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/antiv/mate/agents-mdgit clone --depth 1 https://github.com/antiv/mateWhat 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 | $0.01440 | $0.01440 |
| Opus 5 | $0.00720 | $0.00720 |
| Sonnet 5 | $0.00288 | $0.00288 |
| Haiku 4.5 | $0.00144 | $0.00144 |
Grade A, and why
mate 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 2d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MATE (Multi-Agent Tree Engine) System Instructions
Code Style
- Use Python 3.8+ for all new files
- Follow PEP 8 style guidelines with 4-space indentation
- Use type hints for function parameters and return types
- Prefer descriptive variable names over comments
- Use f-strings for string formatting
- Import statements should be grouped: stdlib, third-party, local imports
Development Environment
- ALWAYS activate the virtual environment before running any Python commands
- Use
source .venv/bin/activatebefore running Python scripts, tests, or imports - This ensures correct package versions and dependencies are used
- All Python commands should be run within the activated virtual environment
Architecture
- Follow the Agent-Manager-Tool Factory pattern
- Keep database models in
shared/utils/models.py - Keep agent business logic separate from database operations
- Use the AgentManager for all agent initialization and hierarchy management
- Tools should be created through the ToolFactory system
- All agents must include token usage tracking callbacks
Server Architecture
- Auth Server: Always runs on port 8000 (
auth_server.py) - ADK Server: Always runs on port 8001 (
adk_main.py) - All requests go through auth server - no direct access to ADK server
- Auth server acts as a proxy to ADK server for all API requests
- Authentication is required for all requests (admin:mate by default)
- A2A (Agent-to-Agent) protocol requests are proxied through auth server
Database
- Use SQLAlchemy ORM for all database operations
- Support PostgreSQL (production), MySQL, and SQLite (development)
- Database sessions should be properly closed in finally blocks
- Use the DatabaseClient for connection management
- Agent configurations are stored in
agents_configtable - Token usage is logged in
token_usage_logstable - Create migrations for each DB changes. Migrations are in
shared/sql/migrations/. Each DB type have separated folder: postgresql, sqlite, mysql - Database updates should go through the migration system, not through a separate script.
- Do not run any DB update. Prepare migrations. Migrations will be run on server run.
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.
- 2d ago First seen · 125 lines · 1,440 tokens per session scan A fae037564731
mate AGENTS.md is an instructions file published in the GitHub repository antiv/mate (88 stars, last pushed 3d ago), licensed Apache-2.0. It adds 1,440 tokens to every session, about $0.0072 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.
Other instructions, from other repositories
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Copilot instructions for mckinsey/vizro, covering github copilot instructions for vizro, pull requests from bots and automated tooling and everything else.
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awesome-ai-gateway CLAUDE.md
Instructions for cuihuan/awesome-ai-gateway, covering claude.md — working rules for this repository, what this repository is, the two goals — judge every change against these, non-negotiable rules and before you commit.
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Instructions for heggria/taskflow, covering agents.md, project overview, architecture, key concepts and phase types (12 total).
AGI-Super-Team AGENTS.md
Instructions for aAAaqwq/AGI-Super-Team, a project described as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters.
zenflow AGENTS.md
Instructions for zendev-sh/zenflow, covering agents.md - zenflow, commands, architecture, key rules and coordinator and messaging.