agentic-bench AGENTS.md

agentic-bench AGENTS.md is an instructions file for Codex, OpenCode from nyosegawa/agentic-bench. It costs 303 tokens per session, scanned A, original, MIT.

Repository instructions for developing an AI-agent benchmarking project with provider adapters, tests, research records, and documentation.

In plain words
What is it for?
They guide changes to configuration parsing, result aggregation, provider interfaces, external integrations, research notes, architecture documents, and tests.
Why use it?
They define a repeatable change process so core logic stays tested and integrations remain separated from it.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/nyosegawa/agentic-bench/agents-md
Clone the repo
git clone --depth 1 https://github.com/nyosegawa/agentic-bench

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for agentic-bench AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/nyosegawa/agentic-bench/agents-md.svg)](https://agentmods.dev/instructions/nyosegawa/agentic-bench/agents-md)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 303 This file is loaded in full into every session.
When invoked 303 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00303 $0.00303
Opus 5 $0.00151 $0.00151
Sonnet 5 $0.00061 $0.00061
Haiku 4.5 $0.00030 $0.00030

Measured 4d ago against content hash 40b964f26c91, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

AGENTS.md · 27 lines

What it actually says

AGENTS.md

Absolute Rules

  1. Instruction budget rule: keep this AGENTS.md in 20-30 lines.
  2. No backward compatibility: never preserve legacy behavior/interfaces.
  3. Commit and push regularly in small, reviewable increments.
  4. Refactor periodically to reduce complexity and technical debt.
  5. Always use the latest versions for libraries/dependencies, and research their usage thoroughly before introducing them.

Development Policy

  1. Write tests for core logic (config parsing, result aggregation, adapter interfaces).
  2. External integrations (GPU cloud APIs, Chrome MCP) are tested via manual verification.
  3. Keep provider adapters behind clean interfaces so core logic remains testable.
  4. Never merge code that breaks existing tests.
  5. Build skills following references/skill-bestpractice.md (gitignored, local only).

Knowledge Management

  1. Store investigation findings in research/ (pure research data only, no tasks/TODOs).
  2. Keep docs/architecture.md and README.md in sync with implementation changes.
  3. When adding providers, model types, or scripts, update all relevant docs in the same commit.

Required Flow Per Change

  1. Implement the change.
  2. Add or update tests for any core logic touched.
  3. Run the verification loop below.
  4. Confirm all checks pass before committing.

Python Verification Loop

  1. pytest
  2. ruff check .
  3. ruff format --check .
Changes

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.

  1. 4d ago First seen · 27 lines · 303 tokens per session scan A 40b964f26c91

Subscribe to this mod's changes

agentic-bench AGENTS.md is an instructions file published in the GitHub repository nyosegawa/agentic-bench (5 stars, last pushed 6mo ago), licensed MIT. It adds 303 tokens to every session, about $0.0015 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 2026-08-31.

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