ln-disambiguate

name	ln-disambiguate
description	Collapse meaningful ambiguity with contrastive examples. Use when a plan/design has several plausible meanings, requirements feel vague, examples would clarify intent faster than grilling, or the user asks to disambiguate, find ambiguity, use behavioral kernels, or ask contrastive questions.

Ln Disambiguate

Generate cases where plausible interpretations diverge; ask the user to classify the case.

Users recognize intent in concrete examples faster than they author abstract predicates. Use the TiCoder move generalized beyond tests: produce examples, counterexamples, edge cases, and candidate outcomes that separate meanings. Then translate the answer into typed conclusions.

Use this instead of ln-grill when the work has enough shape that ambiguity collapse is the next move. Use ln-grill when the idea still needs broad Socratic pressure.

If local context can answer the question, inspect it instead of asking. Read only the context needed to form precise contrasts: memory/SPEC.md, memory/PLAN.md, and files they explicitly point to. Use the current lexicon; when terms are overloaded, name the competing meanings.

For each ambiguity:

1. Name the ambiguous claim.
2. Generate 2–4 competing interpretations.
3. Find the smallest scenario where they produce different outcomes.
4. Ask one contrastive classification question.
5. Translate the answer into candidate durable conclusions:
   • decision
   • invariant
   • constraint
   • assumption
   • example
   • counterexample
   • criterion
   • unresolved ambiguity

Prefer one high-yield question at a time. Multiple choice is useful when options are real; forced choice is harmful when it hides the likely fifth answer. Always allow “other / depends — explain.”

Ask questions like:

• “In this exact case, which outcome is correct?”
• “Is this inside or outside the commitment?”
• “Would this count as a bug?”
• “Which option should be rejected?”
• “Does this example witness the rule, contradict it, or sit outside scope?”

Include your recommended answer when you have enough context, and explain why.

Use behavioral kernels as hidden interviewer machinery. Activate at most the top 2–3 relevant kernels:

Kernel	Looks for	Typical artifact
Identity & reference	ids, references, links, uniqueness	entity / reference invariant
Containment & topology	parent/child, folders, ordering, graphs	membership / topology invariant
Validation & normalization	valid/invalid input, canonical forms	parser or validation contract
State & lifecycle	states, transitions, terminal states	state-machine invariant
Temporal history	undo, redo, audit, expiration	history / timeline invariant
Optimization & preference	best, preferred, tie-breaks	ranking or objective rule
Authority & capability	roles, permissions, delegation	authorization predicate
Concurrency & collaboration	offline, stale, conflict, merge	conflict-resolution semantics
Transactions & atomicity	all-or-nothing multi-object updates	transaction invariant
Resource accounting	balances, quotas, capacity, limits	conservation / bounds invariant
Derived data & views	counts, filters, projections, caches	view consistency invariant
Error & recovery	retry, rollback, compensation	failure / recovery contract
External effects	APIs, queues, webhooks, clocks	boundary / adapter contract
Change & migration	legacy, compatibility, upgrade	migration / refinement invariant
Observability & evidence	logs, traces, explanations, audit	trace / provenance invariant

Example:

A project is deleted while it still has tasks. Which behavior is correct?

A. Delete the tasks too. B. Archive the tasks and keep them readable. C. Move tasks to an unassigned pool. D. Block deletion until tasks are reassigned or deleted. E. Other / depends.

My recommendation is B if historical traceability matters more than cleanup, because it preserves references and gives us a clear data-integrity invariant.

Translate the answer:

• decision: “Deleted projects archive their tasks rather than deleting or reassigning them.”
• invariant: “Archived tasks retain a tombstone reference to the deleted project.”
• positive example: “Deleting a project with open tasks makes those tasks archived and readable.”
• counterexample: “Tasks silently disappearing after project deletion is rejected.”
• criterion: “A deletion test verifies task archival and readable tombstone references.”

Stop when the ambiguity is collapsed, explicitly deferred, or ready for ln-spec.

Do not create or edit planning artifacts here. Durable conclusions promote into memory/SPEC.md or memory/PLAN.md through the next routed skill.

Routing

When the ambiguity pass is complete, present these options to the user. If tool-ask-question is available, use it; otherwise use a numbered list.

#	Label	Target	Why
1	Write/update spec	ln-spec	Durable conclusions should enter memory/SPEC.md
2	Plan frontier	ln-plan	The meaning is clear but work needs sequencing
3	Scope one slice	ln-scope	One implementation slice is now obvious
4	Grill further	ln-grill	The ambiguity pass exposed broader design uncertainty

Recommended: choose ln-spec when decisions, invariants, assumptions, lexicon, examples, or criteria changed.