mcp: project-plan — Vault Semantic Search
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Sources/Homelab/vault-semantic-search.md
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Sources/Homelab/vault-semantic-search.md
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---
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created: '2026-05-17'
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path: Sources/Homelab
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project: vault-semantic-search
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tags:
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- mcp
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- pgvector
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- embeddings
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- wiki
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type: project-plan
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---
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# Vault Semantic Search
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## Goal
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Add a `vault:search` MCP tool that takes a query string (plus optional
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metadata filters) and returns vault notes ranked by semantic similarity.
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The tool runs entirely against the `wiki` schema and reuses the embedding
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infrastructure built by the Postgres consolidation plan.
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Concrete use case: "search the vault for context" returns relevant notes
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without needing to remember slugs or tag names. Semantic search replaces
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the current "list everything, filter client-side" pattern.
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This plan is scoped to vault search only. A future unified memory search
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spanning vault + thoughts + reflections is anticipated via a `scope`
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parameter, but is deferred until vault search is in use and shaping
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requirements are clearer.
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## Locked Decisions
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### Tool surface
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- Name: `vault:search`
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- Read-only. No writes, no side effects.
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- Single MCP tool — no separate "by tag" or "by date" variants. All
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filtering is parameterized on this one tool.
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### Inputs
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- `query` (required, string) — natural-language search query
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- `tags` (optional, list of strings) — restrict to notes carrying any of
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these tags
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- `domain` (optional, string) — one of the schema's domain values
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(homelab, dev, venture, reference)
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- `status` (optional, list of strings) — one or more lifecycle statuses
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- `note_type` (optional, string) — restrict to project-plan,
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session-notes, entity, topic-landing, or synthesis
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- `date_range` (optional, object with `start` and `end`) — filter by
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`updated_at` timestamp range
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- `limit` (optional, int, default 10) — max results to return
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Filters compose via AND. Vector similarity ranks within the filtered
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candidate set.
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### Output shape (minimal)
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```
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[
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{
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"slug": "ob1-deployment",
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"title": "OB1 Main Deployment",
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"path": "Sources/Homelab/ob1-deployment.md",
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"score": 0.82
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},
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...
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]
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```
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Score is the cosine similarity (or pgvector distance converted to
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similarity — implementation detail). Caller uses `vault:get_project`
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to fetch full content for any result they want to read.
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Keeping content fetch separate from search keeps the search response
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cheap and lets callers decide when full body content is worth the round
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trip.
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### Embedding behavior
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- Query embedding runs **server-side** inside the vault MCP server.
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- The server uses the **same embedding model** that produced the stored
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vectors in `public.embeddings` (recorded in the `embedding_model`
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column).
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- Mismatches are not silently tolerated — if the configured query model
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doesn't match the model recorded on the rows being searched, the
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search returns an error rather than incorrect rankings.
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- Embedding model selection is inherited from the consolidation plan;
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this plan doesn't pick or change models.
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### Scope
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- Searches the `wiki` schema only. Both Sources and Wiki layers are
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searchable; filter via `note_type` if a caller wants Sources-only or
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Wiki-only.
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- Does not search `ob1.thoughts` or `ob1.reflections`. That's a separate
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tool (or this tool extended with a `scope` parameter) in a future plan.
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### Backend mechanics
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- Vector similarity query against `public.embeddings` filtered by
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`source_schema = 'wiki'`.
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- Filters applied via WHERE clauses joining `public.embeddings` to
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`wiki.notes` (or whatever the canonical wiki content table is named).
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- HNSW index on `public.embeddings.embedding` is the consolidation
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plan's responsibility; this plan reuses it.
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## Open Items
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- [ ] Final SQL query shape — JOIN pattern between `public.embeddings`
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and the wiki content table depends on exact column names from the
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consolidation plan. Resolve at implementation time.
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- [ ] How to surface "no results" vs "search failed" — empty array
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versus error response. Likely empty array for "no results" and error
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for "model mismatch / query embed failed."
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- [ ] Pagination — `limit` is in scope; offset / cursor pagination is
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not. Add later if it's needed.
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- [ ] Caching — query embedding cost is small but non-zero. No caching
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in v1. If usage patterns show repeated queries, add later.
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## Phases
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### Phase 0 — Verify dependencies
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- [ ] Consolidation plan's Phase 5 (vault indexing) is complete.
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`public.embeddings` has rows where `source_schema = 'wiki'` covering
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the full vault corpus.
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- [ ] HNSW index exists on `public.embeddings.embedding`.
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- [ ] Embedding model is documented and reachable from the vault MCP
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server's host.
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- [ ] The vault MCP user has read access to `public.embeddings`.
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### Phase 1 — Tool implementation
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- [ ] Add `search` function to the vault MCP server.
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- [ ] Wire to the configured embedding model for query embedding.
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- [ ] Build the parameterized SQL query (vector search + filters).
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- [ ] Define the response shape per the locked decisions.
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- [ ] Add input validation (filter values exist in schema, date format
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correct, etc.).
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### Phase 2 — Verification
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- [ ] Smoke test: search for a topic that's definitely in the vault
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("MCP", "Traefik") — confirm relevant results appear in top 5.
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- [ ] Negative test: search for nonsense gibberish — confirm low scores
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or empty results, not garbage matches.
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- [ ] Filter test: search with `tags=["mcp"]` — confirm results are
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restricted correctly.
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- [ ] Combined filter test: query + tag + date range — confirm AND
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semantics work.
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- [ ] Model-mismatch test: temporarily configure a different embedding
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model, confirm tool errors rather than returning bad rankings.
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### Phase 3 — Documentation
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- [ ] Add usage examples to the vault MCP server's README.
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- [ ] Note the tool in CLAUDE.md so agents know to use it.
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- [ ] Capture lessons learned in a session note.
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## Notes
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### Why this isn't part of the consolidation plan
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The consolidation plan builds the substrate — embeddings table, vault
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indexing, HNSW index. This plan builds the user-facing tool that
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consumes the substrate. Splitting them keeps the consolidation plan
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focused on infrastructure and lets the search tool ship with its own
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small, focused scope.
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### Why minimal output instead of full content
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Most searches are exploratory — "what do I have on X" — and only a
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fraction of returned hits will actually be read. Returning bodies in
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the search response means every search pays for content fetching that
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mostly isn't used. Keeping search lightweight and giving callers a
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separate `get_project` path matches the actual usage pattern.
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### Why a single tool with parameters instead of separate tools
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A single parameterized tool (`vault:search` with optional tags, domain,
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etc.) is more flexible than many tools (`vault:search_by_tag`,
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`vault:search_recent`, etc.) and matches how natural-language queries
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combine criteria. "Find recent context-window notes tagged claude-code"
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is one tool call with three filters, not three tool calls composed
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manually.
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### Why server-side embedding is the only correct choice
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Each embedding model produces vectors in its own geometry. Vectors from
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different models aren't comparable, and a model mismatch produces
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silently wrong rankings — not an error, just bad results. The server
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owns the stored vectors and knows which model produced them, so it's
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the right place to ensure the query uses the same model. Letting
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clients embed queries themselves would require every client to know
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which model is correct and would fail invisibly when wrong.
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### Future extension: scope parameter
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Anticipated shape for the unified memory search that follows:
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```
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vault:search(query, scope="vault" | "memory" | "all", ...)
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```
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- `scope="vault"` — current behavior, wiki schema only (default)
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- `scope="memory"` — ob1.thoughts and ob1.reflections only
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- `scope="all"` — everything
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Implementation extends the WHERE clause to include additional
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`source_schema` values. The output shape grows a `source` field so
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callers can tell vault hits from memory hits. Deferred to a separate
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plan once real vault-search usage informs the design.
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### Flag for Lovebug
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The Postgres consolidation plan (`postgres-consolidation-reflection-layer`,
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saved 2026-05-15) was written via the now-deprecated `save_note` path.
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The vault markdown file exists at
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`Sources/Homelab/postgres-consolidation-ob1-reflection-layer.md`, but
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the Postgres row does not — `vault:get_project` returns "not found" for
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that slug. The plan needs to be re-registered via `vault:create_artifact`
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so it shows up in `list_projects`, can have its status updated, and
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participates in the Trello card projection. Not blocking for this
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search plan, but worth handling before the consolidation plan goes
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into execution tracking.
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