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minimax-ai/codex-harness-patterns

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Long-running task patterns distilled from OpenAI Codex harness v0.149.0 — tool output budgeting, context pressure compaction, parallel sub-agent fan-out, structured plan streaming, self-review, sub-agent delegation, world-state tracking, background task management, thread-level goal persistence, per-sub-task model routing, completion auditing, fork-context decision, sub-agent family tracking, goal token budgeting, error recovery strategy, retry with backoff, streaming output reader, session handoff, long-term memory, skill auto-selection, plugin authoring helper, tool discovery pattern, and session branch/fork. 23 Skills total covering the complete agent lifecycle: planning → decomposition → sub-agent parallelism → execution → state tracking → tool discovery → skill/plugin authoring → memory persistence → session branching. Activates when an agent must manage token budget, decompose work, sustain multi-step tasks without losing focus, coordinate sub-agents, pick the right model for the job, prove that a non-trivial task is actually done, stay within an explicit goal token budget, recover from transient failures, read streaming output without filling context, hand off a session cleanly, persist memory across sessions, write a discoverable skill, design a discoverable tool, author a marketplace plugin, or branch / fork / revert a session. **Requires MiniMax Code 0.2.4+** (pinned to the mcode 0.2.4 task / bash tool surface — task(description, prompt, agent_name, run_in_background?), bash(command, timeout?, run_in_background?), task_query, task_output, task_stop — earlier mcode versions used legacy placeholders for the same fields and are not supported).