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ai-training/.cursor/skills/test-run/SKILL.md
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Oleksandr Bezdieniezhnykh cdcd1f6ea7 Fix .cursor skills consistency: flow resolution, tracker-agnostic refs, report naming, error recovery
- Rewrite autopilot flow resolution to 4 deterministic rules based on source code + docs + state file presence
- Replace all hard-coded Jira references with tracker-agnostic terminology across 30+ files
- Move project-management.mdc to _project.md (project-specific, not portable with .cursor)
- Rename FINAL_implementation_report.md to context-dependent names (implementation_report_tests/features/refactor)
- Remove "acknowledged tech debt" option from test-run — failing tests must be fixed or removed
- Add debug/error recovery protocol to protocols.md
- Align directory paths: metrics -> 06_metrics/, add 05_security/, reviews/, 02_task_plans/ to README
- Add missing skills (test-spec, test-run, new-task, ui-design) to README
- Use language-appropriate comment syntax for Arrange/Act/Assert in coderule + testing rules
- Copy updated coderule.mdc to parent suite/.cursor/rules/
- Raise max task complexity from 5 to 8 points in decompose
- Skip test-spec Phase 4 (script generation) during planning context
- Document per-batch vs post-implement test run as intentional
- Add skill-internal state cross-check rule to state.md
2026-03-28 02:34:00 +02:00

5.6 KiB

name, description, category, tags, disable-model-invocation
name description category tags disable-model-invocation
test-run Run the project's test suite, report results, and handle failures. Detects test runners automatically (pytest, dotnet test, cargo test, npm test) or uses scripts/run-tests.sh if available. Trigger phrases: - "run tests", "test suite", "verify tests" build
testing
verification
test-suite
true

Test Run

Run the project's test suite and report results. This skill is invoked by the autopilot at verification checkpoints — after implementing tests, after implementing features, or at any point where the test suite must pass before proceeding.

Workflow

1. Detect Test Runner

Check in order — first match wins:

  1. scripts/run-tests.sh exists → use it (the script already encodes the correct execution strategy)
  2. docker-compose.test.yml exists → run the Docker Suitability Check (see below). Docker is preferred; use it unless hardware constraints prevent it.
  3. Auto-detect from project files:
    • pytest.ini, pyproject.toml with [tool.pytest], or conftest.pypytest
    • *.csproj or *.slndotnet test
    • Cargo.tomlcargo test
    • package.json with test script → npm test
    • Makefile with test target → make test

If no runner detected → report failure and ask user to specify.

Docker Suitability Check

Docker is the preferred test environment. Before using it, verify no constraints prevent easy Docker execution:

  1. Check _docs/02_document/tests/environment.md for a "Test Execution" decision (if the test-spec skill already assessed this, follow that decision)
  2. If no prior decision exists, check for disqualifying factors:
    • Hardware bindings: GPU, MPS, CUDA, TPU, FPGA, sensors, cameras, serial devices, host-level drivers
    • Host dependencies: licensed software, OS-specific services, kernel modules, proprietary SDKs
    • Data/volume constraints: large files (> 100MB) impractical to copy into a container
    • Network/environment: host networking, VPN, specific DNS/firewall rules
    • Performance: Docker overhead would invalidate benchmarks or latency measurements
  3. If any disqualifying factor found → fall back to local test runner. Present to user using Choose format:
══════════════════════════════════════
 DECISION REQUIRED: Docker is preferred but factors
 preventing easy Docker execution detected
══════════════════════════════════════
 Factors detected:
 - [list factors]
══════════════════════════════════════
 A) Run tests locally (recommended)
 B) Run tests in Docker anyway
══════════════════════════════════════
 Recommendation: A — detected constraints prevent
 easy Docker execution
══════════════════════════════════════
  1. If no disqualifying factors → use Docker (preferred default)

2. Run Tests

  1. Execute the detected test runner
  2. Capture output: passed, failed, skipped, errors
  3. If a test environment was spun up, tear it down after tests complete

3. Report Results

Present a summary:

══════════════════════════════════════
 TEST RESULTS: [N passed, M failed, K skipped, E errors]
══════════════════════════════════════

Important: Collection errors (import failures, missing dependencies, syntax errors) count as failures — they are not "skipped" or ignorable.

4. Diagnose Failures

Before presenting choices, list every failing/erroring test with a one-line root cause:

Failures:
 1. test_foo.py::test_bar — missing dependency 'netron' (not installed)
 2. test_baz.py::test_qux — AssertionError: expected 5, got 3 (logic error)
 3. test_old.py::test_legacy — ImportError: no module 'removed_module' (possibly obsolete)

Categorize each as: missing dependency, broken import, logic/assertion error, possibly obsolete, or environment-specific.

5. Handle Outcome

All tests pass → return success to the autopilot for auto-chain.

Any test fails or errors → this is a blocking gate. Never silently ignore or skip failures. Present using Choose format:

══════════════════════════════════════
 TEST RESULTS: [N passed, M failed, K skipped, E errors]
══════════════════════════════════════
 A) Investigate and fix failing tests/code, then re-run
 B) Remove obsolete tests (if diagnosis shows they are no longer relevant)
 C) Abort — fix manually
══════════════════════════════════════
 Recommendation: A — fix failures before proceeding
══════════════════════════════════════
  • If user picks A → investigate root causes, attempt fixes, then re-run (loop back to step 2)
  • If user picks B → confirm which tests to remove, delete them, then re-run (loop back to step 2)
  • If user picks C → return failure to the autopilot

Trigger Conditions

This skill is invoked by the autopilot at test verification checkpoints. It is not typically invoked directly by the user.