Agent Interview and Planning

Extracts the user's true intent through one-question-at-a-time interviews, surfacing hidden assumptions before any plan or code is written.

Reads a plan document, decomposes it into steps, designs a per-step agent chain from the ECC catalogue, and emits ready-to-paste /orchestrate custom prompts without executing them.

Runs a structured clarifying interview for new project requests before building, then outputs a fully specified prompt.md for a fresh agent session to execute.

Inspect and adjust the agent's runtime state, including model, context window, iteration limits, token usage, workspace configuration, subagent status, and request routing metadata.

Establishes a two-party working posture where the user directs and the agent executes, surfacing every genuine fork as a batched single-select question with a recommended default.

Guides collaborative dialogue to turn ideas into approved designs and specs before any implementation begins.

Guides users through a structured interview to clarify their AI agent idea and produces an Agent Requirement Document (ARD).

Turns a rough request into working software via a governed build loop: align first, plan, then iterate one task at a time with deterministic backpressure and holdout-scenario judging.

Guides a structured planning pipeline for coding tasks, including scope detection, research, plan creation, and review.