen · 108 lines · 0 tokens per session scan A 4883fc6d5157
agent is a cursor rule published in the GitHub repository MantisWare/peepit (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,357 tokens. A static security scan graded it A with 0 findings. It is 100% identical to agent, differing in 108 lines, and is treated as a copy.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).