Implement BDEF v1.1 grading: scoring core, per-deck pipeline, ledger, dashboard, StartOS layer

- Deterministic scoring.py (quant 60 / qual 40 / flags -15, profitability heaviest)
- Per-company JSON ledger with forecast-target chaining deck N-1 -> N
- Single-shot sandbox agent with guided-JSON fallback ladder (no tool loop)
- Portfolio dashboard with sparklines, KPI hit rates, BDEF category bars
- 48 unit tests green; endpoints smoke-tested; npm check+build green

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

startos/actions/configure-models.ts

@@ -9,7 +9,7 @@ const inputSpec = InputSpec.of({
       {
         name: 'Model Catalog',
         description:
-          'The local models this service can serve on your Sparks. Each reviewer ' +
+          'The local models this service can serve on your Sparks. Each grader ' +
           'references one of these by its alias. The job runner loads models in ' +
           'waves so you can run a panel across more models than fit in GPU memory ' +
           'at once.',
@@ -23,10 +23,10 @@ const inputSpec = InputSpec.of({
         spec: InputSpec.of({
           alias: Value.text({
             name: 'Alias',
-            description: 'Short name reviewers use to pick this model (e.g. "qwen-32b").',
+            description: 'Short name graders use to pick this model (e.g. "qwen-32b").',
             required: true,
             default: null,
-            placeholder: 'reviewer-a',
+            placeholder: 'grader-a',
             patterns: [
               { regex: '^[a-z0-9][a-z0-9-]{0,30}$',
                 description: 'Lowercase letters, numbers, dashes (max 31 chars).' },
@@ -42,7 +42,7 @@ const inputSpec = InputSpec.of({
           spark: Value.select({
             name: 'Served On',
             description:
-              'Which Spark serves this model. Air-gapped review mode requires the ' +
+              'Which Spark serves this model. Air-gapped grading mode requires the ' +
               'head (primary) Spark; the secondary is used only in local-services mode.',
             default: 'primary',
             values: { primary: 'Primary (head) Spark', secondary: 'Secondary Spark' },
@@ -68,7 +68,7 @@ const inputSpec = InputSpec.of({
   }),
   maxModelLen: Value.number({
     name: 'Max Model Length',
-    description: 'vLLM --max-model-len (context window). Documents are chunked to fit.',
+    description: 'vLLM --max-model-len (context window). Deck text is chunked to fit.',
     required: true,
     default: 32768,
     integer: true,
@@ -77,7 +77,7 @@ const inputSpec = InputSpec.of({
   toolCallParser: Value.text({
     name: 'Tool-Call Parser',
     description:
-      'vLLM tool-call parser for the reviewer\'s read-file tool loop. Match the ' +
+      'vLLM tool-call parser for the grader\'s read-file tool loop. Match the ' +
       'served model family (Qwen3 → "hermes"). Empty disables native tool-calling.',
     required: false,
     default: 'hermes',
@@ -112,7 +112,7 @@ export const configureModels = sdk.Action.withInput(
     description: 'Define the local model catalog served on your Sparks and the serving knobs.',
     warning: null,
     allowedStatuses: 'any',
-    group: null,
+    group: 'Setup',
     visibility: 'enabled',
   }),
 
@@ -146,7 +146,7 @@ export const configureModels = sdk.Action.withInput(
       title: 'Models Configured',
       message:
         'Saved ' + input.models.length + ' model(s). Make sure each is present in ' +
-        'the Spark HF cache for air-gapped runs, then set "Configure Reviewers".',
+        'the Spark HF cache for air-gapped runs, then set "Configure Graders".',
       result: {
         type: 'single',
         value: input.models.map((m) => m.alias).join(', '),