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.
npx agentmods add instructions/opentechil/markitdown-for-ai/agents-mdgit clone --depth 1 https://github.com/OpenTechIL/markitdown-for-aiWrote 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/opentechil/markitdown-for-ai/agents-md)<a href="https://agentmods.dev/instructions/opentechil/markitdown-for-ai/agents-md"><img src="https://agentmods.dev/badge/instructions/opentechil/markitdown-for-ai/agents-md.svg" alt="Measured on agentmods" 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 | $0.00732 | $0.00732 |
| Opus 5 | $0.00366 | $0.00366 |
| Sonnet 5 | $0.00146 | $0.00146 |
| Haiku 4.5 | $0.00073 | $0.00073 |
Grade A, and why
markitdown-for-ai 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 3d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This document provides guidance for AI agents working on this project.
Project Overview
MarkItDown Docker image - A containerized version of Microsoft's markitdown library for converting documents (PDF, DOCX, PPTX, XLSX, HTML) to Markdown.
Conventional Commits
This project uses Conventional Commits format:
<type>(<scope>): <description>
[optional body]
[optional footer]
Types
| Type | Description |
|---|---|
feat |
New feature |
fix |
Bug fix |
docs |
Documentation only |
style |
Code style (formatting, no logic change) |
refactor |
Code refactoring |
test |
Adding/updating tests |
chore |
Maintenance, deps, build changes |
Examples
git commit -m "feat: add support for stdin input"
git commit -m "fix: resolve permission issue with non-root user"
git commit -m "docs: update README with new CLI examples"
Release Process
When committing changes, always:
-
Update CHANGELOG.md - Add entry under
[Unreleased]section:- Use appropriate type:
Added,Changed,Deprecated,Removed,Fixed,Security - Be descriptive but concise
- Use appropriate type:
-
Update version in CHANGELOG.md - When releasing:
- Move
[Unreleased]changes to new[X.Y.Z]section with date - Update version links at bottom
- Move
-
Update README.md - If changes affect:
- New features or commands
- New environment variables
- Build instructions
- Usage examples
-
Tag release - For new versions:
git tag -a v1.0.0 -m "Release v1.0.0" git push --tags
Docker Image Publishing
The GitHub Actions workflow (.github/workflows/docker.yml) automatically:
- Builds and pushes to GHCR on push to main
- Creates multi-platform images (amd64, arm64)
No manual Docker push needed - CI/CD handles it.
Skill Distribution
The document-to-markdown skill lives in skills/document-to-markdown/SKILL.md.
It is installed via:
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.
- 3d ago First seen · 96 lines · 732 tokens per session scan A 487ed1b62225
markitdown-for-ai AGENTS.md is an instructions file published in the GitHub repository OpenTechIL/markitdown-for-ai (3 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 732 tokens to every session, about $0.0037 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.
Other instructions, from other repositories
agentic-reviewer AGENTS.md
Instructions for FilippoScaramuzza/agentic-reviewer, covering agentic reviewer — journal paper review framework, project overview, directory structure, workflow and phase 0: setup & paper conversion.
markitdown-mcp AGENTS.md
Instructions for trsdn/markitdown-mcp, covering ai agents integration guide, 🤖 quick reference for ai agents, primary use cases, available tools and 🛠️ tool usage guide.
mktero AGENTS.md
Instructions for tenglvjun/mktero, covering agents.md, project overview, commands, repository map and architecture and runtime rules.
go-docs-mcp CLAUDE.md
Instructions for drolosoft/go-docs-mcp, covering go-docs-mcp, build & run, architecture, key decisions and tools.
docsift CLAUDE.md
Instructions for anishmoncivarghese/docsift, covering docsift, key documents (read before making product decisions), commands, architecture (src layout, package docsift) and hard rules.
vkr-builder CLAUDE.md
Instructions for maxbarsukov/vkr-builder, covering vkr-builder, как себя вести and основные команды.