Opp repl smoke tests

Skill tabgab/opp_repl-skill/opp-repl-smoke-tests

Run smoke tests with opp_repl — the cheapest regression check. run_smoke_tests() verifies that every (or filtered) simulation starts and terminates without crashing. Load this for baseline health checks before running heavier test suites (fingerprint, statistical, etc.).From its SKILL.md

Install

npx -y skills add tabgab/opp_repl-skill --skill opp-repl-smoke-tests

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SKILL.md

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Smoke tests

Smoke tests are the minimum viable regression check: every selected simulation is launched briefly and its exit status is verified. A PASS means "it ran without crashing"; it does NOT imply correctness.

Upstream reference: https://github.com/omnetpp/opp_repl/blob/main/doc/smoke_tests.md

Python API

# Whole default project
run_smoke_tests()

# Explicit project + filter
run_smoke_tests(simulation_project=aloha_project,
                config_filter="PureAlohaExperiment")

# Time-limit to keep runtime sane
run_smoke_tests(sim_time_limit="1s")

Typical output:

[6/7] . -c TandemQueueExperiment -r 3 PASS
[5/7] . -c TandemQueueExperiment -r 2 PASS
...
Multiple smoke test results: PASS, summary: 7 PASS in 0:00:01.117562

Re-running after a change:

r = run_smoke_tests(simulation_project=aloha_project,
                    config_filter="PureAlohaExperiment")
r.rerun()                       # repeat everything
r.get_fail_results().rerun()  # only failures

Command line

opp_run_smoke_tests --load "~/workspace/omnetpp/**/*.opp" -p fifo
opp_run_smoke_tests -t 1s --filter PureAloha

When to use smoke tests

First CI step -- fast, catches crashes from refactors.
Feature branch sanity check before running longer suites.
Validating a fresh OMNeT++ or INET install.
As part of run_all_tests() / run_release_tests().

For deeper behavioral regressions, follow up with:

opp-repl-fingerprint-tests (trajectory-level).
opp-repl-statistical-tests (scalar results).
opp-repl-speed-tests (performance).
opp-repl-chart-tests (rendered charts).

Pitfalls

PASS is WEAK evidence: many real regressions are behavioral, not crashing. Always layer with fingerprint or statistical tests.
sim_time_limit is critical: without it, a slow sample can turn a smoke suite into an hour-long run. Upstream doesn't set a default -- pick 1s to 10s depending on model complexity.
Use simulation_config_filter=lambda c: not c.abstract semantics is already the default; passing your own predicate overrides it.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.