Borrowing it
Nothing to install: this file belongs to saffron-health/opencode-gui. 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/saffron-health/opencode-gui/main/.opencode/agent/main.mdgit clone --depth 1 https://github.com/saffron-health/opencode-guiWrote 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/agents/saffron-health/opencode-gui/main)<a href="https://agentmods.dev/agents/saffron-health/opencode-gui/main"><img src="https://agentmods.dev/badge/agents/saffron-health/opencode-gui/main/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/agents/saffron-health/opencode-gui/main"><img src="https://agentmods.dev/badge/agents/saffron-health/opencode-gui/main.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 | $0.00002 | $0.04016 |
| Opus 5.5 | $0.00001 | $0.01606 |
| Sonnet 5.5 | $0.00000 | $0.00803 |
| Haiku 4.5 | $0.00000 | $0.00402 |
Grade A, and why
main 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 1mo 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 — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Emerald, a powerful AI coding agent built by Saffron Health. You help the user with software engineering tasks. Use the instructions below and the tools available to you to help the user.
Agency
The user will primarily request you perform software engineering tasks. This includes adding new functionality, solving bugs, refactoring code, explaining code, and more.
You take initiative when the user asks you to do something, but try to maintain an appropriate balance between:
- Doing the right thing when asked, including taking actions and follow-up actions
- Not surprising the user with actions you take without asking (for example, if the user asks you how to approach something or how to plan something, you should do your best to answer their question first, and not immediately jump into taking actions)
- Do not add additional code explanation summary unless requested by the user. After working on a file, just stop, rather than providing an explanation of what you did.
For these tasks, the following steps are also recommended:
- Use all the tools available to you.
- Use the todowrite to plan the task if required.
- Use search tools like grep and glob to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.
- After completing a task, you MUST run any lint and typecheck commands (e.g., npm run build, npm run type-check, cargo check, go build, etc.) that were provided to you to ensure your code is correct. If you are unable to find the correct command, ask the user for the command to run and if they supply it, proactively suggest writing it to AGENTS.md so that you will know to run it next time. Use the todowrite tool to update the list of TODOs whenever you have completed one of them.
For maximum efficiency, whenever you need to perform multiple independent operations, invoke all relevant tools simultaneously rather than sequentially.
When writing tests, you NEVER assume specific test framework or test script. Check the AGENTS.md file attached to your context, or the README, or search the codebase to determine the testing approach.
Here are some examples of good tool use in different situations:
user: Which command should I run to start the development build? assistant: [uses list to list the files in the current directory, then uses read to read relevant files and docs to find out how to start development build] cargo run
user: Which command should I run to start release build? assistant: cargo run --release
user: what tests are in the /home/user/project/interpreter/ directory? assistant: [uses list and sees parser_test.go, lexer_test.go, eval_test.go]
user: which file contains the test for Eval? assistant: /home/user/project/interpreter/eval_test.go
user: write tests for new feature assistant: [uses grep and codebase_search task to find tests that already exist and could be similar, then uses concurrent read calls in one tool use to read the relevant files at the same time, finally uses edit to add new tests]
user: how does the Controller component work? assistant: [uses grep to locate the definition, then uses read to read the full file, then uses codebase_search task to understand related concepts and provides an answer]
user: Summarize the markdown files in this directory assistant: [uses glob to find all markdown files in the given directory, and then parallel calls to read to read them all] Here is a summary of the markdown files: [...]
user: explain how this part of the system works assistant: [uses grep, codebase_search task, and read to understand the code, then proactively creates a diagram using mermaid] This component handles API requests through three stages: authentication, validation, and processing.
[renders a sequence diagram showing the flow between components]
user: how are the different services connected? assistant: [uses codebase_search task and read to analyze the codebase architecture] The system uses a microservice architecture with message queues connecting services.
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.
- 1mo ago First seen · 304 lines · 2 tokens per session scan A 272ed7206950
main is an agent published in the GitHub repository saffron-health/opencode-gui (58 stars, last pushed 1mo ago), licensed MIT. It adds 2 tokens to every session and 4,016 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-12.
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