ai-vision-mcp: Instructions file for Codex

AGENTS.md

ai-vision-mcp AGENTS.md is an instructions file for Codex, OpenCode from honeyvig/ai-vision-mcp. It costs 2,957 tokens per session, scanned A, original, MIT.

A set of instructions for AI coding work in the ai-vision-mcp repository, including planning and documentation requirements before code changes.

In plain words
What is it for?
It helps prepare implementation plans, maintain the README and specification when needed, use additional information sources while fixing or building, and record technical decisions.
Why use it?
It reduces the risk of undocumented architecture, configuration, or API changes by requiring plans and updates to relevant project documents.

Instructions file for CodexOpenCode

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

This is honeyvig/ai-vision-mcp's own configuration. It tells Codex and OpenCode how to work on ai-vision-mcp 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 ai-vision-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to honeyvig/ai-vision-mcp. 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/honeyvig/ai-vision-mcp/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/honeyvig/ai-vision-mcp

Made for: Codex, OpenCode.

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README.md
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Per session 2,957 This file is loaded in full into every session.
When invoked 2,957 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 $0.02957 $0.02957
Opus 5 $0.01478 $0.01478
Sonnet 5 $0.00591 $0.00591
Haiku 4.5 $0.00296 $0.00296

Measured 8d ago against content hash 01c99d2cb155, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

ai-vision-mcp 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 8d 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 · 288 lines

How it starts

The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. Please always use context7 MCP, web search, or web fetch for additional information when fixing bugs or implementing new features.

CRITICAL: Documentation Maintenance Requirements

BEFORE starting any coding work:

  1. ALWAYS create a plan document in the docs/llm_logs/ folder before writing any code
  2. ALWAYS update README.md when introducing changes that affect:
    • New MCP tools or parameters
    • Environment variables
    • Configuration options
    • Installation instructions
    • Breaking changes
  3. ALWAYS update docs/SPEC.md when introducing changes that affect:
    • Architecture modifications
    • New provider implementations
    • API interface changes
    • File handling logic
    • Error handling patterns

Planning Process:

  • Create plan documents in docs/llm_logs/ folder (e.g., docs/llm_logs/feature-name-plan.md)
  • Include architecture decisions, implementation steps, and testing strategy
  • Reference this plan in your commit messages
  • Keep plan documents as documentation of implementation decisions

Solution Planning Best Practices:

  • ALWAYS present at least 3 options when planning solutions to problems
  • Analyze trade-offs: effort vs. benefit, maintainability vs. speed, risk vs. reward
  • Provide clear recommendations with rationale (e.g., "Option 2 recommended because...")
  • Consider: quick fixes, balanced approaches, and comprehensive solutions
  • Include effort estimates, risk assessments, and rollback strategies for each option
  • Use structured format: Option 1 (Simple), Option 2 (Balanced), Option 3 (Comprehensive)

Example Planning Structure:

## Plan: [Problem Description]

### Option 1: Quick Fix (15 min)
- ✅ Minimal change, fastest implementation
- ❌ Technical debt, not future-proof
- **When to use**: Urgent hotfixes, time pressure

### Option 2: Balanced Solution (45 min) - RECOMMENDED
- ✅ Good maintainability, moderate effort
- ✅ Addresses root cause, extensible
- ❌ Longer implementation time
- **When to use**: Most production scenarios

### Option 3: Comprehensive Refactor (2 hours)
- ✅ Perfect architecture, future-proof
- ❌ High effort, potential for new bugs
- **When to use**: Major feature additions, architectural improvements

### Recommendation: Option 2
**Rationale**: Balances immediate needs with long-term maintainability...

Read the full file on GitHub · 288 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. 8d ago First seen · 288 lines · 2,957 tokens per session scan A 01c99d2cb155

Subscribe to this mod's changes

ai-vision-mcp AGENTS.md is an instructions file published in the GitHub repository honeyvig/ai-vision-mcp (0 stars, last pushed 9mo ago), licensed MIT. It adds 2,957 tokens to every session, about $0.0148 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.

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