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name: extensions-and-environments description: "Use MetaGPT extension optimizers, Android/Stanford Town/werewolf/Minecraft integrations, and external environment APIs safely." disable-model-invocation: true metadata: disco-role: operating license: MIT

Extensions and Environments

Use this sub-skill when a request involves MetaGPT extension workflows or external environment integrations: AFlow workflow optimization, SPO prompt optimization, CR code-review experiments, SELA AutoML experiments, Android Assistant, Stanford Town, werewolf, Minecraft, MGX/software environments, or custom ExtEnv read/write APIs.

Route Here For

• Running safe preflight for AFlow or SPO optimizer entrypoints without downloading datasets or calling LLMs.
• Planning AFlow optimization, custom AFlow benchmarks, workflow templates, operators, datasets, and optimizer output directories.
• Planning SPO prompt optimization, template YAMLs, CLI/API use, Streamlit UI setup, and prompt output inspection.
• Understanding Android Assistant learning/acting stages, ADB/device/emulator prerequisites, screenshots/XML paths, and safe setup checks.
• Starting or adapting Stanford Town or werewolf simulations when the user confirms LLM cost, assets, services, and run length.
• Designing custom environment integrations with ExtEnv, Environment, EnvAPIAbstract, mark_as_readable, mark_as_writeable, and concrete Android/Stanford Town/werewolf/Minecraft envs.
• Understanding high-level CR and SELA extension flows, including Python/Java patch review points and AutoML/MCTS experiment configs.

Route Elsewhere

• Core Action, Role, Team, CLI project generation, incremental runs, and software-company workflows: use software-company.
• Data Interpreter task planning/execution and notebook-style analysis: use data-interpreter.
• RAG pipelines, search/browser tools, vector stores, and tool registries: use rag-and-tools.
• Serialization, maintainer internals, memory/experience persistence APIs, and repository parser internals: use maintainer-apis when present.
• Provider credentials, root installation, and cross-cutting LLM configuration failures: use the root MetaGPT config/troubleshooting guidance first, then return here for extension-specific setup.

Reference Map

• references/workflows.md: AFlow, SPO, Android Assistant, Stanford Town, werewolf, CR/SELA, Minecraft, and custom environment recipes.
• references/api-reference.md: extension modules, optimizer classes, environment APIs, action/observation spaces, and config concepts.
• references/configuration.md: optimizer configs, LLM model names, datasets, workspaces, device/browser/service prerequisites, and skip conditions.
• references/troubleshooting.md: missing datasets/templates, API keys, long optimization costs, optional imports, Android/ADB, simulations, Minecraft services, and environment API misuse.
• scripts/optimizer_help_check.py: safe helper for AFlow/SPO parser help and module import availability checks.

Safe First Steps

1. Use scripts/optimizer_help_check.py --target all --mode help to confirm bundled AFlow/SPO parser surfaces without importing optimizer modules or running optimization.
2. Treat AFlow/SPO full optimization, CR confirmation, SELA experiments, Android Assistant, Stanford Town, werewolf, and Minecraft as LLM-costing, service/device-dependent, or long-running unless the user explicitly confirms prerequisites.
3. Prefer planning, config validation, parser help, and module import checks before any command that downloads datasets, starts devices/services, launches browser/UI processes, or runs multi-round simulations.