executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
classic-agentsUse the unsupported AutoGPT Classic, Forge, and direct benchmark packages safely, with workspace, CLI, permissions, and report guidance.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill only for the classic/ project: the original AutoGPT agent,
Forge framework, and direct_benchmark harness. Classic is explicitly
unsupported, its dependencies are not maintained, and the repository documents
known security risks. Prefer the current Platform for new production work.
python scripts/classic_cli_probe.py --repo <checkout> for read-only
layout/import/help checks. It never starts an agent or benchmark.cd classic
poetry install
cp .env.example .env # Forge-oriented setup; use the matching Classic template
poetry run python -m forge
poetry run autogpt --help
poetry run serve --help
poetry run direct-benchmark --help
Keep API keys in local env files only. A Classic workspace stores agent state,
permissions, history, and files under .autogpt/; inspect the permission layer
before allowing shell, file, or web actions.
direct-benchmark run can execute many challenges, strategies, model presets,
parallel workers, retries, and LLM calls. Before running it, choose the
challenge subset, model/provider, attempts, concurrency, timeout, workspace,
and report directory. Use list-challenges, list-models, and
list-strategies first; use --json, --quiet, --keep-answers, or
--retry-failures deliberately. Never assume a missing API key is a reason to
silently switch providers.
Focused safe tests include pytest forge/tests/test_permissions.py -q and
unit tests under original_autogpt/tests/unit/. Route current Platform
self-hosting, backend, and frontend work to the sibling platform-* skills.