feat(v0.3.0): paradigm shift — TimeWeb-only LLM + persistent advisor trail + improvement queue

This is the rc.4 batch the user requested:

  1. Emergency triggers (low HP + close hostile, lava-under-foot)
     bypass the long cooldown so the LLM is consulted BEFORE the bot
     dies, not after.
  2. Active manifesto need is now included in the advisor user prompt
     — the LLM picks suggestions that satisfy the bot's current
     concrete need (L2 tools_wood → "gather logs nearby" not
     "explore further").
  3. Every advisor recommendation is persisted to SQLite
     (advisor_recommendations table) with full token usage. The
     reflex marks 'applied=1' when it dispatches and updates
     outcome_ok/code when the dispatch completes. Ground truth for
     "is the LLM actually helping" lives in the DB, not in logs.
  4. Pi CLI is OUT of every background loop. coach/postmortem and
     coach/reflect now go through the same TimeWeb endpoint
     fast-advisor uses, via the shared coach/llm-call.js helper.
     Pi is reserved for manual operator commands.
  5. The LLM (postmortem, reflect, advisor) can flag "structural
     gaps" — missing skills/features the operator should implement.
     These land in the new improvement_requests table. Dedup by
     title bumps `votes` instead of inserting duplicates so the
     queue doesn't bloat. Operator views via
     `node scripts/list-improvements.js`.
  6. A deterministic trigger-tuner runs hourly: reads 24h of
     recommendation stats, flags triggers whose success rate is
     below 25% (sample ≥ 5) or whose prompts are expensive (>1000
     input tokens) with mediocre payoff. Improvements get
     source="tuner", category="tuning". No LLM call.

New files:
  runtime/coach/llm-call.js        — askAnalytical() helper
  runtime/coach/trigger-tuner.js   — stats → improvements
  runtime/coach/trigger-tuner.test.js
  scripts/list-improvements.js     — operator CLI

Schema additions:
  advisor_recommendations: id, ts, trigger_reason, planned_skill,
    recommended_skill, action, rationale, active_need, tokens_in,
    tokens_out, latency_ms, applied, outcome_ok, outcome_code, outcome_at
  improvement_requests: id, ts, source, category, title, description,
    context, priority, status, duplicate_of, votes, implemented_at, notes

Renamed env-var consumers:
  Pi-coach drainOnce({ askPi })   → drainOnce({ askAnalyticalFn? })
  Pi-reflect runOnce({ askPi })   → runOnce({ askAnalyticalFn? })
  bot.js attachCoach/attachReflect no longer pass askPi
  attachTuner() added to bot.js spawn handler
  lessons.source 'pi-coach'   → 'timeweb-coach'
  lessons.source 'pi-reflect' → 'timeweb-reflect'

Token cost measured live:
  ~705 input + 45 output = ~750 total per advisor call
  worst case @ 6 calls/hour rate cap = ~108K tokens/day
  OpenAI gpt-5-mini reference price: ~$0.60/month

Operator usage:
  node scripts/list-improvements.js                # open queue
  node scripts/list-improvements.js --stats        # advisor performance
  node scripts/list-improvements.js --done 17 "shipped in 0.3.1"
  node scripts/list-improvements.js --reject 18 "duplicate"

Tests: 360 green (was 332, +28 new).

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

runtime/knowledge/schema.sql

@@ -179,3 +179,63 @@ CREATE TABLE IF NOT EXISTS code_changes (
   outcome         TEXT,                      -- 'applied'|'rolled_back'|'rejected'
   notes           TEXT
 );
+
+----------------------------------------------------------------------
+-- Advisor recommendations (v0.3.0+ fast LLM trail)
+-- Every time runtime/coach/advisor-trigger.js asks the fast LLM and
+-- the answer is cached on ctx, we write a row here. When the reflex
+-- consumes the recommendation and dispatches, we attach the dispatch
+-- result later via outcome_ok / outcome_code. The history is the
+-- ground truth for trigger-tuner.js stats and for the operator's
+-- "what is the LLM suggesting and is it actually helping" question.
+----------------------------------------------------------------------
+CREATE TABLE IF NOT EXISTS advisor_recommendations (
+  id                  INTEGER PRIMARY KEY AUTOINCREMENT,
+  ts                  INTEGER NOT NULL,
+  trigger_reason      TEXT NOT NULL,           -- 'wedged_*', 'repeat_*', 'preempt_retry_*', 'emergency_*'
+  planned_skill       TEXT,                    -- what manifesto/curriculum was about to dispatch
+  recommended_skill   TEXT,                    -- what the LLM said to do instead
+  action              TEXT NOT NULL,           -- 'switch_skill' | 'continue' | 'wait'
+  rationale           TEXT,
+  active_need         TEXT,                    -- 'L2 tools_wood' etc.
+  tokens_in           INTEGER,
+  tokens_out          INTEGER,
+  latency_ms          INTEGER,
+  applied             INTEGER NOT NULL DEFAULT 0,  -- 1 if reflex actually dispatched recommended_skill
+  outcome_ok          INTEGER,                  -- NULL until dispatch finishes
+  outcome_code        TEXT,
+  outcome_at          INTEGER
+);
+CREATE INDEX IF NOT EXISTS idx_advisor_ts        ON advisor_recommendations(ts);
+CREATE INDEX IF NOT EXISTS idx_advisor_trigger   ON advisor_recommendations(trigger_reason);
+CREATE INDEX IF NOT EXISTS idx_advisor_outcome   ON advisor_recommendations(outcome_ok);
+
+----------------------------------------------------------------------
+-- Improvement requests (v0.3.0+)
+-- The LLM (postmortem / reflect / advisor) can flag situations where
+-- the bot lacked the right skill or feature. Instead of trying to
+-- self-patch (which we explicitly disabled), it writes an entry here.
+-- The operator reads `scripts/list-improvements.js` and decides what
+-- to implement. Implemented entries get marked so the bot stops
+-- re-flagging the same gap.
+----------------------------------------------------------------------
+CREATE TABLE IF NOT EXISTS improvement_requests (
+  id              INTEGER PRIMARY KEY AUTOINCREMENT,
+  ts              INTEGER NOT NULL,
+  source          TEXT NOT NULL,                -- 'postmortem'|'reflect'|'advisor'|'tuner'|'manual'
+  category        TEXT,                          -- 'skill'|'tuning'|'perception'|'planning'|'social'|'other'
+  title           TEXT NOT NULL,
+  description     TEXT,
+  context         TEXT,                          -- JSON: position, snapshot tail, related lesson ids
+  priority        INTEGER NOT NULL DEFAULT 3,    -- 1..5 (1=urgent, 5=nice-to-have)
+  status          TEXT NOT NULL DEFAULT 'open',  -- 'open'|'in_progress'|'implemented'|'rejected'|'duplicate'
+  duplicate_of    INTEGER,                       -- another row id if dup
+  votes           INTEGER NOT NULL DEFAULT 1,    -- bumped each time the bot re-flags same gap
+  implemented_at  INTEGER,
+  notes           TEXT,
+  FOREIGN KEY (duplicate_of) REFERENCES improvement_requests(id)
+);
+CREATE INDEX IF NOT EXISTS idx_improvements_status   ON improvement_requests(status);
+CREATE INDEX IF NOT EXISTS idx_improvements_priority ON improvement_requests(priority);
+CREATE INDEX IF NOT EXISTS idx_improvements_source   ON improvement_requests(source);
+CREATE INDEX IF NOT EXISTS idx_improvements_ts       ON improvement_requests(ts);