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arjuncrevathi (@arjuncrevathi) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agni by arjuncrevathi — Agent Skill

Name: Agni
Author: arjuncrevathi

Agni

Testing standards — unit/integration/e2e strategy, pytest and vitest conventions, and LLM eval suites. Use when writing tests, discussing test coverage or TDD, fixing flaky tests, or building evals for prompts and model outputs.

File contents

Agni — Trial by Fire (Testing)

Agni governs the fire every change must pass through before it is trusted.

The pyramid

Many unit tests (fast, no I/O), some integration tests (real DB via testcontainers/Docker), few e2e tests (critical user flows only).
Unit suite runs in seconds and on every save; the full suite runs in CI on every PR (see brahma).
Coverage is a smoke alarm, not a goal: alert on drops, don't chase 100%. Untested error paths matter more than tested getters.

What to test

Test behavior through public interfaces, not implementation. A refactor that preserves behavior should break zero tests.
Every bug fix gets a regression test that fails on the old code, in the same PR (see durga).
Name tests as claims: test_expired_token_returns_401, not test_auth_2.
Cover the unhappy paths: empty input, timeout, malformed data, permission denied.

Tools

Python: pytest. Shared setup lives in fixtures (conftest.py), not copy-paste. Use pytest.mark.parametrize for input matrices; factory-boy/builders for test data.
JS/TS: vitest for unit/component (with Testing Library), playwright for e2e. Query by role/label, not CSS selectors.
Mock external services at the boundary only — the HTTP layer (respx, msw) or your adapter interface. Never mock your own domain logic; if you must, the design is wrong (see vishwakarma).

Flakes

No flaky tests. A test that fails 1-in-20 runs gets fixed today or deleted — retrying-until-green trains everyone to ignore red.
Usual demons: real time (sleep, datetime.now() — inject clocks), shared state between tests, unawaited async work, order dependence. Run with pytest -p no:randomly off / --random-order to expose ordering bugs.

AI-native specifics

Every prompt gets an eval suite: a golden dataset (20+ real, versioned examples) with expected outputs or grading criteria, runnable via one command.
Assert on structure, not exact wording: parse the JSON, check required fields, check facts/citations present — never assert output == "exact string" against a model.
LLM-as-judge with caution: pin the judge model and version, write a rubric, spot-check judge verdicts against human labels regularly. Prefer deterministic checks (schema, regex, contains) wherever possible.
Run evals in CI on any PR touching prompts, model config, or the prompt layer. Set a pass threshold; a score drop blocks merge.
Track eval scores over time (per prompt, per model version) in the prompt changelog (see saraswati) so regressions are visible, not vibes.
Keep eval runs cheap: temperature 0, small model for smoke evals, full suite nightly.

Before merging — checklist

New behavior covered by tests at the right pyramid level
Unhappy paths tested, not just the golden path
No mocks of internal domain logic
Suite passes 3 consecutive runs locally (no flakes)
Prompt/model changes ran the eval suite; score recorded

---name: agnidescription: Testing standards — unit/integration/e2e strategy, pytest and vitest conventions, and LLM eval suites. Use when writing tests, discussing test coverage or TDD, fixing flaky tests, or building evals for prompts and model outputs.---# Agni — Trial by Fire (Testing)Agni governs the fire every change must pass through before it is trusted.## The pyramid- Many unit tests (fast, no I/O), some integration tests (real DB via testcontainers/Docker), few e2e tests (critical user flows only).- Unit suite runs in seconds and on every save; the full suite runs in CI on every PR (see `brahma`).- Coverage is a smoke alarm, not a goal: alert on drops, don't chase 100%. Untested error paths matter more than tested getters.## What to test- Test behavior through public interfaces, not implementation. A refactor that preserves behavior should break zero tests.- Every bug fix gets a regression test that fails on the old code, in the same PR (see `durga`).- Name tests as claims: `test_expired_token_returns_401`, not `test_auth_2`.- Cover the unhappy paths: empty input, timeout, malformed data, permission denied.## Tools- Python: `pytest`. Shared setup lives in fixtures (`conftest.py`), not copy-paste. Use `pytest.mark.parametrize` for input matrices; `factory-boy`/builders for test data.- JS/TS: `vitest` for unit/component (with Testing Library), `playwright` for e2e. Query by role/label, not CSS selectors.- Mock external services at the boundary only — the HTTP layer (`respx`, `msw`) or your adapter interface. Never mock your own domain logic; if you must, the design is wrong (see `vishwakarma`).## Flakes- No flaky tests. A test that fails 1-in-20 runs gets fixed today or deleted — retrying-until-green trains everyone to ignore red.- Usual demons: real time (`sleep`, `datetime.now()` — inject clocks), shared state between tests, unawaited async work, order dependence. Run with `pytest -p no:randomly` off / `--random-order` to expose ordering bugs.## AI-native specifics- Every prompt gets an eval suite: a golden dataset (20+ real, versioned examples) with expected outputs or grading criteria, runnable via one command.- Assert on structure, not exact wording: parse the JSON, check required fields, check facts/citations present — never `assert output == "exact string"` against a model.- LLM-as-judge with caution: pin the judge model and version, write a rubric, spot-check judge verdicts against human labels regularly. Prefer deterministic checks (schema, regex, contains) wherever possible.- Run evals in CI on any PR touching prompts, model config, or the prompt layer. Set a pass threshold; a score drop blocks merge.- Track eval scores over time (per prompt, per model version) in the prompt changelog (see `saraswati`) so regressions are visible, not vibes.- Keep eval runs cheap: temperature 0, small model for smoke evals, full suite nightly.## Before merging — checklist- [ ] New behavior covered by tests at the right pyramid level- [ ] Unhappy paths tested, not just the golden path- [ ] No mocks of internal domain logic- [ ] Suite passes 3 consecutive runs locally (no flakes)- [ ] Prompt/model changes ran the eval suite; score recorded

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