Opp Repl Overview

opp_repl — overview and skill map

opp_repl replaces ad-hoc shell loops and Qtenv clicking with reproducible, scriptable workflows for OMNeT++. Same feature set, three interfaces:

1. An IPython REPL (opp_repl) — interactive Python with all helpers pre-loaded.
2. A shell wrapper suite (opp_build_project, opp_run_simulations, opp_run_*_tests, opp_update_*_test_results) — for CI and scripts.
3. An MCP server — so AI agents can drive the REPL as a tool endpoint (a Unix-socket stdio bridge for local clients like Claude Code, or a TCP+token endpoint for remote ones).

Decision tree — load exactly these skills for common tasks

I want to...

...install opp_repl. -> opp-repl-installation

...create a NEW OMNeT++ project from scratch (write files, build, run). -> opp-repl-project-scaffolding (← START HERE for "make a new simulation", "create a new model", "build an example from zero"). On current opp_repl this is ONE function call: create_project(name, path=..., namespace=False). -> then opp-repl-running-simulations -> then opp-repl-result-analysis to read the numbers out (on current opp_repl: r.get_scalars() on the result). -> keep opp-repl-troubleshooting on hand; new projects hit §4 of that skill on namespace/package mismatches.

...run simulations that already exist. -> opp-repl-running-simulations + opp-repl-filtering -> opp-repl-tasks-and-results for drill-down / rerun -> opp-repl-result-analysis for reading the numbers.

...regression-test my project. Pick the type:

• opp-repl-smoke-tests — it ran, it didn't crash.
• opp-repl-fingerprint-tests — behavior hasn't drifted.
• opp-repl-statistical-tests — scalar results in tolerance.
• opp-repl-speed-tests — CPU instr. count stable.
• opp-repl-chart-tests — analysis charts unchanged.
• opp-repl-module-image-tests — module/network rendering unchanged.
• opp-repl-sanitizer-tests — ASAN/UBSan clean.
• opp-repl-feature-and-release-tests — comprehensive suites. All test skills cross-reference opp-repl-running-simulations and opp-repl-tasks-and-results.

...compare / bisect / debug a regression. -> opp-repl-comparing-simulations + opp-repl-fingerprint-tests -> opp-repl-tasks-and-results for post-mortem inspection.

...tune a parameter to match a target. -> opp-repl-parameter-optimization + opp-repl-running-simulations.

...measure speed / cost. -> opp-repl-profiling (call-stack profiles) or opp-repl-speed-tests (regression) or opp-repl-coverage-reports (line coverage).

...distribute runs across multiple machines. -> opp-repl-ssh-cluster + opp-repl-running-simulations.

...read .sca / .vec result files after a run. -> opp-repl-result-analysis (includes a bundled script).

...debug a build or run failure. -> opp-repl-troubleshooting FIRST — it maps every common symptom ("Building X failed", exit 127, "Class not found", etc.) to its exact cause and fix.

...wire opp_repl into an AI agent / MCP client. -> opp-repl-mcp-server (stdio bridge over a Unix socket, or TCP+token) + opp-repl-ai-workflows.

...let a human (in a terminal) and the AI share ONE live REPL. -> opp-repl-shared-terminal (tmux + MCP, same namespace).

...isolate the agent's execute_python under bubblewrap. -> opp-repl-sandbox.

...set up CI on GitHub. -> opp-repl-cli-tools + opp-repl-github-actions.

...use a specific opp_env-managed OMNeT++ version (e.g. omnetpp-6.3.0 + inet-4.6.0). -> opp-repl-opp-env-integration + opp-repl-opp-files.

Compact feature inventory

Upstream API touchpoints worth knowing

Three high-value APIs on current opp_repl (Apr 2026+) that the skills route you to:

• create_project(name, path=..., namespace=False) — scaffold a new project in one call. See opp-repl-project-scaffolding.
• build_project(simulation_project=p) — builds. Auto-generates the Makefile from .oppbuildspec on the first call.
• r.get_scalars() / r.get_vectors() / r.get_histograms() — read results into a pandas DataFrame directly from a SimulationTaskResult or MultipleSimulationTaskResults.

Minimum viable skill set for a first session

If you're not sure what to load, start with this core set — it covers 80% of real-world opp_repl usage:

opp-repl-concepts                 (object model)
opp-repl-opp-files                (project descriptors)
opp-repl-running-simulations      (the main entry point)
opp-repl-tasks-and-results        (inspecting what happened)
opp-repl-result-analysis          (reading .sca files)
opp-repl-troubleshooting          (decoding errors)

Add one or two test-type skills when testing, or opp-repl-mcp-server when connecting an agent.

When NOT to use opp_repl

• One-off manual runs — plain opp_run or the OMNeT++ IDE is simpler.
• Pure analysis of existing .sca files with no rerun — just use opp_scavetool directly.
• Simulations that require GUI interaction during the run — opp_repl is headless.

Authoritative upstream references

• Repo: https://github.com/omnetpp/opp_repl
• Docs: https://github.com/omnetpp/opp_repl/tree/main/doc
• Each sibling skill cites the specific upstream doc file it distils.