forge-loop

name	forge-loop
description	Run the evidence-first Forge loop for a subject spec. Use this when you need to process evidence one unit at a time, produce unit summaries, rewrite the rolling summary, branch out to sub-agents for bounded exploration, choose adaptive clarification profiles, and emit speculative variants when blockers remain.
allowed-tools	Read, Write, Edit, Glob, Grep, Agent

Use this after intake and whenever more evidence work is needed.

Core loop

For each evidence unit:

1. write a one-sentence unit summary
2. rewrite the one-sentence rolling summary
3. update claims and provenance links
4. update critical decision coverage and blocker state
5. if the evidence introduces or refines domain terms, update UBIQUITOUS-LANGUAGE.md at the project root
6. choose the next action:
   • Read More
   • Ask
   • Mark Assumption

Hard rules

• Never append to the rolling summary mechanically. Rewrite it.
• Every canonical claim must be traceable through sidecar claim ids and evidence unit ids.
• If the evidence source is large, spawn sub-agents per independent area.
• Sub-agents return typed patch proposals only.
• No major canon claim may exist without evidence_refs or an explicit assumption id.
• Analogy prompts are ambiguity-heavy by default.
• question_rounds_completed = 0 on a sparse analogy_feature or parity_clone run means required clarification buckets are still open unless the user explicitly approved a recommended default pack.

Clarification policy

First classify or reuse request_archetype.

Use these clarification profiles:

• feature, analogy_feature, parity_clone
  • core_outcome
  • scope_boundary
  • implementation_constraints
  • acceptance_signal
• bugfix
  • reproduction
  • expected behavior
  • environment/version boundary
• integration
  • external system boundary
  • auth/data ownership
  • failure/retry expectations
• migration
  • source/target boundary
  • compatibility window
  • rollback expectation

Additional required categories by archetype:

Category	feature	analogy_feature	parity_clone	integration	bugfix	migration	refactor	reverse_spec
Failure modes	required	required	required	required	required	required	optional	required
Security boundaries	required	required	required	required	optional	optional	optional	required
Edge cases	required	required	required	required	required	required	optional	required
Scalability assumptions	required	optional	optional	required	optional	required	optional	optional
Operational concerns	required	optional	optional	required	optional	required	optional	required
Ordering and concurrency	required	optional	optional	required	required	required	optional	optional

Categories marked required must be addressed before the drafting gate opens. Categories marked optional are skipped unless evidence suggests relevance.

Walk down each branch of the design tree. For each branch, determine whether evidence closes it or whether ambiguity remains that blocks critical decision coverage.

Ask only if:

• passive evidence is exhausted, ambiguous, or lower value
• the ambiguity blocks critical decision coverage or blocker closure

Ask the user directly when input is needed. Ask one batch at a time, max 3, with recommended options first. Allow a recommended default pack when the user can approve defaults in one reply. Record answers as new evidence_unit records.

Research-first directive

Walk down each branch of the design tree. Before escalating any question to the user:

1. search the codebase for existing patterns, implementations, or conventions that answer it
2. check existing evidence and prior clarification answers for implicit answers
3. check AGENTS.md for prior implementation learnings that answer it
4. only escalate if genuinely unanswerable from available sources

For sparse analogy_feature or parity_clone runs:

• seed required clarification ids before drafting:
  • rq-core-outcome
  • rq-scope-boundary
  • rq-implementation-constraints
  • rq-acceptance-signal
• do not mark those buckets covered from analogy alone
• if the user does not answer, keep the run in AWAITING_CLARIFICATION or SPECULATIVE_DRAFT

Raised drafting gate

All required categories (per archetype matrix) must be addressed before transitioning to drafting. "Addressed" means the loop has a concrete answer from evidence, codebase research, or user answer — or has explicitly marked the category as out-of-scope with user agreement. "We didn't think to ask" is not addressed.

Escalation re-entry

When adversarial-escalation.json or beads-escalation.json is present as input, scope the round to the escalated findings only. Do not run a full generic pass. Address the specific findings, affected sections, and reopened blockers listed in the escalation artifact. Both artifact types use the same schema and are handled identically.

Speculative mode

If required clarifications remain unresolved:

• do not collapse uncertainty into one canonized solution shape
• emit 2-3 bounded variants instead of one settled handoff path
• each variant must list:
  • included scope
  • excluded scope
  • unresolved decisions
  • tradeoffs
  • risk notes
• set planning_status=SPECULATIVE_DRAFT
• keep handoff_status=WITHHELD
• do not emit one canonized “best guess” subject spec as if it were final

Required outputs

Update:

• spec-ledger.md
• question-backlog.md
• evidence-ledger.jsonl
• claim-ledger.jsonl
• the Core Model, Behavior and Flows, and Open Questions sections

question-backlog.md must always split:

• Required Clarifications
• Deferred Questions
• Non-blocking Assumptions

No open planning-critical questions is illegal for a sparse analogy_feature or parity_clone run unless:

• each required clarification id is closed by evidence
• or answered by the user
• or explicitly approved through a recommended default pack

Branching guidance

Good fanout targets:

• separate docs sections
• separate subsystems
• UI vs API behavior
• contradiction investigations

Bad fanout targets:

• tightly coupled edits to the same section
• final readiness decisions
• user interaction policy