Borrowing it
Nothing to install: this file belongs to nyosegawa/agentic-bench. 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/nyosegawa/agentic-bench/main/AGENTS.mdgit clone --depth 1 https://github.com/nyosegawa/agentic-benchWrote 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/nyosegawa/agentic-bench/agents-md)<a href="https://agentmods.dev/instructions/nyosegawa/agentic-bench/agents-md"><img src="https://agentmods.dev/badge/instructions/nyosegawa/agentic-bench/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/nyosegawa/agentic-bench/agents-md"><img src="https://agentmods.dev/badge/instructions/nyosegawa/agentic-bench/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 | $0.00303 | $0.00303 |
| Opus 5.5 | $0.00121 | $0.00121 |
| Sonnet 5.5 | $0.00061 | $0.00061 |
| Haiku 4.5 | $0.00030 | $0.00030 |
Grade A, and why
agentic-bench 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 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.
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.
What it actually says
AGENTS.md
Absolute Rules
- Instruction budget rule: keep this AGENTS.md in 20-30 lines.
- No backward compatibility: never preserve legacy behavior/interfaces.
- Commit and push regularly in small, reviewable increments.
- Refactor periodically to reduce complexity and technical debt.
- Always use the latest versions for libraries/dependencies, and research their usage thoroughly before introducing them.
Development Policy
- Write tests for core logic (config parsing, result aggregation, adapter interfaces).
- External integrations (GPU cloud APIs, Chrome MCP) are tested via manual verification.
- Keep provider adapters behind clean interfaces so core logic remains testable.
- Never merge code that breaks existing tests.
- Build skills following
references/skill-bestpractice.md(gitignored, local only).
Knowledge Management
- Store investigation findings in
research/(pure research data only, no tasks/TODOs). - Keep
docs/architecture.mdandREADME.mdin sync with implementation changes. - When adding providers, model types, or scripts, update all relevant docs in the same commit.
Required Flow Per Change
- Implement the change.
- Add or update tests for any core logic touched.
- Run the verification loop below.
- Confirm all checks pass before committing.
Python Verification Loop
pytestruff check .ruff format --check .
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 · 27 lines · 303 tokens per session scan A 40b964f26c91
agentic-bench AGENTS.md is an instructions file published in the GitHub repository nyosegawa/agentic-bench (5 stars, last pushed 8mo ago), licensed MIT. It adds 303 tokens to every session, about $0.0012 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 2026-08-31.
agentic-bench AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agentic-bench AGENTS.mdnyosegawa/agentic-bench | Instructions file | 5 | 8mo ago | MIT | A | 303 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |