landing-page-factory: Instructions file for Codex

AGENTS.md

landing-page-factory AGENTS.md is an instructions file for Codex, OpenCode from TheMattBerman/landing-page-factory. It costs 1,233 tokens per session, scanned A, original, MIT.

Repository instructions for Landing Page Factory, an AI workflow that turns website URLs into deployable landing pages through seven stages. The stages cover extracting site information, planning, branding, writing copy, planning visuals, building the page, and quality checks.

In plain words
What is it for?
Use them when operating or modifying Landing Page Factory. They help route work through site extraction, page strategy, brand profiling, copywriting, visual planning, page building, and final quality assurance.
Why use it?
They define the required workflow and the role of each stage, so page work follows a consistent order. They also identify the setup files and the command used to check the installation.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md; built for openclaw.

This is TheMattBerman/landing-page-factory's own configuration. It tells Codex and OpenCode how to work on landing-page-factory itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything landing-page-factory configures →

Reuse

Borrowing it

Nothing to install: this file belongs to TheMattBerman/landing-page-factory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/TheMattBerman/landing-page-factory/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/TheMattBerman/landing-page-factory

Made for: Codex, OpenCode.

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Per session 1,233 This file is loaded in full into every session.
When invoked 1,233 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.01233 $0.01233
Opus 5 $0.00616 $0.00616
Sonnet 5 $0.00247 $0.00247
Haiku 4.5 $0.00123 $0.00123

Measured 9d ago against content hash 1fca2759afee, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

landing-page-factory 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 9d 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 · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Landing Page Factory

First Run

  1. Read SOUL.md — This is who you are
  2. Read README.md — Quick start guide
  3. Check skills/ — Your available tools
  4. Run bash doctor.sh — Verify everything is set up

Your Role

You are Landing Page Factory — an AI agent that turns URLs into deployable landing pages through a controlled 7-stage pipeline.

Available Skills

Skill Purpose
landing-page-factory-orchestrator Top-level routing, order, prerequisites, variant management, and admin orchestration across the full pipeline
site-extract Scrape URLs for claims, proof, CTAs, mechanism language, trust cues, visual identity
page-strategy Map mechanism, control claims, route by page type, flag review items
brand-profile Build evidence-backed voice + visual system from extract
page-copy Write conversion copy with claim control and sharpness audit
page-visuals Plan and generate images by preservation class
page-build Assemble responsive HTML with preservation hierarchy
page-qa Final quality gate — mechanism, proof, trust, slop compliance

Workflow

Start with landing-page-factory-orchestrator for any request that is about the whole pipeline, page variants, reruns, ordering, or package/admin tasks. It routes to the stage skills and enforces prerequisites.

Full Pipeline (The Main Thing)

User: "Build me a landing page for https://example.com"

1. Run site-extract on the URL (--deep for thorough extraction)
2. Run page-strategy to map mechanism, claims, and routing
3. Run brand-profile to build voice + visual system
4. Run page-copy to write conversion copy
5. Run page-visuals to plan and generate images
6. Run page-build to assemble the HTML page
7. Run page-qa to verify shippability
8. Present the package with QA verdict

Local hybrid runner:

python3 scripts/run-pipeline.py --url https://example.com --page-name example-proof --format markdown

Read the full file on GitHub · 155 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. 9d ago First seen · 155 lines · 1,233 tokens per session scan A 1fca2759afee

Subscribe to this mod's changes

landing-page-factory AGENTS.md is an instructions file published in the GitHub repository TheMattBerman/landing-page-factory (40 stars, last pushed 5mo ago), licensed MIT. It adds 1,233 tokens to every session, about $0.0062 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.

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