verifiers AGENTS.md

A set of instructions for developing and running Verifiers tasksets, which are small programs used to test AI agents. It covers code structure, commands, configuration, documentation, and dependencies.

In plain words
What is it for?
It helps create tasksets and test harnesses, validate TOML configuration files, run evaluations with uv, and keep code and documentation aligned with the project.
Why use it?
It prevents common setup and testing mistakes, such as using the wrong command, changing shared dependencies, or overriding interfaces incorrectly.

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/primeintellect-ai/verifiers/agents-md
Clone the repo
git clone --depth 1 https://github.com/PrimeIntellect-ai/verifiers

Made for: Codex, OpenCode.

Per session 571 This file is loaded in full into every session.
When invoked 571 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 $0.00571 $0.00571
Opus 5 $0.00285 $0.00285
Sonnet 5 $0.00114 $0.00114
Haiku 4.5 $0.00057 $0.00057

Measured today against content hash 8d02cdfcd5a1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

verifiers 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 today.

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 · 29 lines

How it starts

The opening of the file, as written. The whole thing — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Writing code

  • Code is the source of truth: before writing anything, read the v1 code for existing helpers, harnesses, and interfaces instead of reinventing them. Prefer verifiers-native task, trace, server, harness, and runtime interfaces over repeated path/import/discovery plumbing in user packages.
  • Minimal config surface: expose as few knobs as possible, but as many as needed.
  • Keep tasksets small: a basic taskset fits in a few dozen idiomatic lines — typed data/task/config classes, load(), and decorated scoring on the task. Don't override Taskset.__init__ (implement load()); don't override Harness.__init__ (use setup()).

Running code

  • Always use uv: run code and commands with uv run, never raw python. Make sure uv is installed (docs).
  • Scaffold environments: create a new taskset/environment with uv run init <name> (uv run init -h lists options like -T/-H), and run evals with uv run eval <taskset>.
  • Validate TOML first: validate config .toml files before running them.
  • Don't add dependencies: never add dependencies or optional extras to the top-level pyproject.toml.

Docs

  • Kept intentionally minimal: docs/, skills/, and configs/ are deliberately sparse. Don't touch them unless your change breaks their assumptions.
  • Docs reflect main, not history: describe the current state of the codebase only — no removed/legacy fields, migration paths, or "this used to be X" anecdotes.

Skills

  • Use bundled skills first: the skills in skills/ cover the core workflows — create-environments (build or migrate a v1 taskset/environment/harness), evaluate-environments (configure and run evals), release (publish stable versions), and brainstorm (ideation and research planning). Reach for them before doing the work by hand.

Testing

  • Prefer e2e tests over unit tests: v1's end-to-end tests are sufficient — extra unit tests clog the repo. Editing existing tests is fine; to check your own work, write a temporary script instead of committing new tests.
  • Run the contributor checks: run uv run pre-commit install once, then for touched areas uv run ruff check --fix ., uv run pytest tests/, and uv run pre-commit run --all-files.

Read the full file on GitHub · 29 lines

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. today Changed · -11 tokens per session 8d02cdfcd5a1
  2. 3d ago First seen · 29 lines · 582 tokens per session scan A 54654d4ca04f

Subscribe to this mod's changes

verifiers AGENTS.md is an instructions file published in the GitHub repository PrimeIntellect-ai/verifiers (4,577 stars, last pushed today), licensed MIT. It adds 571 tokens to every session, about $0.0029 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.

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