Caliber is a tool that generates and continuously updates AI context and configuration files for software repositories, including CLAUDE.md, AGENTS.md, and platform-specific rules. Development teams use it to keep coding agents aligned with the current codebase across tools such as Claude Code, Cursor, Codex, OpenCode, and GitHub Copilot. Its catalogue entries include skills, hooks, rules, instructions, and settings for configuring that workflow.
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.
git clone --depth 1 https://github.com/caliber-ai-org/ai-setupWrote 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/rules/caliber-ai-org/ai-setup/testing)<a href="https://agentmods.dev/rules/caliber-ai-org/ai-setup/testing"><img src="https://agentmods.dev/badge/rules/caliber-ai-org/ai-setup/testing/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/rules/caliber-ai-org/ai-setup/testing"><img src="https://agentmods.dev/badge/rules/caliber-ai-org/ai-setup/testing.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 | $0.00000 | $0.00194 |
| Opus 5.5 | $0.00000 | $0.00078 |
| Sonnet 5.5 | $0.00000 | $0.00039 |
| Haiku 4.5 | $0.00000 | $0.00019 |
Grade A, and why
testing scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- Use `vi.mock()` for `fs`, `child_process` · `vi.mocked()` for type-safe assertions What it actually says
- Vitest config:
vitest.config.ts· Setup:src/test/setup.ts - Run all:
npm run test· Single:npx vitest run src/scoring/__tests__/accuracy.test.ts - Coverage:
npm run test:coverage(v8 provider) - LLM calls globally mocked in
src/test/setup.ts— never call real providers - Use
vi.mock()forfs,child_process·vi.mocked()for type-safe assertions - Tests colocated:
src/llm/__tests__/config.test.tstestssrc/llm/config.ts - Pattern:
describe→beforeEach(vi.clearAllMocks)→itwithexpect - Environment vars: save in
beforeEach, restore inafterEach
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.
- 1mo ago First seen · 13 lines · 0 tokens per session scan A 2f657a75c504
testing is a cursor rule published in the GitHub repository caliber-ai-org/ai-setup (1,306 stars, last pushed 22d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 194 tokens. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other cursor rules, from other repositories
unit-tests-todos
When proposing/writing unit test specs.
hatch3r-testing
Coverage thresholds, mocking strategy, property-based testing, mutation-score targets, flaky test quarantine, and snapshot test discipline.
adr-003
Architecture questions, answered from your ADRs.
api
For creating tests for the backend in bubblelab-api.
testing
Testing conventions, bug fix workflow, TDD, mocking discipline, and test commands.
pytest-what-to-test-and-mocking
We mock external API calls (mark live tests with @pytest.mark.flaky()). We reuse fixtures from conftest.py. We always use pytest as testing framework - never import unittest. We use pytest-mock (mocker fixture) and monkeypatch for mocking, not unittest.mock.