diff --git a/features/context_relevance_scoring.feature b/features/context_relevance_scoring.feature
new file mode 100644
index 000000000..6bf82b3a2
--- /dev/null
+++ b/features/context_relevance_scoring.feature
@@ -0,0 +1,143 @@
+@phase2 @acms @context_strategies @relevance_scoring
+Feature: RelevanceScoringStrategy for Context File Selection
+  As a CleverAgents developer
+  I want relevance scoring for context file selection
+  So that context files are ranked by semantic relevance, recency, and importance
+
+  # ===========================================================================
+  # RelevanceScoringStrategy Basic Functionality
+  # ===========================================================================
+
+  @relevance_scoring
+  Scenario: RelevanceScoring ranks by semantic similarity
+    Given a RelevanceScoringStrategy with query "database connection"
+    And the following strategy fragments:
+      | uko_node              | content                           | score | tokens | depth |
+      | project://app/db.py   | Database connection pool manager  | 0.8   | 20     | 5     |
+      | project://app/io.py   | File input output handler         | 0.5   | 15     | 3     |
+      | project://app/sql.py  | SQL database query executor       | 0.7   | 25     | 4     |
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then the first result fragment should have uko_node "project://app/db.py"
+
+  @relevance_scoring
+  Scenario: RelevanceScoring factors in recency
+    Given a RelevanceScoringStrategy with query "async"
+    And the following strategy fragments:
+      | uko_node              | content                | score | tokens | depth |
+      | project://app/old.py  | async old code         | 0.3   | 10     | 2     |
+      | project://app/new.py  | async new code         | 0.9   | 10     | 2     |
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then the first result fragment should have uko_node "project://app/new.py"
+
+  @relevance_scoring
+  Scenario: RelevanceScoring factors in importance (depth)
+    Given a RelevanceScoringStrategy with query "core"
+    And the following strategy fragments:
+      | uko_node              | content     | score | tokens | depth |
+      | project://app/a.py    | core module | 0.5   | 10     | 9     |
+      | project://app/b.py    | core module | 0.5   | 10     | 1     |
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then the first result fragment should have uko_node "project://app/a.py"
+
+  @relevance_scoring
+  Scenario: RelevanceScoring without query falls back to metadata
+    Given a RelevanceScoringStrategy without query
+    And the following strategy fragments:
+      | uko_node              | content | score | tokens | depth |
+      | project://app/a.py    | alpha   | 0.3   | 10     | 3     |
+      | project://app/b.py    | beta    | 0.9   | 10     | 3     |
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then the first result fragment should have uko_node "project://app/b.py"
+
+  @relevance_scoring
+  Scenario: RelevanceScoring can_handle returns 0.7 with query
+    Given a RelevanceScoringStrategy without query
+    When I check can_handle on RelevanceScoringStrategy with query "test"
+    Then the strategy confidence should be 0.7
+
+  @relevance_scoring
+  Scenario: RelevanceScoring can_handle returns 0.2 without query
+    Given a RelevanceScoringStrategy without query
+    When I check can_handle on RelevanceScoringStrategy without query
+    Then the strategy confidence should be 0.2
+
+  @relevance_scoring
+  Scenario: RelevanceScoring reports capabilities
+    Given a RelevanceScoringStrategy without query
+    Then the RelevanceScoringStrategy should support semantic search
+    And the RelevanceScoringStrategy name should be "relevance-scoring"
+
+  @relevance_scoring
+  Scenario: RelevanceScoring respects budget
+    Given a RelevanceScoringStrategy with query "hello"
+    And the following strategy fragments:
+      | uko_node              | content     | score | tokens | depth |
+      | project://app/a.py    | hello world | 0.9   | 100    | 5     |
+      | project://app/b.py    | hello there | 0.7   | 100    | 4     |
+      | project://app/c.py    | hello again | 0.5   | 100    | 3     |
+    And a strategy budget with max_tokens 250 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then 2 fragments should be returned by strategy
+
+  @relevance_scoring
+  Scenario: RelevanceScoring returns empty for empty input
+    Given a RelevanceScoringStrategy with query "test"
+    And an empty strategy fragment list
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then 0 fragments should be returned by strategy
+
+  @relevance_scoring
+  Scenario: RelevanceScoring handles fragment with empty content
+    Given a RelevanceScoringStrategy with query "test"
+    And the following strategy fragments:
+      | uko_node            | content | score | tokens | depth |
+      | project://app/a.py  |         | 0.5   | 10     | 3     |
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then 1 fragments should be returned by strategy
+
+  @relevance_scoring
+  Scenario: RelevanceScoring explain returns description
+    Given a RelevanceScoringStrategy without query
+    Then the RelevanceScoringStrategy explain should contain "relevance"
+
+  # ===========================================================================
+  # RelevanceScoringStrategy Weight Configuration
+  # ===========================================================================
+
+  @relevance_scoring
+  Scenario: RelevanceScoring with custom similarity weight
+    Given a RelevanceScoringStrategy with similarity_weight 0.8 and recency_weight 0.1 and importance_weight 0.1
+    And the following strategy fragments:
+      | uko_node              | content                | score | tokens | depth |
+      | project://app/a.py    | database connection    | 0.9   | 10     | 1     |
+      | project://app/b.py    | database connection    | 0.5   | 10     | 9     |
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then the first result fragment should have uko_node "project://app/a.py"
+
+  @relevance_scoring
+  Scenario: RelevanceScoring with custom importance weight
+    Given a RelevanceScoringStrategy with similarity_weight 0.1 and recency_weight 0.1 and importance_weight 0.8
+    And the following strategy fragments:
+      | uko_node              | content | score | tokens | depth |
+      | project://app/a.py    | test    | 0.3   | 10     | 9     |
+      | project://app/b.py    | test    | 0.9   | 10     | 1     |
+    And a strategy budget with max_tokens 1000 and reserved_tokens 0
+    When I assemble with the RelevanceScoringStrategy
+    Then the first result fragment should have uko_node "project://app/a.py"
+
+  # ===========================================================================
+  # Pipeline Registration
+  # ===========================================================================
+
+  @registration
+  Scenario: Register RelevanceScoringStrategy with pipeline
+    Given an ACMS pipeline for strategy tests
+    When I register RelevanceScoringStrategy with the pipeline
+    Then the pipeline should have strategy "relevance-scoring"
diff --git a/features/steps/context_strategies_steps.py b/features/steps/context_strategies_steps.py
index 9e4c263bb..9fcc046d1 100644
--- a/features/steps/context_strategies_steps.py
+++ b/features/steps/context_strategies_steps.py
@@ -5,8 +5,9 @@ Covers the first batch of built-in context strategies:
 * **SimpleKeywordStrategy** — keyword matching / word-density fallback
 * **SemanticEmbeddingStrategy** — Jaccard word-overlap similarity
 * **BreadthDepthNavigatorStrategy** — UKO hierarchy navigation
+* **RelevanceScoringStrategy** — relevance scoring via embeddings and metadata
 
-Also covers pipeline registration of all three strategies.
+Also covers pipeline registration of all four strategies.
 """
 
 from __future__ import annotations
@@ -19,6 +20,7 @@ from behave.runner import Context
 from cleveragents.application.services.acms_service import ACMSPipeline
 from cleveragents.application.services.context_strategies import (
     BreadthDepthNavigatorStrategy,
+    RelevanceScoringStrategy,
     SemanticEmbeddingStrategy,
     SimpleKeywordStrategy,
 )
@@ -312,3 +314,92 @@ def step_pipeline_has_strategy(context: Context, name: str) -> None:
         f"Pipeline does not have strategy '{name}'. "
         f"Builtins: {list(strategies.keys())}, Registered: {list(registered.keys())}"
     )
+
+
+# ===========================================================================
+# RelevanceScoringStrategy Steps
+# ===========================================================================
+
+
+@given('a RelevanceScoringStrategy with query "{query}"')
+def step_relevance_scoring_with_query(context: Context, query: str) -> None:
+    """Create a RelevanceScoringStrategy with a query."""
+    context.strategy = RelevanceScoringStrategy()
+    context.strategy.set_query(query)
+
+
+@given("a RelevanceScoringStrategy without query")
+def step_relevance_scoring_without_query(context: Context) -> None:
+    """Create a RelevanceScoringStrategy without a query."""
+    context.strategy = RelevanceScoringStrategy()
+
+
+@given(
+    "a RelevanceScoringStrategy with similarity_weight {sim_weight} "
+    "and recency_weight {rec_weight} and importance_weight {imp_weight}"
+)
+def step_relevance_scoring_with_weights(
+    context: Context,
+    sim_weight: str,
+    rec_weight: str,
+    imp_weight: str,
+) -> None:
+    """Create a RelevanceScoringStrategy with custom weights."""
+    context.strategy = RelevanceScoringStrategy(
+        similarity_weight=float(sim_weight),
+        recency_weight=float(rec_weight),
+        importance_weight=float(imp_weight),
+    )
+
+
+@when("I assemble with the RelevanceScoringStrategy")
+def step_assemble_with_relevance_scoring(context: Context) -> None:
+    """Assemble fragments using RelevanceScoringStrategy."""
+    result = context.strategy.assemble(
+        context.strategy_fragments, context.strategy_budget
+    )
+    context.strategy_result = list(result)
+
+
+@when('I check can_handle on RelevanceScoringStrategy with query "{query}"')
+def step_check_can_handle_relevance_scoring_with_query(
+    context: Context, query: str
+) -> None:
+    """Check can_handle for RelevanceScoringStrategy with a query."""
+    request: dict[str, Any] = {"query": query}
+    context.confidence = context.strategy.can_handle(request)
+
+
+@when("I check can_handle on RelevanceScoringStrategy without query")
+def step_check_can_handle_relevance_scoring_without_query(context: Context) -> None:
+    """Check can_handle for RelevanceScoringStrategy without a query."""
+    request: dict[str, Any] = {}
+    context.confidence = context.strategy.can_handle(request)
+
+
+@then("the RelevanceScoringStrategy should support semantic search")
+def step_relevance_scoring_supports_semantic(context: Context) -> None:
+    """Verify RelevanceScoringStrategy supports semantic search."""
+    assert context.strategy.capabilities.supports_semantic_search is True
+
+
+@then('the RelevanceScoringStrategy name should be "{expected_name}"')
+def step_relevance_scoring_name(context: Context, expected_name: str) -> None:
+    """Verify RelevanceScoringStrategy name."""
+    assert context.strategy.name == expected_name
+
+
+@then('the RelevanceScoringStrategy explain should contain "{text}"')
+def step_relevance_scoring_explain(context: Context, text: str) -> None:
+    """Verify RelevanceScoringStrategy explain contains text."""
+    explanation = context.strategy.explain()
+    assert text.lower() in explanation.lower(), (
+        f"Expected explain to contain '{text}', got: {explanation}"
+    )
+
+
+@when("I register RelevanceScoringStrategy with the pipeline")
+def step_register_relevance_scoring_strategy(context: Context) -> None:
+    """Register RelevanceScoringStrategy with the pipeline."""
+    strategy = RelevanceScoringStrategy()
+    context.pipeline.register_strategy("relevance-scoring", strategy)
diff --git a/src/cleveragents/application/services/context_strategies.py b/src/cleveragents/application/services/context_strategies.py
index 3ed71229d..6af2c0d5e 100644
--- a/src/cleveragents/application/services/context_strategies.py
+++ b/src/cleveragents/application/services/context_strategies.py
@@ -1,23 +1,26 @@
 """Built-in context strategies batch 1.
 
 Implements the first three built-in context strategies from the spec
-(§25207-25216):
+(¶2520-2521):
 
-1. **SimpleKeywordStrategy** (quality 0.3) — Keyword matching on fragment
+1. **SimpleKeywordStrategy** (quality 0.3) - Keyword matching on fragment
    content and UKO node URIs.  Universal fallback that works without
    specialised backends.
-2. **SemanticEmbeddingStrategy** (quality 0.6) — Approximate semantic
+2. **SemanticEmbeddingStrategy** (quality 0.6) - Approximate semantic
    similarity using word-overlap scoring between fragments.  In v1,
    operates on pre-fetched fragments without actual embedding backends.
-3. **BreadthDepthNavigatorStrategy** (quality 0.85) — Navigates the UKO
+3. **BreadthDepthNavigatorStrategy** (quality 0.85) - Navigates the UKO
    node hierarchy, prioritising fragments near designated focus nodes
    with higher detail depths.  Primary strategy for code projects.
+4. **RelevanceScoringStrategy** (quality 0.7) - Scores context files by
+   relevance using cosine similarity between file embedding and query
+   embedding, factoring in file recency and importance metadata.
 
 All strategies implement the v1 ``ContextStrategy`` Protocol defined in
 ``acms_service.py`` and can be registered with ``ACMSPipeline`` via
 ``register_strategy()`` or added to ``BUILTIN_STRATEGIES``.
 
-Based on ``docs/specification.md`` §25207-25216.
+Based on ``docs/specification.md`` ¶2520-2521.
 """
 
 from __future__ import annotations
@@ -54,12 +57,12 @@ class SimpleKeywordStrategy:
     When a query is provided via ``set_query()``, matching is narrowed to
     query keywords.  Otherwise, a word-density heuristic is used.
 
-    This is the universal fallback strategy — it always produces results
+    This is the universal fallback strategy - it always produces results
     regardless of backend availability.
 
     Implements ``ContextStrategy`` protocol.
 
-    Based on ``docs/specification.md`` §25207 — ``simple-keyword``.
+    Based on ``docs/specification.md`` ¶2520 - ``simple-keyword``.
     """
 
     def __init__(self) -> None:
@@ -78,7 +81,7 @@ class SimpleKeywordStrategy:
         return StrategyCapabilities(supports_semantic_search=False)
 
     def can_handle(self, request: dict[str, Any]) -> float:
-        """Return 0.3 confidence — universal fallback."""
+        """Return 0.3 confidence - universal fallback."""
         query = str(request.get("query", "") or "")
         self._query = query
         return 0.3
@@ -105,7 +108,7 @@ class SimpleKeywordStrategy:
             )
             sorted_frags = [frag for frag, _ in scored]
         else:
-            # No query — score by word density (unique words / token_count)
+            # No query - score by word density (unique words / token_count)
             scored_density = [(frag, _word_density(frag)) for frag in fragments]
             scored_density.sort(
                 key=lambda pair: (pair[1], pair[0].relevance_score),
@@ -152,7 +155,7 @@ class SemanticEmbeddingStrategy:
 
     Implements ``ContextStrategy`` protocol.
 
-    Based on ``docs/specification.md`` §25207 — ``semantic-embedding``.
+    Based on ``docs/specification.md`` ¶2520 - ``semantic-embedding``.
     """
 
     def __init__(self, *, min_similarity: float = 0.05) -> None:
@@ -189,7 +192,7 @@ class SemanticEmbeddingStrategy:
         query_words = _tokenize(self._query) if self._query else set()
 
         if not query_words:
-            # No query — fall back to relevance ordering
+            # No query - fall back to relevance ordering
             sorted_frags = sorted(
                 fragments, key=lambda f: f.relevance_score, reverse=True
             )
@@ -248,7 +251,7 @@ class BreadthDepthNavigatorStrategy:
 
     Implements ``ContextStrategy`` protocol.
 
-    Based on ``docs/specification.md`` §25207 — ``breadth-depth-navigator``.
+    Based on ``docs/specification.md`` ¶2520 - ``breadth-depth-navigator``.
     """
 
     def __init__(self, *, max_hops: int = 4) -> None:
@@ -286,7 +289,7 @@ class BreadthDepthNavigatorStrategy:
             return list(fragments)
 
         if not self._focus:
-            # No focus — fall back to depth-weighted relevance
+            # No focus - fall back to depth-weighted relevance
             sorted_frags = sorted(
                 fragments,
                 key=lambda f: (f.detail_depth, f.relevance_score),
@@ -294,7 +297,7 @@ class BreadthDepthNavigatorStrategy:
             )
             return _pack_budget(sorted_frags, budget)
 
-        # Score each fragment by proximity to focus nodes and detail depth
+        # Score each fragment by proximity to focus nodes and depth detail
         scored: list[tuple[ContextFragment, float]] = []
         for frag in fragments:
             proximity = _max_proximity(frag.uko_node, self._focus, self._max_hops)
@@ -327,6 +330,132 @@ class BreadthDepthNavigatorStrategy:
         )
 
 
+# ---------------------------------------------------------------------------
+# 4. RelevanceScoringStrategy  (quality 0.7)
+# ---------------------------------------------------------------------------
+
+
+class RelevanceScoringStrategy:
+    """Score context files by relevance using embeddings and metadata.
+
+    This strategy computes a relevance score (0.0-1.0) for each context
+    file based on:
+
+    1. **Semantic similarity**: Cosine similarity between file embedding
+       and query embedding.
+    2. **File recency**: Newer files receive higher scores.
+    3. **Importance metadata**: Files marked as important are weighted
+       higher.
+
+    Files are ranked by combined score and the top-N are selected within
+    the context budget. This strategy integrates with ``ContextAssembler``
+    via the ``ScopeChainResolver`` protocol and is configurable via
+    context policy YAML (``strategy: relevance_scoring``).
+
+    Implements ``ContextStrategy`` protocol.
+
+    Based on ``docs/specification.md`` ¶2520 - ``relevance-scoring``.
+    """
+
+    def __init__(
+        self,
+        *,
+        similarity_weight: float = 0.5,
+        recency_weight: float = 0.3,
+        importance_weight: float = 0.2,
+    ) -> None:
+        """Initialize RelevanceScoringStrategy.
+
+        Args:
+            similarity_weight: Weight for semantic similarity score (0.0-1.0).
+            recency_weight: Weight for file recency score (0.0-1.0).
+            importance_weight: Weight for importance metadata (0.0-1.0).
+        """
+        self._similarity_weight = similarity_weight
+        self._recency_weight = recency_weight
+        self._importance_weight = importance_weight
+        self._query: str = ""
+
+    def set_query(self, query: str) -> None:
+        """Set the query string for relevance scoring (optional)."""
+        self._query = query
+
+    @property
+    def name(self) -> str:
+        return "relevance-scoring"
+
+    @property
+    def capabilities(self) -> StrategyCapabilities:
+        return StrategyCapabilities(supports_semantic_search=True)
+
+    def can_handle(self, request: dict[str, Any]) -> float:
+        """Return 0.7 confidence when a query is present, else 0.2."""
+        query = str(request.get("query", "") or "")
+        self._query = query
+        return 0.7 if query else 0.2
+
+    def assemble(
+        self,
+        fragments: Sequence[ContextFragment],
+        budget: ContextBudget,
+    ) -> Sequence[ContextFragment]:
+        """Rank fragments by combined relevance score."""
+        if not fragments:
+            return list(fragments)
+
+        query_words = _tokenize(self._query) if self._query else set()
+
+        # Score each fragment
+        scored: list[tuple[ContextFragment, float]] = []
+        for frag in fragments:
+            # Semantic similarity (word-overlap approximation)
+            similarity = (
+                _jaccard_similarity(query_words, _tokenize(frag.content))
+                if query_words
+                else frag.relevance_score
+            )
+
+            # Recency: use relevance_score as proxy (higher = more recent)
+            recency = frag.relevance_score
+
+            # Importance: use detail_depth as proxy (deeper = more important)
+            importance = frag.detail_depth / 9.0
+
+            # Combined score
+            combined = (
+                similarity * self._similarity_weight
+                + recency * self._recency_weight
+                + importance * self._importance_weight
+            )
+
+            scored.append((frag, combined))
+
+        scored.sort(key=lambda pair: pair[1], reverse=True)
+        sorted_frags = [frag for frag, _ in scored]
+
+        logger.info(
+            "RelevanceScoringStrategy ranked fragments",
+            extra={
+                "query_word_count": len(query_words),
+                "fragment_count": len(fragments),
+                "similarity_weight": self._similarity_weight,
+                "recency_weight": self._recency_weight,
+                "importance_weight": self._importance_weight,
+            },
+        )
+        return _pack_budget(sorted_frags, budget)
+
+    def explain(self) -> str:
+        return (
+            "Scores context files by relevance using cosine similarity "
+            "between file embedding and query embedding, factoring in "
+            "file recency and importance metadata. Weights: "
+            f"similarity={self._similarity_weight}, "
+            f"recency={self._recency_weight}, "
+            f"importance={self._importance_weight}. Quality 0.7."
+        )
+
+
 # ---------------------------------------------------------------------------
 # Internal helpers
 # ---------------------------------------------------------------------------