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/omnigent-ai/omnigent/agents-mdgit clone --depth 1 https://github.com/omnigent-ai/omnigentWrote 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/omnigent-ai/omnigent/agents-md)<a href="https://agentmods.dev/instructions/omnigent-ai/omnigent/agents-md"><img src="https://agentmods.dev/badge/instructions/omnigent-ai/omnigent/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 | $0.00979 | $0.00979 |
| Opus 5 | $0.00490 | $0.00490 |
| Sonnet 5 | $0.00196 | $0.00196 |
| Haiku 4.5 | $0.00098 | $0.00098 |
Grade B, and why
omnigent AGENTS.md scanned grade B with 1 finding 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 4d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
When you finish a task, print instructions to the user on how to test it: the How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent guidance
Guidance for AI agents (Claude Code, Copilot, Cursor, etc.) working in this
repository. See CONTRIBUTING.md for the full contributor workflow.
Committing
Run the pre-commit hook before committing (pre-commit run --all-files, or
let it run on staged files via git commit). Fix any issues it reports so the
commit lands clean — CI runs the same checks.
Local development shortcuts
Use just for common tasks; run just --list for grouped recipes.
just ensure— install/check prerequisitesjust run-ios/just run-android— build/run mobile appsjust dev/just dev-mobile— start the omnigent dev podjust electron-dev/just electron-build— Electron desktop shelljust lint/just lint-all— run pre-commitjust normalize-locks— rewrite lockfile registries to PyPI/npmjs.org
Pull requests
When you open a pull request, fill in the repo's PR template at
.github/pull_request_template.md (case-sensitive on Linux — note the lowercase
filename). Keep every section and checkbox row so reviewers can skim them.
- Summary — what changed and why.
- Test Plan — how you verified it.
- Demo — a video or images showing the change. Expected on contributor
PRs for UI / frontend changes (check the "UI / frontend change" box under
Type of change) so reviewers can see the new behaviour without checking out
the branch. Use
N/Afor non-visual changes. - Type of change / Test coverage — check all that apply (at least one each).
- Coverage notes — required if you checked "Manual verification completed" or "Not applicable".
Generate the description from the actual diff and this session's context — lead
with the motivation, then the change. Don't pass a --body that skips these
sections.
Finishing a task
When you finish a task, print instructions to the user on how to test it: the commands to run, the inputs to provide, or the steps to reproduce so they can verify the result themselves. Prefer verification that is best performed by a human, such as concrete manual behavior checks, rather than only listing unit test commands. Don't leave the user guessing how to confirm the work — tell them exactly what to do.
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.
- 4d ago First seen · 94 lines · 979 tokens per session scan B 629f4b8a5685
omnigent AGENTS.md is an instructions file published in the GitHub repository omnigent-ai/omnigent (9,591 stars, last pushed yesterday), licensed Apache-2.0. It adds 979 tokens to every session, about $0.0049 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
autocontext CLAUDE.md
Instructions for greyhaven-ai/autocontext, covering claude.md, project overview, repository layout, commands and setup.
autocontext AGENTS.md
Instructions for greyhaven-ai/autocontext, covering agent guide, pick the right surface, working directories, setup and parity-last changes.
terminator CLAUDE.md
Instructions for mediar-ai/terminator, covering terminator - ai-native gui automation, installation, release, or with globally installed cli and development.
plexe AGENTS.md
Instructions for plexe-ai/plexe, covering agents.md, architecture, 6-phase workflow (plexe/workflow.py), multi-agent system (plexe/agents/) and tree search (plexe/search/).
plexe CLAUDE.md
Instructions for plexe-ai/plexe, a project described as: ✨ Build a machine learning model from a prompt.
langgraph AGENTS.md
AGENTS.md instructions for langchain-ai/langgraph, covering agents instructions, corridor security analysis, libraries and dependency map.