feat(folio): drive spec.ts under --generator llm; drop spec-llm.ts

examples/folio/sanderling/spec-llm.ts

@@ -1,29 +0,0 @@
-// LLM action-backend variant of spec.ts.
-//
-// The properties and the login `setup` are reused verbatim; only the action
-// generator changes. Instead of the seeded fuzzer drawing a random candidate,
-// `llm({ model })` hands selection to a vision model: each step it sees the
-// screenshot plus the candidate list the system already enumerates and returns
-// which candidate to act on. The candidate set, the typed input values, action
-// execution, and the trace are all identical to the seeded run.
-//
-// Requirements:
-//   - OPENROUTER_API_KEY (OpenRouter) or OPENAI_API_KEY (OpenAI) in the
-//     environment; OpenRouter wins when both are set. With a plain OpenAI key,
-//     drop the vendor prefix from the model id ("gpt-5.4-nano").
-//   - A model that supports image input AND strict json_schema structured
-//     outputs. A model lacking either fails clearly.
-import { llm } from "@sanderling/spec";
-
-export { properties, setup } from "./spec";
-
-// instructions only describe WHAT the app is — its purpose and features. They
-// say nothing about HOW to test it: no bug, no technique, no "try to break it"
-// (the base prompt already carries the bug-finding goal). The model figures out
-// how to test entirely on its own. If we encoded the answer here, a "pass"
-// would prove nothing and the feature would be worse than useless.
-export const actionsRoot = llm({
-  model: "gpt-5.4-nano",
-  instructions:
-    "Folio is a personal-finance ledger app. After signing in, the home screen lists accounts, each with a balance. You can create accounts, open an account to see its ledger, and add transactions; each transaction has an amount and changes that account's balance and the overall total.",
-});

examples/folio/sanderling/spec.ts

@@ -6,6 +6,7 @@ import {
   extract,
   from,
   integers,
+  llm,
   next,
   weighted,
   whenRoute,
@@ -187,3 +188,15 @@ export const actionsRoot = weighted(
   [5, doubleTaps],
   [25, defaultActions],
 );
+
+// The LLM generator is orthogonal to actionsRoot: with `--generator llm` a model
+// picks from the SAME weighted candidate set above, reading the screenshot and a
+// numbered, weight-annotated list; the default `--generator seeded` ignores it.
+// instructions describe only WHAT the app is, never HOW to test it — the model
+// figures out how to surface bugs on its own. With a plain OpenAI key, drop the
+// vendor prefix from the model id.
+export const generator = llm({
+  model: "gpt-5.4-nano",
+  instructions:
+    "Folio is a personal-finance ledger app. After signing in, the home screen lists accounts, each with a balance. You can create accounts, open an account to see its ledger, and add transactions; each transaction has an amount and changes that account's balance and the overall total.",
+});