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.
curl -O https://raw.githubusercontent.com/honeyvig/ai-vision-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/honeyvig/ai-vision-mcpWrote 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/instructions/honeyvig/ai-vision-mcp/agents-md)<a href="https://agentmods.dev/instructions/honeyvig/ai-vision-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/honeyvig/ai-vision-mcp/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.
<a href="https://agentmods.dev/instructions/honeyvig/ai-vision-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/honeyvig/ai-vision-mcp/agents-md.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 | $0.02957 | $0.02957 |
| Opus 5 | $0.01478 | $0.01478 |
| Sonnet 5 | $0.00591 | $0.00591 |
| Haiku 4.5 | $0.00296 | $0.00296 |
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.
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:
- ALWAYS create a plan document in the
docs/llm_logs/folder before writing any code - ALWAYS update README.md when introducing changes that affect:
- New MCP tools or parameters
- Environment variables
- Configuration options
- Installation instructions
- Breaking changes
- 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...
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.
- 8d ago First seen · 288 lines · 2,957 tokens per session scan A 01c99d2cb155
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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