* feat(phase5): explainable memory operations (v3.10)

Feature 2 of the Phase 5 split (umbrella #129): every retrieval can say
why, and any warm memory can be asked where it stands.

- query(): opt-in explain=true attaches ExplanationFactor[] per result
  (rank_score, epistemic_status weight, search_mode, temporal_decay)
- explainMemory() + GET /memory/:id/explain?warm_id= reports scores,
  access patterns, and standing against the sleep-cycle score
  thresholds. Flags are scoped honestly (would_evict_by_threshold,
  would_flag_low_confidence): capacity eviction and the outcome /
  contradiction revision channels depend on live cross-table state and
  are deliberately not predicted here.
- memforge_explain MCP tool wired to the real client method; TS +
  Python SDKs including both resilient wrappers; OpenAPI entries
- query cache key extracted to queryKey() in cache.ts so the explain
  flag's participation in the key is unit-testable without Redis
- warm_id validated to int8 range at REST and MCP boundaries; invalid
  agent ids on /explain return 400 like sibling routes

No schema migration — pure code feature.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011xCqQo49d3CEbn6oEvb3Ru

* feat(phase5): causal memory graph (v3.10)

Feature 3 of the Phase 5 split (umbrella #129): MemForge learns which
events tend to follow which, and can answer "what led here" and "what
happens next".

- causal_edges table (migration-v3.10.sql + canonical schema, RLS,
  agent-scoped FKs to warm_tier with CASCADE)
- Sleep Phase 6.1 mines memory_sequences for repeated content-level
  A→B patterns (md5 of 50-char prefixes, the Phase 5.5 technique) —
  the umbrella's row-pair grouping was unsatisfiable under the table's
  UNIQUE constraint and could never mine an edge. One edge per
  pattern, anchored to the earliest surviving pair so re-mining
  updates in place and FK cascade self-heals evicted anchors. Not
  skip-gated: the phase's input accumulates independently of its own
  change history, so the zero-change gate would permanently disable
  mining for young agents. Count surfaces as
  SleepCycleResult.causal_edges_updated.
- getCausalChain(): recursive CTE traversal (causes|effects, depth
  clamped 1-10, 50-row cap, ordered depth then strength)
- predict(): context match → outgoing edges → ranked events.
  probability = confidence × (1 − e^(−strength/30)) — monotonic,
  never saturated (raw strength × confidence clamps to 1.0 for
  virtually every mined edge). Shared-pool matches are excluded from
  the cause-id list (different id space than warm_tier), and effects
  are confined to the requested namespace.
- GET /memory/:id/causal + POST /memory/:id/predict, memforge_causal_chain
  + memforge_predict MCP tools wired to real client methods, TS +
  Python SDKs incl. resilient wrappers, tool-definitions, OpenAPI
- tests/causal-graph.test.ts: 39 tests (traversal incl. cycles/clamps/
  cap/ordering, predict incl. namespace + pool-collision, Phase 6.1
  mining incl. cv weighting + anchor stability + true re-confirmation
  step, pruning, HTTP status paths, migration shape + RLS)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011xCqQo49d3CEbn6oEvb3Ru

* feat(phase5): hierarchical abstraction engine (v3.11)

Feature 4 of the Phase 5 split (umbrella #129): Sleep Phase 5.11
distills cross-cutting principles from accumulated meta-reflections
into a queryable abstractions store.

- abstractions table (migration-v3.11.sql + canonical schema): level
  CHECK (principle|strategy|mental_model), stored md5 content_hash
  with UNIQUE (agent_id, level, namespace, content_hash) — the
  umbrella's ON CONFLICT DO NOTHING had no conflict target and would
  have duplicated every principle every cycle. RLS + grants.
- Phase 5.11: gated on LLM presence, token budget, and >= 3
  meta-reflections; prompt carries the codebase-standard injection
  guard, wraps reflection content via wrapUserContent, and states the
  schema's own limits (max 10, under 2000 chars, empty-array escape)
  so one overlong item does not sink the batch silently. Re-derivation
  is corroboration: ON CONFLICT keeps GREATEST confidence and revives
  a deactivated principle only when the fresh derivation is confident
  (>= 0.5); the conditional WHERE keeps change counts honest. Age-based
  deactivation of stale sub-0.2 principles. Not skip-gated (input
  accumulates independently). Cancellation checkpoint before the LLM
  call so canceled dream runs stop paying. runPhase now records real
  per-phase token spend in sleep_phase_analytics; count surfaces as
  SleepCycleResult.principles_extracted.
- getAbstractions()/getPrinciples(), GET /principles + /abstractions
  routes, memforge_principles + memforge_mental_models MCP tools wired
  to real client methods (honest description: only 'principle' is
  auto-extracted today), TS + Python SDKs incl. resilient wrappers,
  tool-definitions, OpenAPI with shared Abstraction component.
- tests/abstractions.test.ts: 39 tests — read paths, extraction incl.
  dedup/revival/confidence-raise/skip-gate immunity/token attribution/
  budget + window gates/malformed + schema-invalid + missing-key LLM
  output, deactivation age and scope boundaries, ordering tiebreak,
  HTTP status paths, migration shape + RLS.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011xCqQo49d3CEbn6oEvb3Ru

* feat(phase5): cross-agent transfer learning bootstrap (v3.12)

Feature 7 of the Phase 5 split (umbrella #129), the final feature:
bootstrap a new agent from an experienced one.

- bootstrapAgent(): one atomic transaction copying established warm
  memories (as epistemic 'inferred', 0.5x importance/confidence,
  _transferred_from provenance), active procedures, and active
  principles from source to target. Set-based INSERT..SELECT with
  dedup BEFORE each LIMIT so already-carried knowledge frees its cap
  slot (the umbrella looped N+1 with no-op ON CONFLICTs — procedures
  has no unique constraint at all). Memory dedup is namespace-scoped
  and content-exact (organic rows hash content differently, so
  equality is the only honest check). Warm-tier creates carry
  hash-chained audit records ('bootstrap' trigger) like consolidation
  and cold-restore do.
- Phase 5.12 promotion now also considers 'inferred' rows meeting the
  same evidence bar — bootstrap is the first mass writer of inferred
  status, which was previously terminal: excluded by every epistemic
  filter except 'all' with no path out regardless of corroboration.
- POST /memory/:id/bootstrap (path agent = target; Zod-validated body
  incl. agent-id regex and 0-1000/0-100 caps), memforge_bootstrap MCP
  tool with its own agent-pair validation and a real executor, TS +
  Python SDKs incl. resilient wrappers, tool-definitions, OpenAPI.
  Docs state honestly: principles carry no provenance marker (no
  metadata column); transferred rows are FTS-searchable immediately,
  semantic after embedding backfill, filter-visible after promotion.
- No schema migration (audit trigger comment updated in schema.sql).
- tests/bootstrap.test.ts: 22 tests — transfer semantics per category,
  discounts + provenance, namespace scoping incl. the cross-namespace
  dedup regression, per-category caps incl. principle slot-starvation
  regression, idempotency, organic-content dedup, transaction rollback
  via sabotage trigger, inferred->established promotion, auto-register,
  HTTP status paths.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011xCqQo49d3CEbn6oEvb3Ru

* feat(phase5): contested status for close-call conflict resolution (#166)

The final Phase 5 slice — the Feature 1 remnant that never landed with
PR #137. Phase 2.5 conflict resolution stops encoding noise as fact.

- When the multi-factor score gap between two conflicting live memories
  is at most one point (one factor's worth of noise on the quantized
  integer scale), both are marked epistemic_status='contested' and the
  conflict resolves with a contested(...) strategy and no winner. The
  umbrella used a relative 20% threshold, which inverts on small
  integer scores (pure-noise 1-vs-0 reads as decisive, well-evidenced
  6-vs-5 as contested) and is distorted by the mutual +100 supersession
  offset — the absolute delta is immune to both.
- Superseded rows never contest: supersession is an absolute signal,
  and marking stale facts contested would resurface them as live
  disputes.
- Contested is not terminal (the trap this same stack fixed for
  'inferred'): Phase 5.12 promotes contested rows that earn the
  corroboration bar, and a decisive re-resolution of the dispute clears
  the winner's badge back to provisional.
- tests/contested-conflicts.test.ts: 9 tests — close-call semantics,
  the exact one-vs-two-point boundary, winner/loser path preservation
  incl. untouched winner confidence, both-superseded decisiveness,
  contested recovery via decisive re-resolution. Conflict pairs share
  an identical relative timestamp (separate INSERTs see different
  now(), silently handing one side the +3 recency factor).
- tests/epistemic-confidence.test.ts: the promotion tests now pin the
  new behavior (corroborated contested rows promote; uncorroborated
  ones stay).


Claude-Session: https://claude.ai/code/session_011xCqQo49d3CEbn6oEvb3Ru

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>