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/primeintellect-ai/verifiers/agents-mdgit clone --depth 1 https://github.com/PrimeIntellect-ai/verifiersWhat 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 | $0.00571 | $0.00571 |
| Opus 5 | $0.00285 | $0.00285 |
| Sonnet 5 | $0.00114 | $0.00114 |
| Haiku 4.5 | $0.00057 | $0.00057 |
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.
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 overrideTaskset.__init__(implementload()); don't overrideHarness.__init__(usesetup()).
Running code
- Always use uv: run code and commands with
uv run, never rawpython. Make sureuvis installed (docs). - Scaffold environments: create a new taskset/environment with
uv run init <name>(uv run init -hlists options like-T/-H), and run evals withuv run eval <taskset>. - Validate TOML first: validate config
.tomlfiles 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/, andconfigs/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), andbrainstorm(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 installonce, then for touched areasuv run ruff check --fix .,uv run pytest tests/, anduv run pre-commit run --all-files.
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.
- today Changed · -11 tokens per session 8d02cdfcd5a1
- 3d ago First seen · 29 lines · 582 tokens per session scan A 54654d4ca04f
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.
Other instructions, from other repositories
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.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.