feat(v0.3.0): auto-trigger fast-advisor + token usage tracking

Closes the awareness → LLM → action loop that the rc.1/2/3 sequence
left as a followup. When the bot is wedged, looping, or just suffered
a preempt-then-retry, the reflex fires advise() in the background;
when the recommendation lands it overrides the next dispatch.

Async by design: advise() takes 5-15s on TimeWeb's hosted endpoint —
too slow for a synchronous reflex tick. tickAdvisor() is fire-and-
forget, the result lands on ctx.advisorRecommendation, and the *next*
tick reads and consumes it. Recommendations age out after 60s.

Components:

- runtime/coach/advisor-trigger.js — policy + async fire path
  - tickAdvisor(ctx, {plannedSkillId}) checks three triggers:
    1. wedged > 60s (no significant move)
    2. last 4+ dispatches are the same skill AND it's planned again
    3. preempt within last 30s + same skill being retried
  - 90s trigger cooldown, single-in-flight guard
  - consumeFreshRecommendation(ctx) reads/clears the cache
- runtime/reflex.js — curriculumReflex calls tickAdvisor() every tick
  and consumes a fresh recommendation BEFORE dispatching. ctx flag
  disableAdvisor=true for tests.
- runtime/bot.js — dispatchAction maintains a rolling 8-slot
  reflexCtx.recentSkillIds for the loop-detection trigger.

Token usage:

- runtime/llm/provider.js — normaliseUsage() reads OpenAI/TimeWeb-
  style {prompt_tokens, completion_tokens, total_tokens} from the
  response. Returned on every complete() result and logged at info
  level as "in=Nt/out=Mt".
- runtime/coach/fast-advisor.js — getUsageSnapshot() aggregates
  total tokens across all calls in the session.

Measured on live TimeWeb endpoint (gpt-5.4-mini agent):
  per call: ~705 input + 45 output = ~750 tokens
  rate limit: 6 calls/hour
  worst case at full budget: ~108K tokens/day
  estimated cost (OpenAI gpt-5-mini reference price): ~$0.60/month

Well within any reasonable budget — model can run hot 24/7.

Smoke verified: scripts/check-timeweb.js probe 4 produces
  trigger fired: true (wedged_90s)
  recommendation: recovery.tunnel-out
  rationale: "Stuck wedged for 90s; exploration is failing."
  latency: 5302ms

Tests: 345 green (was 332, +13 advisor-trigger).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

runtime/reflex.js

@@ -26,6 +26,7 @@ import {
 } from "./actions.js";
 import { runSkill, getSkill } from "./skills/index.js";
 import { consult as consultAdvice, reportOutcome as reportAdviceOutcome } from "./coach/advice.js";
+import { tickAdvisor, consumeFreshRecommendation } from "./coach/advisor-trigger.js";
 import { pickActiveNeed } from "./manifesto/state.js";
 import { situationHash } from "./scenario-memory.js";
 import { tickModes } from "./modes.js";
@@ -530,8 +531,25 @@ function curriculumReflex(ctx) {
 
 	// Pick what to dispatch: manifesto wins over curriculum plan because
 	// it expresses concrete needs rather than abstract "next milestone".
-	const skillId = manifestoSkillId ?? plan.skillId;
-	const skillSource = manifestoSkillId ? `manifesto:${activeNeed.need.id}` : "curriculum";
+	let skillId = manifestoSkillId ?? plan.skillId;
+	let skillSource = manifestoSkillId ? `manifesto:${activeNeed.need.id}` : "curriculum";
+
+	// v0.3.0 fast-advisor: if a fresh recommendation is sitting on ctx
+	// (the result of a previous tick's async advise() call), use it.
+	// This is the closing of the awareness → LLM → action loop.
+	if (!ctx.disableAdvisor) {
+		const rec = consumeFreshRecommendation(ctx);
+		if (rec && rec.skillId) {
+			info(REFLEX_LOG, `advisor override: ${skillId} → ${rec.skillId} (${rec.triggerReason}, ${rec.rationale?.slice(0, 60)})`);
+			skillId = rec.skillId;
+			skillSource = `advisor:${rec.triggerReason}`;
+		}
+		// Always fire-and-forget another advise() if triggers fire — the
+		// result lands on a future tick. tickAdvisor handles its own
+		// cooldown / in-flight checks so this is safe to call every tick.
+		tickAdvisor(ctx, { plannedSkillId: skillId });
+	}
+
 	const skill = getSkill(skillId);
 	if (!skill) {
 		// Suggested a skill that isn't registered yet — fall back