# Opp Repl Overview > START HERE. Entry-point skill for opp_repl — the Python REPL, CLI tools, and MCP server for OMNeT++ simulations (github.com/omnetpp/opp_repl). Contains a decision tree that maps the user's task to the exact sibling skills to load. Load this first whenever the user asks about opp_repl, OMNeT++ simulations, running sims from Python, or regression-testing simulation models. Even weak models can navigate the rest of the pack via this skill's triage table. - Skill: `tabgab/opp-repl-overview` (Agent Skill) - Install (CLI): `npx skillmds@latest add tabgab/opp-repl-overview` - Raw SKILL.md: https://api.skillmd.com/api/skills/tabgab/opp-repl-overview/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tabgab (https://skillmd.com/u/tabgab) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tabgab/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 | Capability | Skill | |-----------------------------|------------------------------------| | Install / setenv | `opp-repl-installation` | | Core object model | `opp-repl-concepts` | | `.opp` descriptors | `opp-repl-opp-files` | | **NEW project from zero** | `opp-repl-project-scaffolding` | | Interactive REPL usage | `opp-repl-repl-usage` | | Shell wrappers for CI | `opp-repl-cli-tools` | | Build + run simulations | `opp-repl-running-simulations` | | Filtering configs/runs | `opp-repl-filtering` | | Task & result model | `opp-repl-tasks-and-results` | | **Read .sca / .vec** | `opp-repl-result-analysis` | | **Error decoder** | `opp-repl-troubleshooting` | | Smoke tests | `opp-repl-smoke-tests` | | Fingerprint tests | `opp-repl-fingerprint-tests` | | Statistical tests | `opp-repl-statistical-tests` | | Speed tests | `opp-repl-speed-tests` | | Chart tests | `opp-repl-chart-tests` | | Module-image tests | `opp-repl-module-image-tests` | | Sanitizer tests | `opp-repl-sanitizer-tests` | | Feature + release tests | `opp-repl-feature-and-release-tests` | | Comparing simulations | `opp-repl-comparing-simulations` | | Parameter optimization | `opp-repl-parameter-optimization` | | Coverage reports | `opp-repl-coverage-reports` | | Profiling (perf/Hotspot) | `opp-repl-profiling` | | Overlay builds | `opp-repl-overlay-builds` | | SSH / Dask cluster runs | `opp-repl-ssh-cluster` | | GitHub Actions dispatch | `opp-repl-github-actions` | | opp_env integration | `opp-repl-opp-env-integration` | | MCP server for AI agents | `opp-repl-mcp-server` | | Shared human+AI REPL | `opp-repl-shared-terminal` | | Sandboxed execution | `opp-repl-sandbox` | | End-to-end AI recipes | `opp-repl-ai-workflows` | ## 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.