autoagent-action AGENTS.md

Project instructions for AutoAgent, a GitHub Action that runs AI coding agents on repository pull requests. A pull request is a proposed code change that others review before it is merged.

In plain words
What is it for?
Use them when modifying AutoAgent’s action definition, documentation, result handling, tests, or pull-request analysis features.
Why use it?
They give a coding agent the project layout, important files, supported agents, and expected checks. This reduces the risk of making changes that conflict with the action’s workflow.

Instructions file for CodexOpenCode

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 instructions/erans/autoagent-action/agents-md
Clone the repo
git clone --depth 1 https://github.com/erans/autoagent-action

Made for: Codex, OpenCode.

Per session 1,807 This file is loaded in full into every session.
When invoked 1,807 The same file — it is already loaded in full.
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 $0.01807 $0.01807
Opus 5 $0.00903 $0.00903
Sonnet 5 $0.00361 $0.00361
Haiku 4.5 $0.00181 $0.00181

Measured 2d ago against content hash 0a642911c49c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

autoagent-action 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 2d 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.

AGENTS.md · 174 lines

How it starts

The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AutoAgent Project Structure

AutoAgent is a composable GitHub Action that integrates with multiple AI agents (Cursor CLI, Claude Code, Gemini CLI, Codex CLI, Amp Code, and OpenCode) to run automated code analysis and prompts on repositories as part of Pull Request workflows.

Project Overview

AutoAgent provides a standardized way to run AI-driven code analysis in GitHub Actions, offering predefined security checks, code reviews, and refactoring suggestions, plus support for custom prompts. It automatically installs the required AI agent and posts structured results back to the PR as comments.

Directory Structure

Root Files

File Purpose
action.yml GitHub Action definition with inputs, outputs, and execution steps
README.md User documentation with usage examples and configuration options
results.json Temporary file storing analysis results in JSON format
test.sh Comprehensive test script that validates the entire implementation
test-simple.sh Lightweight test script for syntax validation and basic functionality

/docs/ - Documentation

File Purpose
prd.md Product Requirements Document defining features, inputs, and behavior
tech-prd.md Technical Implementation Guide with architecture and code structure details

/rules/ - AI Agent Prompts

Contains predefined prompt templates that guide the AI agent's analysis:

File Purpose
base.prompt Shared initialization context for all agents running in GitHub Actions
code-review.prompt Comprehensive code review focusing on quality, architecture, performance, and maintainability
owasp-check.prompt Security analysis based on OWASP Top 10 guidelines
refactor-suggestions.prompt Code smell detection and refactoring opportunity identification
comment.prompt Output formatting instructions for PR comments (concise, actionable, with compare links)

Read the full file on GitHub · 174 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. 2d ago First seen · 174 lines · 1,807 tokens per session scan A 0a642911c49c

Subscribe to this mod's changes

autoagent-action AGENTS.md is an instructions file published in the GitHub repository erans/autoagent-action (17 stars, last pushed 11mo ago), licensed MIT. It adds 1,807 tokens to every session, about $0.0090 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.