echo

🔊 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 "$MEMSTACK_PATH/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 "$MEMSTACK_PATH/db/memstack-db.py" search "<keywords>" --project <project>

Step 3: Recent Sessions and Insights

For additional context:

python "$MEMSTACK_PATH/db/memstack-db.py" get-sessions <project> --limit 5
python "$MEMSTACK_PATH/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 "$MEMSTACK_PATH/skills/echo/index-sessions.py

Use --force to re-embed all content (e.g., after changing embedding model):

python "$MEMSTACK_PATH/skills/echo/index-sessions.py --force

Inputs

• Keywords from the user's prompt (project name, feature name, date range)
• Vector DB: $MEMSTACK_PATH/memory\vectors\lancedb\ (via LanceDB)
• Database: $MEMSTACK_PATH/db\memstack.db (via memstack-db.py)
• Fallback: $MEMSTACK_PATH/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-search slash 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)