chore(openspec): drop 9 superseded proposals + 11 stub archive files

Drop 9 batch proposals that are superseded by the boocode-lift-analysis
(boocontext-audit, conductor upgrades, self-healing/verify-gate skills):
add-3tier-memory, import-llm-evaluator, import-pregel-engine, plugin-platform,
conductor-evolution, code-intelligence-upgrade, dev-workflow, ui-overhaul,
agent-reliability.

Delete 11 stub archive files (49-66B each, 'Status: Shipped. Archived.' only)
that provide zero documentation value over the existing CHANGELOG.md + git tags.

openspec/changes/archived/2026-06-07-memory-context-engineering/specs/hybrid-search/spec.md

@@ -0,0 +1,73 @@
+## ADDED Requirements
+
+### Requirement: Hybrid search with vector + keyword fusion
+The system SHALL combine vector similarity search and keyword search into unified ranked results.
+
+#### Scenario: Vector search runs when embedding provider available
+- **WHEN** an embedding provider is configured
+- **THEN** the system SHALL compute a query embedding and perform cosine similarity search
+- **WHEN** no embedding provider is configured
+- **THEN** the system SHALL gracefully degrade to keyword-only search
+
+#### Scenario: Keyword search always runs
+- **WHEN** a search query is submitted
+- **THEN** the system SHALL always perform keyword search regardless of embedding provider availability
+
+#### Scenario: Weighted score merging
+- **WHEN** both vector and keyword results are available
+- **THEN** the final score SHALL be: `vector_weight * vector_score + keyword_weight * keyword_score`
+- **THEN** default weights SHALL be `vector_weight=0.7`, `keyword_weight=0.3`
+- **THEN** weights SHALL be configurable
+
+### Requirement: Vector search via numpy cosine similarity
+The system SHALL perform vector search using numpy-vectorized cosine similarity for performance.
+
+#### Scenario: Vectorized cosine similarity
+- **WHEN** numpy is available
+- **THEN** all chunk embeddings SHALL be loaded into a numpy matrix `(N, D)`
+- **THEN** cosine similarity SHALL be computed as `matrix @ query_vector` (BLAS matrix-vector multiply)
+- **THEN** top-K results SHALL be selected via `argpartition` (O(N) average)
+
+#### Scenario: Pure-Python fallback
+- **WHEN** numpy is unavailable
+- **THEN** cosine similarity SHALL be computed per-row with pure Python
+- **THEN** results SHALL be sorted and the top K returned
+
+### Requirement: Three-tier keyword search (FTS5 → trigram → LIKE)
+The system SHALL provide a cascading keyword search strategy for multi-language support.
+
+#### Scenario: Standard FTS5 for ASCII queries
+- **WHEN** the query contains only ASCII characters
+- **THEN** the system SHALL use SQLite FTS5 with the unicode61 tokenizer
+- **THEN** BM25 ranking SHALL be converted to a `[0, 1)` score
+
+#### Scenario: Trigram FTS5 for CJK queries
+- **WHEN** the query contains CJK (Chinese, Japanese, Korean) characters
+- **THEN** the system SHALL use SQLite FTS5 with the trigram tokenizer
+- **THEN** CJK character sequences and ASCII words SHALL be extracted and joined with AND
+
+#### Scenario: LIKE fallback for edge cases
+- **WHEN** FTS5 is unavailable or returns empty results
+- **THEN** the system SHALL fall back to LIKE-based search
+- **THEN** CJK runs (1+ chars) and ASCII words (3+ chars) SHALL be matched independently
+
+### Requirement: Temporal decay for dated memory files
+The system SHALL apply exponential decay to search scores for dated memory files.
+
+#### Scenario: Decay applied to dated files
+- **WHEN** a memory chunk path matches `YYYY-MM-DD.md`
+- **THEN** the combined score SHALL be multiplied by `exp(-ln(2)/half_life * age_days)`
+- **THEN** the default `half_life` SHALL be 30 days
+- **WHEN** the path does not contain a date (e.g., `MEMORY.md`)
+- **THEN** no decay SHALL be applied (multiplier = 1.0)
+
+### Requirement: Result filtering and limits
+The system SHALL filter search results by minimum score and maximum count.
+
+#### Scenario: Min score threshold
+- **WHEN** search results are merged
+- **THEN** results with score below `min_score` (default 0.1) SHALL be discarded
+
+#### Scenario: Max results limit
+- **WHEN** search results exceed `max_results`
+- **THEN** only the top `max_results` by combined score SHALL be returned