Pick the right megaplan profile, thinking-strength tier, and robustness level for the work in front of you — for both Codex and Claude harnesses. Consult before invoking megaplan.
Three-round adversarial critique of epic drafts (high / mid / low abstraction) with revision after each round. Produces a chain-ready revised epic.
Observe an in-flight megaplan — introspect state, trace events, diagnose blockages, detect drift. Companion to megaplan-decision. Use during and after a run, not before.
AI agent harness for coordinating Claude and GPT to make and execute extremely robust plans.
Methodology for running multi-profile LLM bake-offs via megaplan and presenting fair, blind-assessed comparisons. Cost/quality discipline, prompt hygiene, pre-merge gates, and reporting patterns. Use when the user says "bakeoff", "bake off", "megaplan bakeoff", or asks to compare profile mixes head-to-head.
Run megaplan plans and chains inside a provider-managed container (today, Railway) with a persistent workspace volume. Use when the run needs to outlast a local terminal session, span multiple repos, or share a long-lived dev box across concurrent chains. Covers `cloud.yaml` fields, `extra_repos[]` + `chain_session` multi-tenancy, the operator loop, and the gotchas that wedge fresh runs.
Run an epic — a chain of sprint-sized megaplans driven sequentially via `megaplan chain`. Use when the work is bigger than ~2 weeks and needs to be decomposed into multiple plans with state, ordering, and failure semantics handled by the harness.
File and manage megaplan tickets — short, repo-scoped notes on problems or observations that get folded into epics and auto-addressed when the resolving epic completes.