forge-completeness

name	forge-completeness
description	Score a subject spec against the ontology and canonical readiness contract. Use this when you need claim-level coverage scoring, corroboration checks, critical-decision coverage, risk-weighted assumptions, contradiction penalties, and blocker-aware 80/80 gates.
allowed-tools	Read, Write, Glob, Grep

Use this whenever canon changes materially.

Do not finalize readiness here. Emit structured inputs for the forge-orchestrator verdict.

Ontology

Track these dimensions:

• purpose and success criteria
• actors
• boundaries
• core flows
• entities
• state transitions
• interfaces
• constraints
• failure modes
• non-goals
• assumptions
• acceptance criteria

Sub-signal requirements

The following sub-signals feed into existing dimensions as required coverage inputs. A dimension cannot score above partial unless its mapped sub-signals are addressed (per the archetype applicability matrix in forge-loop).

Sub-signal	Feeds into dimensions
Security boundaries	constraints, interfaces
Edge cases	failure modes, state transitions
Scalability assumptions	constraints, assumptions
Operational concerns	interfaces, constraints
Ordering and concurrency	state transitions, core flows

For archetypes where a sub-signal is marked optional in the forge-loop matrix, that sub-signal does not block the parent dimension's coverage. It only contributes if evidence is present.

Domain terminology check

Read UBIQUITOUS-LANGUAGE.md at the project root (if it exists). Verify that major canonical claims, entity names, and interface descriptions use terms from the glossary. Flag inconsistencies as a sub-signal feeding into entities and interfaces dimensions. Mismatched terminology does not block readiness on its own but reduces confidence in those dimensions.

For each dimension track:

• coverage
• confidence
• evidence_count
• independent_evidence_count
• weighted_support
• contradiction_count
• planning_blocker

Also track:

• critical_decision_coverage
• corroboration_score
• acceptance_scenarios_present
• assumption_load
• assumption_risk_score
• unconfirmed_product_decisions

Scores

Compute:

• completeness_score
• evidence_confidence_score
• decision_risk_score

Rules:

• blocker dimensions weigh more
• duplicate evidence should not inflate support
• multiple files from one origin do not count as strong corroboration by themselves
• unresolved assumptions cap the affected dimension
• contradictions reduce confidence and readiness
• a single direct prompt may strongly support purpose, but may not alone push boundaries, interfaces, constraints, failure modes, or acceptance criteria past partial unless the evidence is explicit
• no run may be planning-ready if a critical decision bucket is covered only by assumptions
• assumption risk matters more than raw assumption count
• on sparse analogy_feature or parity_clone runs with zero question rounds, core_outcome, scope_boundary, implementation_constraints, and acceptance_signal must remain partial, open, or assumed, not covered

Every assumption must be labeled:

• criticality: low | medium | high
• reversibility: easy | moderate | expensive

High-criticality or expensive-to-reverse assumptions increase assumption_risk_score directly.

Gates

Every scoring pass must emit:

• why_not_ready[]
• blocker_dimensions[]
• minimum_next_actions[]
• critical_decision_coverage
• corroboration_score
• assumption_risk_score
• unconfirmed_product_decisions

The orchestrator may only mark a run PLANNING_READY when:

• completeness_score >= 80
• evidence_confidence_score >= 80
• contradictions are resolved
• blocker reasons are empty
• critical decision buckets are closed
• acceptance scenarios are present or intentionally deferred
• assumption risk is below threshold

The orchestrator must not mark a run PLANNING_READY when:

• request_archetype is analogy_feature or parity_clone
• starting_evidence_density is sparse
• question_rounds_completed = 0

Output

Update:

• completeness-matrix.md
• manifest.json
• specs/<subject>.md under Completeness Status and Planning Readiness