Terminal-Bench is a benchmark and execution harness for testing whether AI agents can complete difficult, end-to-end tasks in real terminal environments. It is used by developers of language-model agents and benchmarking systems, with tasks covering activities such as compiling code, training models, and setting up servers. Catalogue add-ons help agents work with the benchmark and its task suite.
Borrowing it
Nothing to install: this file belongs to harbor-framework/terminal-bench-1. 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/harbor-framework/terminal-bench-1/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/harbor-framework/terminal-bench-1Wrote 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/harbor-framework/terminal-bench-1/copilot-instructions)<a href="https://agentmods.dev/instructions/harbor-framework/terminal-bench-1/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/harbor-framework/terminal-bench-1/copilot-instructions/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/harbor-framework/terminal-bench-1/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/harbor-framework/terminal-bench-1/copilot-instructions.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 | $0.00599 | $0.00599 |
| Opus 5.5 | $0.00240 | $0.00240 |
| Sonnet 5.5 | $0.00120 | $0.00120 |
| Haiku 4.5 | $0.00060 | $0.00060 |
Grade A, and why
terminal-bench-1 copilot-instructions.md scanned grade A 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
6. **Pinned Dependencies**: If the task uses external dependencies (e.g., Docker images, pip packages, etc.), verify their versions are pinned to ensure reproducibility. Don't require common apt packages like curl, vim, What it actually says
If the pull request implements a task for the Terminal-Bench benchmark (creates files in the tasks/** directory), then please do the following review of the task:
Please review this pull request and add a comment with a summary of the task instruction and how it implements the solution, and how it tests task completion. Be concise and to the point.
Use the following criteria to review the task:
- Behavior in Task Description: Check whether all behavior tested in the test cases is described in the task description.
- Behavior in Tests: Verify that all behavior described in the task description is checked in the unit tests.
- Informative Test Docstrings: Ensure test cases have informative docstrings that describe which behavior they check.
- Anti-Cheating Measures: Evaluate if it's hard for the agent to cheat on the task (e.g., by editing data files, looking inside files for strings that represent solutions, training on the test set, etc.). Note that the tests and solution are NOT visible to the agent. Don't worry about non-randomized, static tests, since the agent can't see the tests.
- Structured Data Schema: If the agent produces structured data (e.g., it is tasked with building an API), check whether the exact schema is described in the task.yaml or a separate file.
- Pinned Dependencies: If the task uses external dependencies (e.g., Docker images, pip packages, etc.), verify their versions are pinned to ensure reproducibility. Don't require common apt packages like curl, vim, etc. to be pinned. All python dependencies should be pinned.
- Typos: Check for any typos. Look very closely at file and variable names, because these can be hard to catch.
- Tests or Solution in Image: Ensure the tests/ folder or solution file is NOT copied to the image. They should not be. The /tests folder is automatically copied over by the harness after the agent runs.
- Test Dependencies in Image: Check that test dependencies are NOT installed in the image during the build process. They should be installed in the run-tests.sh script instead.
- Hardcoded Solution: Verify the solution doesn't directly output or hard-code the final answer using commands like echo/cat without performing the steps an agent would need to compute it. Using echo/cat to write source files or scripts that are then executed is acceptable. This check should PASS when the solution demonstrates a sequence of commands (e.g., data processing, running code) that derives the answer, and FAIL when the solution simply prints or writes the final answer without computation.
Format your response as a nice list.
Finally, a Terminal-Bench task should be realistic, interesting, and non-adversarial. Please comment if that is not the case.
Use the repository's CLAUDE.md for guidance on style and conventions. Be constructive and helpful in your feedback.
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.
- 2mo ago First seen · 23 lines · 599 tokens per session scan A a9e738a90e8c
terminal-bench-1 copilot-instructions.md is an instructions file published in the GitHub repository harbor-framework/terminal-bench-1 (2,612 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 599 tokens to every session, about $0.0024 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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