lianhuanhua-skills: Instructions file for Codex

AGENTS.md

lianhuanhua-skills AGENTS.md is an instructions file for Codex, OpenCode from littlewindy123/lianhuanhua-skills. It costs 249 tokens per session, scanned A, original, MIT.

Repository instructions for a Codex plugin that turns stories, audio, or video plus character references into consistent comic-style vertical videos. They define where creative decisions belong and how Python, FFmpeg, JSON, and generated media should be handled.

In plain words
What is it for?
Use them when developing or maintaining the plugin, running its tests and diagnostic command, validating JSON and Python files, or checking generated video output.
Why use it?
They keep creative choices, automated processing, data formats, and privacy rules consistent across changes. They also require checking that the final video is valid before reporting success.

Instructions file for CodexOpenCode

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

This is littlewindy123/lianhuanhua-skills's own configuration. It tells Codex and OpenCode how to work on lianhuanhua-skills 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 lianhuanhua-skills configures →

Reuse

Borrowing it

Nothing to install: this file belongs to littlewindy123/lianhuanhua-skills. 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/littlewindy123/lianhuanhua-skills/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/littlewindy123/lianhuanhua-skills

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for lianhuanhua-skills AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/littlewindy123/lianhuanhua-skills/agents-md/github.svg)](https://agentmods.dev/instructions/littlewindy123/lianhuanhua-skills/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/littlewindy123/lianhuanhua-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/littlewindy123/lianhuanhua-skills/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.

agentmods 80×15 button for lianhuanhua-skills AGENTS.md

Your own site · 80×15
<a href="https://agentmods.dev/instructions/littlewindy123/lianhuanhua-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/littlewindy123/lianhuanhua-skills/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 249 This file is loaded in full into every session.
When invoked 249 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 $0.00249 $0.00249
Opus 5 $0.00125 $0.00125
Sonnet 5 $0.00050 $0.00050
Haiku 4.5 $0.00025 $0.00025

Measured 12d ago against content hash a5942143f863, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

lianhuanhua-skills 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 12d 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 · 32 lines

What it actually says

AGENTS.md

Project goal

Build and maintain a Codex-only plugin that turns a story, audio file, or video plus character references into a consistent comic-style vertical video.

Architecture rules

  • Creative decisions belong in SKILL.md and references/.
  • Deterministic behavior belongs in Python and FFmpeg.
  • JSON files are contracts. Update schemas and templates together.
  • Never log API keys, tokens, or private user media contents unnecessarily.
  • Preserve raw Doubao event logs without secrets for protocol debugging.
  • Keep image generation sequential; do not optimize it into uncontrolled parallel batches.
  • Do not report successful delivery unless ffprobe validates the final output.

Commands

SKILL=plugins/lianhuanhua/skills/lianhuanhua
PYTHONPATH="$SKILL/scripts" pytest -q "$SKILL/tests"
python "$SKILL/scripts/lianhuanhua_cli.py" doctor

Before committing

  1. Compile all Python files.
  2. Validate every JSON file.
  3. Run tests.
  4. Run the synthetic FFmpeg smoke test described in CODEX_TODO.md.
  5. Check that repository files contain no credentials or generated user media.
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. 12d ago First seen · 32 lines · 249 tokens per session scan A a5942143f863

Subscribe to this mod's changes

lianhuanhua-skills AGENTS.md is an instructions file published in the GitHub repository littlewindy123/lianhuanhua-skills (8 stars, last pushed 2mo ago), licensed MIT. It adds 249 tokens to every session, about $0.0012 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.

Related

Other instructions, from other repositories

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.

github/spec-kit · 7,126 tokens

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.

vercel/next.js · 7,296 tokens

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.

openai/codex · 5,153 tokens

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).

microsoft/vscode · 6,785 tokens

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.

langchain-ai/langchain · 4,469 tokens

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).

microsoft/vscode · 5,001 tokens