3.0 KiB
| name | description | user_invocable | arg_hint | builtin |
|---|---|---|---|---|
| memory-debug | Diagnose and fix Signet memory issues (daemon health, embeddings, search quality, and data integrity). | true | [symptom or query] | true |
/memory-debug
Debug the Signet memory system when recall quality is poor, memories are missing, or remember/recall commands fail.
Use this skill when the user asks things like:
- "memory is broken"
- "recall isn't finding anything"
- "remember didn't save"
- "why are results low quality?"
syntax
/memory-debug
/memory-debug recall is empty
/memory-debug embeddings failing
workflow
Run these checks in order and stop when you find the root cause.
1) verify daemon + config
signet status
curl -s http://localhost:3850/health
If daemon is down or unresponsive, restart it:
# preferred: CLI commands
signet start # start if not running
signet restart # stop + start (graceful)
# if CLI restart doesn't work, kill and restart manually
signet stop
pkill -f "signet.*daemon" # force kill if stop hangs
signet start
# if installed as a system service (launchd on macOS, systemd on Linux)
# macOS:
launchctl kickstart -k gui/$(id -u)/ai.signet.daemon
# Linux:
systemctl --user restart signet-daemon
After restart, confirm daemon is healthy:
signet status
curl -s http://localhost:3850/health
Then verify key files exist:
~/.agents/agent.yaml~/.agents/memory/memories.db
2) verify write path (remember)
signet remember "memory-debug smoke test" -t debug,smoke -w claude-code
Expected: success response with embedded: true or a clear fallback message.
If save fails, capture exact CLI error and recommend the fix (daemon restart, permissions, missing config, etc.).
3) verify read path (recall)
signet recall "memory-debug smoke test" -l 5 --json
If no results:
- retry with simpler keyword query
- check
search.min_scoreandsearch.alphain~/.agents/agent.yaml - confirm the memory was actually written in step 2
4) check embedding health
If memories save but semantic recall is weak:
signet recall "memory-debug smoke test" --json
Inspect whether scores are keyword-heavy and whether embedding appears unavailable.
Then verify embedding provider configuration in ~/.agents/agent.yaml:
embedding.providerembedding.modelembedding.dimensions
Common fixes:
- provider offline (Ollama/OpenAI unreachable)
- wrong model name
- dimensions mismatch after model change
5) advanced diagnostics
Check daemon logs for hook and memory errors:
signet logs -c hooks
signet logs -c memory
Use direct API checks for deeper issues:
curl -s "http://localhost:3850/api/memory/search?q=debug&limit=5"
curl -s http://localhost:3850/api/status
response format
When reporting back, include:
- what failed
- exact command + error
- likely root cause
- concrete fix steps
- verification command to confirm fix
Prefer minimal, reproducible checks over broad speculation.