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name: software-company description: "Use MetaGPT's core virtual software-company workflow from CLI or Python, including project generation, incremental updates, recovery, config initialization, and custom Role/Action/Team patterns." disable-model-invocation: true metadata: disco-role: operating license: MIT

Software Company

Use this sub-skill when a user asks how to run MetaGPT as a virtual software company, generate a project from a one-line idea, continue an existing project, recover a serialized team, initialize configuration, choose CLI options, or build a small custom Action/Role/Team workflow.

Quick Routing

User intent	Use this sub-skill for	Key reference
"Generate a 2048 game" or "build a repo from this idea"	metagpt CLI and metagpt.software_company.generate_repo project generation	CLI and Python recipes
"Explain MetaGPT CLI options"	Option names, defaults, boolean spelling, and safe dry checks	CLI option guide
"Configure OpenAI/Claude/Ollama"	config2.yaml initialization, precedence, provider fields, and placeholder-key diagnosis	Configuration workflow
"Continue this project"	--inc, --project-name, --project-path, and QA rewrite inputs	Incremental updates
"Recover a previous run"	--recover-path / recover_path validation and serialized team storage	Recovery workflow
"Build a custom agent/team"	Safe Action, Role, RoleReactMode, Message, and Team patterns	Custom roles and teams

Before Running LLM Workflows

• Confirm Python is >=3.9,<3.12; Python 3.9 or 3.10 is the safest choice, and use the root MetaGPT installation/config guidance when present.
• Confirm metagpt --help works before any project-generation command.
• Initialize config with metagpt --init-config, then replace placeholder secrets in ~/.metagpt/config2.yaml; do not paste API keys into chat or generated files.
• Treat metagpt "Create a 2048 game", generate_repo(...), Team.run(...), debate examples, and custom roles that call _aask(...) as LLM-spending workflows requiring working provider config.
• Use scripts/inspect_role_action.py --check-cli-help --json for safe no-network inspection of installed APIs and CLI availability, then use root troubleshooting guidance for cross-cutting provider/config failures when present.

Runtime References

• Workflows: CLI/Python recipes, output layout, config, incremental/recovery usage, custom agent patterns, and safe validation.
• API Reference: important signatures, classes, parameters, defaults, and behavior notes.
• Troubleshooting: config placeholders, provider failures, dependency issues, Mermaid/browser problems, Typer/click help, --inc, recover_path, and long outputs.

Boundaries

• For Data Interpreter roles, DataAnalyst/DI-generated project workflows, and notebook-style data analysis, route to the data-interpreter sub-skill.
• For RAG, search, browser, tool registry, or retrieval internals, route to the rag-and-tools sub-skill.
• For AFlow, SPO, game examples beyond the core workflow, Android assistants, and environment extensions, route to extensions-and-environments when that sub-skill is present.
• For serialization internals, experiment pools, repository parser internals, and maintainer APIs, route to maintainer-apis when that sub-skill is present.