Files
gbrain/test
Garry TanandClaude Opus 4.7 9f0bdca443 cycle: calibration_profile phase + shared voice gate across surfaces (T6)
The calibration narrative layer. Reads TakesScorecard, asks an LLM to
write 2-4 conversational pattern statements ("right on tactics, late on
macro by 18 months"), passes them through the voice gate, derives active
bias tags, writes the row to calibration_profiles. This is the read-side
that E1 (think anti-bias rewrite), E3 (contradictions join), E6
(dashboard), and E7 (real-time nudges) all consume.

Voice gate (D24 — single function, multiple surfaces):
  ALL five calibration UX surfaces import the same gateVoice() function
  from src/core/calibration/voice-gate.ts. Mode parameter
  ('pattern_statement' | 'nudge' | 'forecast_blurb' | 'dashboard_caption'
  | 'morning_pulse') drives surface-specific tuning via the rubric the
  gate ships to its Haiku judge. NO forked implementations — voice
  rubric drift would defeat the gate.

  Each mode's rubric explicitly forbids preachy / clinical / corporate
  voice; a structural test pins this. Anchors the cross-cutting voice
  rule from /plan-ceo-review D2-D8.

Fallback policy (D11):
  Up to 2 generation attempts (configurable). On both rejects → fall back
  to a hand-written template from src/core/calibration/templates.ts.
  Templates are intentionally short and a little "robotic" — they're the
  safety net, not the destination. voice_gate_passed=false +
  voice_gate_attempts get persisted on the calibration_profiles row so
  the operator can review the failing examples and tune the rubric over
  time. Suppressing the surface silently is NEVER an option — that's how
  voice quality silently degrades.

  parseJudgeOutput defaults to 'academic' on parse failure (NEVER passes
  pass-through) so a Haiku output garble falls through to the template
  rather than letting unverified text reach the user.

calibration_profile phase:
  Extends BaseCyclePhase. Cold-brain skip: <5 resolved takes → no row
  written, no LLM call. Otherwise: scorecard via engine.getScorecard()
  → patterns via voice-gated generator → bias tags via separate
  generator (best-effort; failure logs warning, phase continues).

  The DB INSERT lands in the v67 calibration_profiles row with
  source_id, holder, the patterns, voice gate audit fields, active bias
  tags, and grade_completion (F1 fix — partial-grade state surfaces to
  the dashboard "60% graded" badge).

  Budget gate at $0.50/cycle default (mostly Haiku). Below-budget
  before-LLM-call check returns status='warn' without writing the row.

  Per-domain scorecards are a placeholder for v0.36.0.0 ship state —
  the F12 batchGetTakesScorecards() engine method that powers per-domain
  rendering lands in Lane C alongside the CLI/MCP surface.

Architecture:
  parsePatternStatementsOutput is tolerant of LLM emitting numbered
  lists / bulleted lines despite the prompt asking for plain lines.
  Caps at 4 patterns + drops excessively long lines (>200 chars).

  parseBiasTagsOutput lowercases input + drops non-kebab-case tokens
  (defends against the LLM emitting "Over-Confident Geography" with
  spaces or capitals). Caps at 4 tags.

Tests: 43 cases across two new test files.
  voice-gate.test.ts (24): parseJudgeOutput (7), gateVoice happy path
  (3), fallback path (5), mode parity (2), templates (7).
  calibration-profile.test.ts (19): parsers (10), pickFallbackSlots
  (3), phase integration (6 — cold-brain skip, happy path, voice gate
  fallback, grade_completion plumbed through, bias-tags failure
  non-fatal, source_id scope reaches INSERT).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-17 16:19:27 -07:00
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