speckit-clarify

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Spec-Forge Setup (replaces file-based Pre-Execution Checks)

1. Read .specify/feature.json (written by /speckit-specify) to get spec_forge_feature_id.
   • If the file is missing or has no spec_forge_feature_id, instruct the user to run /speckit-specify first (do not create a new feature here) and stop.
2. Call get_feature_snapshot with { featureId } to load the current specification content, the active constitution, and any existing clarifications. This is the in-memory equivalent of loading FEATURE_SPEC and .specify/memory/constitution.md in the original command — there is no check-prerequisites.ps1 step needed.

Outline

Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly via Spec-Forge's clarify_ask / clarify_answer tools (Spec-Forge models clarifications as first-class Q&A rows tied to the feature — there is no ## Clarifications markdown section to append to).

Note: This clarification workflow is expected to run (and be completed) BEFORE invoking /speckit-plan. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.

Execution steps:

1. Use the specification content loaded in Spec-Forge Setup step 2 as the artifact under review.

2. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).

   Functional Scope & Behavior:

   • Core user goals & success criteria
   • Explicit out-of-scope declarations
   • User roles / personas differentiation

   Domain & Data Model:

   • Entities, attributes, relationships
   • Identity & uniqueness rules
   • Lifecycle/state transitions
   • Data volume / scale assumptions