vbrevik 🔊 Echo — Searching the Archives...
Recall information from past CC sessions using semantic vector search.
Activation
When this skill activates, output:
🔊 Echo — Searching the archives...
Then execute the protocol below.
Context Guard
| Context | Status | Priority |
|---|---|---|
| User says "recall", "remember", "last session", "what did we" | ACTIVE — search memory | P1 |
| User asks about past work explicitly ("did we build X?") | ACTIVE — search memory | P1 |
| User says "continue from" or "resume" a past topic | ACTIVE — search memory | P2 |
| User is describing NEW work to do ("build X", "add Y") | DORMANT — this is new work, not recall | — |
| User mentions "memory" in code context (RAM, variables) | DORMANT — technical term, not MemStack recall | — |
| User mentions a project name in present tense ("work on X") | DORMANT — forward-looking, not recall | — |
| User says "save" or "log" (Diary/Project territory) | DORMANT — Diary or Project skill handles writing | — |
Anti-Rationalization
If you're thinking any of these, STOP — you're about to skip the protocol:
| You're thinking... | Reality |
|---|---|
| "I remember this from earlier in the conversation" | You don't persist. Earlier context may be compacted. Run the search. |
| "I can just summarize from what I know" | You know nothing from prior sessions. The database does. Search it. |
| "The user probably doesn't need exact details" | Users ask Echo for specifics — dates, decisions, file paths. Run all steps. |
| "Vector search seems slow, I'll skip to SQLite" | Vector search returns the best results. Always try it first. |
| "I found one result, that's probably enough" | Run ALL steps (vector + SQLite + insights). One source misses context another catches. |
| "The keywords are too vague to search" | Search anyway. Vague queries still return useful semantic matches. |
Protocol
Step 1: Semantic Vector Search (primary)
Try LanceDB vector search first for best-quality results:
python C:/Projects/memstack/skills/echo/search.py "<keywords>" --top-k 5
If this returns results, present them with scores, dates, and source files.
Step 2: SQLite Keyword Search (augment or fallback)
Always run SQLite search to supplement vector results or as fallback if Step 1 fails:
python C:/Projects/memstack/db/memstack-db.py search "<keywords>" --project <project>
Step 3: Recent Sessions and Insights
For additional context:
python C:/Projects/memstack/db/memstack-db.py get-sessions <project> --limit 5
python C:/Projects/memstack/db/memstack-db.py get-insights <project>
Step 4: Markdown Fallback
If both vector and SQLite return nothing, check memory/sessions/ and memory/projects/ for markdown files.
Step 5: Present Findings
Combine and deduplicate results from all sources:
- Vector results: Show with similarity scores and section headings
- SQLite results: Show with dates and accomplishment summaries
- Source attribution: Always show which source (vector/SQLite/markdown) each result came from
- Date and project name
- What was accomplished
- What was left pending
- Key decisions and insights
Step 6: No Results
If nothing found across all sources — say clearly: "No session logs found for [topic]. Use Diary to save future sessions."
Indexing
To re-index sessions after new diary entries (normally done automatically):
python C:/Projects/memstack/skills/echo/index-sessions.py
Use --force to re-embed all content (e.g., after changing embedding model):
python C:/Projects/memstack/skills/echo/index-sessions.py --force
Inputs
- Keywords from the user's prompt (project name, feature name, date range)
- Vector DB:
C:\Projects\memstack\memory\vectors\lancedb\(via LanceDB) - Database:
C:\Projects\memstack\db\memstack.db(via memstack-db.py) - Fallback:
C:\Projects\memstack\memory\(legacy markdown files)
Outputs
- Ranked results with semantic similarity scores
- Source type attribution (vector, database, or markdown fallback)
- Summary of relevant past session context
Example Usage
User: "Do you remember what we did on AdminStack last session?"
🔊 Echo — Searching the archives...
Vector search (top 3):
[1] AdminStack — 2026-02-18 (session)
Section: Accomplished
Score: 0.912
Built CC Monitor page with session cards, auto-refresh, notifications.
Created /api/cc-sessions CRUD + public report endpoint.
[2] AdminStack — 2026-02-17 (session)
Section: Decisions
Score: 0.847
Used SWR for auto-refresh instead of polling. API key via HMAC-SHA256.
[3] AdminStack — 2026-02-18 (session)
Section: Next Steps
Score: 0.791
Deploy dashboard, add notification preferences, test mobile view.
SQLite insights (3):
- [decision] Used SWR for auto-refresh instead of polling
- [decision] API key validation via HMAC-SHA256
- [pattern] Next.js App Router + SWR for all dashboard pages
Level History
- Lv.1 — Base: Session log search and recall. (Origin: MemStack v1.0, Feb 2026)
- Lv.2 — Enhanced: Added YAML frontmatter, context guard, activation message. (Origin: MemStack v2.0 MemoryCore merge, Feb 2026)
- Lv.3 — Advanced: SQLite backend as primary source, markdown as fallback, insight search. (Origin: MemStack v2.1 Accomplish-inspired upgrade, Feb 2026)
- Lv.4 — Native: CC rules integration (
.claude/rules/echo.md),/memstack-searchslash command, auto-indexed CLAUDE.md context. (Origin: MemStack v3.0-beta, Feb 2026) - Lv.5 — Semantic: LanceDB vector-powered recall with sentence-transformers embeddings (OpenAI optional). Auto-indexes sessions/plans, semantic similarity across all logs, SQLite fallback. (Origin: MemStack v3.1, Feb 2026)