Memory-librarian skill + stuck detector hook for Claude Code integration

[harreh3iesh]

Hey Mila love what you built with mempalace. The palace metaphor is brilliant and the 96.6% LongMemEval score is no joke.

I built engram, a set of hooks and tools that give AI coding assistants human-like memory. It's backend-agnostic and already has first-class mempalace support. Thought some of these patterns might be useful for the project:

What engram does that could help mempalace users

1. Stuck detector (graph_stuck_detector.sh)
A Claude Code PreToolUse hook that counts Grep/Glob calls and escalates. After 5 searches it says "stop grepping, use mempalace search." It already detects .mempalace/ and suggests the right command. Saves tokens and teaches the AI to reach for memory instead of brute-forcing.

2. Context watchdog (context_watchdog.py)
Audits graph staleness, ghost nodes (dead references), and token budget. Could adapt to audit mempalace's ChromaDB state — you claim ~170 tokens at wake-up, this tool could verify that.

3. Memory-librarian skill (memory-librarian.md)
A graduated retrieval protocol (5 levels from 0 tokens to unbounded). The idea: don't dump everything into context. Try mempalace search first (~500 tokens). Only escalate to reading source files if memory doesn't have the answer. Like how humans actually remember things.

What I'd contribute

Happy to open a PR adding:

• The stuck detector as a .agents/plugins/ hook (already works, just needs mempalace-specific paths)
• The graduated retrieval skill adapted for mempalace's wing/room/hall metaphor
• Ghost node detection for the temporal knowledge graph

All stdlib Python, no new dependencies. 49 tests passing.

FROM ONE STRONG WOMAN TO ANOTHER WITH LOVE <3.
Thank you for open-sourcing this. The community needs more tools like mempalace.

— h3

[web3guru888]

The stuck detector concept is interesting — tracking Grep/Glob call counts as a search frustration signal and switching to semantic queries is a real pattern. We've seen similar behavior in our agents: repeated grep attempts on the same directory often mean the index needs a fresh mine pass, not more searching.

The memory-librarian skill approach (dedicated context prepend from palace on session start) maps directly to how we use the status and search tools in our own pipeline. One thing we'd add: the librarian query should be scoped to the active wing, not the full palace. Full-palace prefill on a 200+ drawer palace adds noise, especially when agents are working in a single domain.

One question about the engram integration: when you do the pre-session palace query, are you using the raw drawer content or running it through AAAK compression first? We've found that AAAK-compressed content in the context window is more useful for agents because it preserves relationships that a direct chunk might miss — but it does rely on AAAK expand working correctly at search time.