save-memories

Save Memories to cc-memory Database

Review the current conversation and persist structured memories through the anti-patch writer (llm.memory_writer.upsert_smart). Never bypass this path: it auto-decides whether to MERGE (overwrite an existing high-similarity memory), SUPERSEDE (archive an old version and insert a refined one with a chain link), or INSERT (genuinely new fact). This prevents stacked duplicates.

Step 1 — Review the conversation

Identify, since the last save or session start:

• Decisions made (architecture choices, parameter selections, tradeoffs)
• Results (measurements, benchmarks with specific numbers + units)
• Bugs found and fixed (root cause + fix; especially "NEVER do X" warnings)
• Config changes (hyperparameters, env vars, settings that were tuned)
• Architecture insights (module structure, pipeline design, data flow)
• Tasks still pending or blocked

Step 2 — Score each candidate

• category: one of decision, result, config, bug, task, arch, note
• importance: 1-5 (5=critical/never-forget, 4=important, 3=useful, 2=minor, 1=skip)
• content: one self-contained sentence with specific values (numbers, file paths, parameter names). Bad: "tuned the learning rate". Good: "lr=3e-4 picked over 1e-3 because val_loss flatlined after epoch 8."
• topic: a short lowercase keyword for grouping (e.g. auth, pipeline, ui)

Step 3 — Quality bar

• Only conclusions, not the discussion process
• Each memory must be understandable WITHOUT the conversation
• 5-15 memories per call (quality > quantity)
• Skip: tool errors, navigation, meta-discussion, conversation logistics
• Do NOT save memories ABOUT the memory plugin itself unless it's a critical bug

Step 4 — Run this exact command

The writer handles dedup, similarity-based reconcile, and MEMORY.md regen automatically. Do not call db.insert_memory directly.

python3 -c "
import sys
from pathlib import Path

PLUGIN = Path.home() / '.claude' / 'hooks' / 'cc-memory'
sys.path.insert(0, str(PLUGIN / 'cc_memory'))

from core.db import MemoryDB
from llm.memory_writer import upsert_batch

project = str(Path('.').resolve())
db = MemoryDB(Path(project) / 'memory' / 'memory.db')
pid = db.upsert_project(project)

memories = [
    # {'category': 'decision', 'content': '...', 'importance': 4, 'topic': 'auth'},
    # ADD MEMORIES HERE — see Step 2 for fields
]

counts = upsert_batch(db, pid, None, memories, memory_dir=Path(project) / 'memory')
print(f\"inserted={counts.get('inserted',0)} \"
      f\"merged={counts.get('merged',0)} \"
      f\"superseded={counts.get('superseded',0)} \"
      f\"skipped={counts.get('skipped',0)}\")
"

Step 5 — Report results

Tell the user the breakdown:

• Inserted: brand-new facts
• Merged: refined an existing high-similarity memory in place
• Superseded: replaced an older version of the same fact (preserved as chain)
• Skipped: exact duplicates already present

The merged/superseded counts are good: they mean the writer is preventing the patch-style stacking the v2.1 anti-patch contract was designed to stop. See docs/MEMORY_RULES.md for the full contract.