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/harbor-framework/terminal-bench-science/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.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 | $0.12599 | $0.12599 |
| Opus 5 | $0.06300 | $0.06300 |
| Sonnet 5 | $0.02520 | $0.02520 |
| Haiku 4.5 | $0.01260 | $0.01260 |
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 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.
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 — 244 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`
├── 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
│ └── 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.
- 4d ago First seen · 244 lines · 12,599 tokens per session scan A bddb647fbe2d
terminal-bench-science AGENTS.md is an instructions file published in the GitHub repository harbor-framework/terminal-bench-science (379 stars, last pushed 5d ago), licensed Apache-2.0. It adds 12,599 tokens to every session, about $0.0630 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
wisp-science AGENTS.md
AGENTS.md instructions for xuzhougeng/wisp-science, covering agents.md, project orientation, repository layout, engineering rules and verification commands.
MoleCode AGENTS.md
Instructions for AtomFlow-AI/MoleCode, covering agents.md — molecode, what this repo is, use the molecode skill, molecode-first rule and cli quick start.
open-science CLAUDE.md
Claude Code instructions for ai4s-research/open-science, a project described as: Open Science Desktop — local-first, model-agnostic AI research workbench for macOS, Windows & Linux. Open-source Claude Science desktop alternative built on Tauri + MCP + agent skills.
preprint-fulltext copilot-instructions.md
Copilot instructions for genecell/preprint-fulltext, covering preprint-fulltext, when to use, why full text (for grounded reasoning), cli and python.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.