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Lens packs (v0.41.2.0)

Four bundled schema packs that turn the modusbrain dream cycle into a multi-lens brain. Activate one with modusbrain config set schema_pack <name> and the cycle picks up the pack’s declared phases on the next modusbrain dream run.

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The four packs

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modusbrain-creator

Atom + concept content-creator lifecycle. Drives two cycle phases:
  • extract_atoms — per source, Haiku extracts 1-3 atoms from each transcript with the closed 11-value atom_type enum (insight, anecdote, quote, framework, statistic, story_angle, strategy_angle, strategy, endorsement, critique, collection). Writes atoms/{YYYY-MM-DD}/{slug} pages. Budget cap $0.30/source/run.
  • synthesize_concepts — globally aggregates atoms by frontmatter concepts: ref. Tier by count: T1 ≥10, T2 ≥5, T3 ≥2. T1/T2 get Sonnet narratives; T3 falls back to a deterministic stub. Writes concepts/{slug} pages. Budget cap $1.50/run.
One calibration domain: concept_themes / cluster_summary / [concept] — tier histogram + page count, not Brier (concepts don’t have binary outcomes to score against).

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modusbrain-investor

YC / investor lens. Declares 2 net-new page types on top of gbrain-base’s deal/person/company/yc seed:
  • thesis (NEW) — investment thesis with thesis_text + key_bets[] + market_view + vintage. Files at investing/theses/{slug}. Extractable (the LLM mines claims into facts).
  • bet_resolution_log (NEW) — outcome record for a thesis’s bet. FK to a take row via take_id; carries resolved_outcome + resolved_at + learned_pattern. Files at investing/bets/{YYYY-MM}/{slug}.
No new cycle phases — consumes the existing extract_facts/propose_takes/grade_takes/calibration_profile loop. Three calibration domains: deal_success (scalar_brier over deal-attached takes), founder_evaluation (scalar_brier over person-attached takes), market_call (weighted_brier over thesis-attached takes; weighted by conviction so high-stakes misses cost more).

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modusbrain-engineer

Bridge-only pack. Declares learning page type + reuses base code. No new cycle phases — the daemon-side gstack-learnings IngestionSource (T8) watches ~/.gstack/projects/{repo}/learnings.jsonl and emits each JSONL line as a learning page when this pack is active. Three calibration domains: architecture_calls (scalar_brier), effort_estimates (weighted_brier), risk_assessment (scalar_brier). Speculative ADR/postmortem/refactor_thesis/tech_debt types deferred to v0.42+ — they’ll ship when a real user authors the first one (D8).

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modusbrain-everything

Meta-pack stacking creator + investor + engineer via the v0.38 extends + borrow_from chain. Single-active-pack constraint preserved — this IS the active pack; the registry walks extends + borrow to materialize the merged view. Activate via modusbrain config set schema_pack modusbrain-everything and calibration_profile produces all 7 domain scorecards in one JSONB.

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Calibration profile widening (T10)

Before v0.41.2.0, calibration_profiles.domain_scorecards was a JSON.stringify({}) placeholder. v0.41.2.0 widens it: each declared domain produces a {n, brier, accuracy, aggregator, page_types, extras} entry. Four aggregator algorithms (closed enum):
  • scalar_brier — AVG(POWER(weight - outcome::int, 2)). Default for probabilistic predictions.
  • weighted_brier — Brier weighted by ABS(weight - 0.5) * 2 (conviction proxy). High-conviction misses cost more.
  • count_based — simple SUM(hit) / COUNT(*) accuracy without Brier. Use when probability isn’t natural.
  • cluster_summary — descriptive rollup (page count + tier histogram). For domains like concept_themes where there’s no binary outcome.
Pack manifests declare domains with {name, aggregator, page_types}. Domain names are OPEN (third-party packs can declare new domain labels without a modusbrain release). Aggregator algorithms are CLOSED (safe SQL stays in code, validated at pack-load).

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take_domain_assignments table (T1)

New JOIN table (migration v94): take_domain_assignments(take_id BIGINT FK, domain TEXT, pack TEXT, source TEXT, confidence REAL, assigned_at TIMESTAMPTZ, PK(take_id, domain)). Multi-domain assignment honest — a take about “Sequoia’s investment in Anthropic” can land in BOTH deal_success AND market_call rather than being force-bucketed.

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What this enables for the user

  • Atoms + concepts ship in the binary. Your OpenClaw’s parallel atom-pipeline-coordinator + atom-backfill-coordinator + concept- synthesis crons can retire (T12 follow-up). One modusbrain dream cron covers everything.
  • gstack learnings reach modusbrain. Engineer-pack-active brains surface every gstack-logged learning as a queryable page within seconds of being written.
  • Multi-lens calibration. Activate modusbrain-everything and see how often you’re wrong on deals AND market calls AND architecture AND effort estimates in one modusbrain calibration --json call.
  • Lossless OpenClaw migration. The markdown-greenfield importer (T7, mode=‘migration’) re-ingests existing OpenClaw pages with permanent slug-keyed idempotency + per-row JSONL audit
    • the imported_from marker so extract_atoms + synthesize_concepts don’t re-extract already-atomized material.

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v0.41.2.1 follow-ups (filed in plan)

  • Per-page-type frontmatter_validators on PageTypeSchema so the atom_type enum (currently hardcoded in extract_atoms.ts) reads from the active pack manifest at runtime per D11.
  • 3-check quality gate (truism / punchline / entity-page reject) as a multi-pass extract_atoms refinement.
  • Embedding-similarity dedup in synthesize_concepts (currently exact-string concept ref match only).
  • Voice gate integration for T1 Canon narratives.
  • op_checkpoint resumability for cross-cycle continuation in both phases.
  • Parity-baseline eval gates against your OpenClaw’s existing 13K atoms
    • 11K concepts on a 500-page sample subset.