Borrowing it
Nothing to install: this file belongs to harbor-framework/terminal-bench-science. 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-science/main/AGENTS.mdgit clone --depth 1 https://github.com/harbor-framework/terminal-bench-scienceWrote 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-science/agents-md)<a href="https://agentmods.dev/instructions/harbor-framework/terminal-bench-science/agents-md"><img src="https://agentmods.dev/badge/instructions/harbor-framework/terminal-bench-science/agents-md/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-science/agents-md"><img src="https://agentmods.dev/badge/instructions/harbor-framework/terminal-bench-science/agents-md.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 | $0.17366 | $0.17366 |
| Opus 5.5 | $0.06946 | $0.06946 |
| Sonnet 5.5 | $0.03473 | $0.03473 |
| Haiku 4.5 | $0.01737 | $0.01737 |
Grade A, and why
terminal-bench-science 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 yesterday.
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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Terminal-Bench-Science
Agent benchmark built with Harbor. Shared CI, review bot, and task structure come from the benchmark-template repo via a template git remote. Terminal-Bench-Science is a natural science benchmark targeting real scientific research workflows.
Repo Structure
.
├── CONTRIBUTING.md # Contributor guide (task creation, submission)
├── REVIEWING.md # Reviewer guide (review order, DRI model, labels)
├── TASK_REVIEW_AUTOMATION.md # CI pipeline docs (checks, commands, secrets)
├── TASK_IMPLEMENTATION_RUBRIC.toml # Symlink/alias — see rubrics/task-implementation.toml
├── README.md # Public-facing overview with benchmark progress table
├── task-template.toml # Template for `harbor tasks init`
├── task-hint-template.md # Template for a task's optional authoring/hint.md
├── task-readme-template.md # Authored sections of a task README (seeded by tools/task-readme)
├── tasks/ # Benchmark tasks (each has instruction.md, task.toml, etc.)
├── rubrics/
│ ├── task-implementation.toml # 39-criteria rubric for PR review (`harbor check`)
│ ├── task-proposal.md # LLM reviewer prompt for Discussions/Discord
│ ├── author-fit.md # LLM prompt for the separate author-aware pass (advisory author–task fit rating + COI flag)
│ └── trial-analysis.toml # 8-criteria trial-quality rubric for `harbor analyze` (per-trial, via -r)
├── ci_checks/
│ ├── check-*.sh # Static check scripts (canary, dockerfile, paths, etc.)
│ ├── check-*.py # Python checks (similarity, AI detection, task proposal link, rubric consistency)
│ ├── check-task-fields.sh # task.toml schema validation
│ ├── rubric_review.py # LLM proposal rubric review + author-fit pass (--author-fit / --author-fit-only)
│ └── test-tasks/ # Intentional-failure tasks for CI regression testing
├── .github/
│ ├── workflows/ # CI workflows (see TASK_REVIEW_AUTOMATION.md)
│ ├── llm-config.yml # Default agents/models for /run and /cheat (harbor_run.* keys)
│ ├── reviewer-pool.yml # Reviewer pool: reviewers_by_field (domain) + reviewers_technical + reviewers_final (domain & technical reviewed in parallel)
│ ├── hack-trial-prompt.md # Adversarial prompt for /cheat trials
│ ├── scripts/render_hint.py # Renders authoring/hint.md for /run hints
│ └── pull_request_template.md # PR template for task submissions
└── tools/
├── review-status/ # PR review status report generator
├── rubric-regression/ # Generates the fail-rubric-* review meta-task dataset (CI catch-rate gate)
├── batch-grader/ # Batch grading tool
└── task-readme/ # Renders a task README.md from its task.toml (deterministic; no LLM)
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.
- yesterday Changed · +1 lines · +633 tokens per session 23f92625d9de
- 5d ago Changed · +192 tokens per session 4567081915ea
- 6d ago Changed · +373 tokens per session e2b203a83443
- 8d ago Changed · -2 lines · +530 tokens per session d0fbaf20aa94
- 9d ago Changed · +1 lines · +471 tokens per session 32c54f6ac322
- 13d ago Changed · +31 tokens per session 14f67f66e912
- 15d ago Changed · +1 lines · +327 tokens per session 011b76d5a397
- 1mo ago Changed · +7 lines · +411 tokens per session 6567c468e0fc
- 1mo ago Changed · +15 lines · +1,063 tokens per session caaba0c982e4
- 1mo ago First seen · 246 lines · 13,335 tokens per session scan A 2a473752afeb
terminal-bench-science AGENTS.md is an instructions file published in the GitHub repository harbor-framework/terminal-bench-science (778 stars, last pushed yesterday), licensed Apache-2.0. It adds 17,366 tokens to every session, about $0.0695 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 2026-08-30.
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