Run repository tests using commands suited to Python, Node, or Rust projects. Detect the project setup from its files, run the full suite or a selected target, and show passing results or failure details.
Run the test suite for the current repo, auto-detecting Python (pytest/uv), Node (vitest/pnpm), or Rust (cargo test)
Review the instructions the agent will receive before you install.
Detects the stack and runs tests with the right command.
if [ -f "uv.lock" ]; then
STACK="python"
elif [ -f "pnpm-lock.yaml" ] || [ -f "package.json" ]; then
STACK="node"
elif [ -f "Cargo.toml" ]; then
STACK="rust"
else
STACK="unknown"
fi
# All tests
uv run pytest --tb=short -q $ARGUMENTS
# With coverage
uv run pytest --cov=src --cov-report=term-missing -q
# Specific file or folder
uv run pytest $ARGUMENTS -v
# All tests
pnpm vitest run $ARGUMENTS
# With coverage
pnpm vitest run --coverage
# Watch mode (dev)
pnpm vitest
npm test -- --passWithNoTests $ARGUMENTS
cargo test --quiet $ARGUMENTS 2>&1
Tests: my-api (Python/pytest)
───────────────────────────────
uv run pytest --tb=short -q
[pytest output]
✅ 42 passed in 3.1s → Ready to push
On failure:
❌ 2 failed
FAILED tests/test_billing.py::TestInvoice::test_promo_expired
AssertionError: expected discount=0, got discount=10
→ Fix before pushing.
/ci:tests
/ci:tests tests/test_orders.py
/ci:tests src/components/Button.test.tsx
Target: $ARGUMENTS
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Evaluate Claude Code agents, skills, and commands with weighted quality criteria. Get scores, readiness grades, template comparisons, and practical improvement suggestions in Markdown or JSON reports to guide reviews before release.
Assess a codebase across seven areas, including exposed credentials, security, dependencies, structure, and tests. Receive category scores, findings tied to file locations, and a prioritized improvement plan with smaller tasks that can be completed quickly.
Measure code patterns such as casts to any or disabled lint rules, then run repeated, targeted edits. Keep changes that improve the chosen metric, revert unsuccessful changes, and track results for each improvement loop.
Generate several separate solutions, compare them against criteria fixed in advance, and verify the selected result through executable checks or an independent reviewer. Keep a proof log documenting candidates, evidence, failures, and remaining limits.
Monitor a live application after deployment and compare it with a saved baseline, a record of its earlier state. Check page responses, loading times, and new errors, flag persistent changes, and produce a per-page health report.
Rebuild project context after clearing a conversation or returning to work. Review recent Git history, uncommitted changes, and TODO markers to get a structured summary of project status and suggested next steps.