…-agnostic)

Two CPU-agnostic speed levers on top of BonsaiIngestor. Both produce
measurable wall-clock wins without any machine-specific tuning.

1. Compact-output mode (compact=True)
   LLM emits ~30 tokens of ENTS/BELIEFS/RETRACTS instead of ~150 tokens
   of full DSL. Python synthesizes the DSL deterministically.

   - New skill: tools/skills/graphstore-bonsai-dsl-compact/SKILL.md
     (~620 source tokens, clear rules + negative examples so model does
     NOT promote third-person observations to first-person beliefs).
   - Parser: _parse_compact_output(cleaned) -> CompactTurn
   - Templates: _synthesize_dsl(turn, msg_id=..., session_id=..., role=..., text=...)
     Deterministic DSL builder. Same input always produces same output.

   Wins (4B TQ1_0, CPU only):
     warm avg:  3.9s  ->  1.7s   (2.3x faster)
     cold:     19.6s  -> 10.1s   (1.9x faster)
     5-msg:    35.3s  -> 16.9s   (2.1x faster)
     raw out:  335B   ->  98B    (3.4x smaller)

   Quality on T1-T5 smoke: all 5 messages ingested, 1 correct belief
   (fact:favorite_color=green), zero spurious beliefs, fact_id reuse
   working across the correction test.

   API shape:
     ing = BonsaiIngestor(model_path=..., compact=True, kv_cache_path=...)
     ing.ingest(text, msg_id="m:s1:0", session_id="s1")   # msg_id required
     # compact=False keeps the full-DSL path unchanged (backward compat)

2. Persistent KV cache (kv_cache_path=...)
   llama.cpp's save_state/load_state pickled to disk. Eliminates the
   ~10s cold penalty on every process restart (serverless, CLI
   one-shots, dev iteration).

   Workflow:
     run 1:  ing = BonsaiIngestor(..., kv_cache_path="/tmp/bonsai_kv.bin")
             ing.warmup()
             ing.save_kv_cache()
     run 2:  ing = BonsaiIngestor(..., kv_cache_path="/tmp/bonsai_kv.bin")
             ing.ingest("...")    # <- 2.0s instead of ~10s cold

   Safety: the cache file stores a meta dict alongside the state -
   skill fingerprint, model path+size, n_ctx, chat_format. On load,
   stale meta (different skill, different model, different context
   size) is rejected and the process warms fresh. Corrupt pickle or
   wrong-shape payloads also silently fall back to fresh warm.

   Disk cost: ~406 MB for n_ctx=2048 on 4B TQ1_0. Opt-in; no-op when
   kv_cache_path is not set.

Skipped: `-march=native` llama.cpp rebuild. That would only optimize
this machine and could ship binaries that crash on CPUs without the
same ISA. Kept portable instead.

Tests: +23 unit tests (48 total on bonsai_ingestor)
  - compact parser: all-sections / none / missing / case-insensitive /
    fence tolerance / escaped quotes / unknown-prefix ignore
  - dsl synthesis: min-turn / entities + matching edges / dedupe /
    belief + retract pair / quote escaping / overall kind order
  - KV cache: no-op without path / no-op without llm / missing file /
    stale meta rejection / corrupt pickle / wrong shape / meta shape

Full suite: 1850 passed, 101 skipped.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>