@rwb3n/checkpoints

---
template: checkpoint
title: "Session 31: Memory Agent Skill & Search Fix"
version: 1.0.0
author: Hephaestus (Builder)
date: 2025-12-05
project_phase: "Phase 11: ReasoningBank Prototype"
status: complete
references:
  - "@.claude/skills/memory-agent/SKILL.md"
  - "@haios_etl/database.py"
  - "@haios_etl/retrieval.py"
---
# generated: 2025-12-05
# System Auto: last updated on: 2025-12-05 21:37:15
# Session 31: Memory Agent Skill & Search Fix
## Date: 2025-12-05 | Agent: Hephaestus (Builder)

---

## Quick Reference

### Identity
- **Agent:** Hephaestus (Builder)
- **Mission:** Agent Memory ETL Pipeline
- **Spec:** @docs/specs/TRD-ETL-v2.md
- **Schema:** @docs/specs/memory_db_schema_v3.sql (AUTHORITATIVE)

### Status
| Component | Status | Details |
|-----------|--------|---------|
| Memory Agent Skill | CREATED | .claude/skills/memory-agent/SKILL.md |
| Search Fix | IMPLEMENTED | Awaiting MCP server restart |
| Embeddings | 96.6% (60,279/62,428) | New docs added |

---

## Completed This Session

### 1. Dogfood Complete
Ingested updated docs into memory:
- Concepts: 60,446 -> 62,428 (+1,982)
- Embeddings: 59,707 -> 60,279 (+572)
- Coverage: 96.6% (new concepts pending embedding)

### 2. Memory Agent Skill Created
Created `.claude/skills/memory-agent/SKILL.md` - a Claude Code Skill that:
- Implements the ReasoningBank CLOSED loop pattern
- Is model-invoked (Claude decides when to use it)
- Instructs on RETRIEVE -> INJECT -> EXECUTE -> EXTRACT flow

Key innovation: Using Claude Code's native Skills system instead of building a wrapper.

### 3. Critical Bug Fixes

#### Fix A: Similarity Threshold Too Strict
**File:** `haios_etl/retrieval.py:160`
**Change:** Threshold 0.8 -> 0.6
**Reason:** Per Session 30 recommendation, 0.8 was too strict for experiential learning

#### Fix B: Search Missing 99% of Content
**File:** `haios_etl/database.py:285-351`
**Problem:** `search_memories()` only queried 572 artifact embeddings, ignoring 59,707 concept embeddings
**Fix:** Added UNION ALL to search both artifact AND concept embeddings

```sql
-- Now searches BOTH (was only artifacts)
SELECT ... FROM embeddings e JOIN artifacts a ...
UNION ALL
SELECT ... FROM embeddings e JOIN concepts c ...
```

**Verification:** Direct Python test confirms fix works. MCP server requires restart for changes to take effect.

---

## Key Discovery: Search Was Broken

The memory search was essentially non-functional because:
1. Artifacts = 572 file-level entries
2. Concepts = 62,428 content-level entries (where knowledge lives)
3. Search only looked at artifacts (0.9% of content)

This explains why `memory_search_with_experience` always returned empty results despite having 60k+ embeddings.

---

## Architecture Insight: Skills vs Plugins

| Feature | Slash Command | Skill | Plugin |
|---------|---------------|-------|--------|
| Invocation | User types `/cmd` | Claude decides | User installs |
| Location | .claude/commands/ | .claude/skills/ | marketplace |
| Best for | Explicit workflows | Context-aware help | Distribution |

Memory Agent is a Skill because Claude should autonomously decide when to retrieve context or extract learnings.

---

## Pending

1. **MCP Server Restart** - Required for search fixes to take effect
2. **Remaining Embeddings** - 2,149 concepts need embeddings (96.6% -> 100%)
3. **Test Memory Search** - Verify fix works after restart

---

## Handoff Notes

To verify fixes after MCP restart:
```
memory_search_with_experience(query="HAIOS architecture", space_id="dev_copilot")
```
Should return concept results, not empty.

---

**HANDOFF STATUS: Prototype complete, awaiting MCP restart for verification**