conductor(tracks): Add two deferred future tracks

- aggregation_smarter_summaries: Sub-agent summarization, hash-based caching
- system_context_exposure: Expose hidden _SYSTEM_PROMPT for user customization

conductor/tracks/aggregation_smarter_summaries_20260322/spec.md

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+# Specification: Smarter Aggregation with Sub-Agent Summarization
+
+## 1. Overview
+
+This track improves the context aggregation system to use sub-agent passes for intelligent summarization and hash-based caching to avoid redundant work.
+
+**Current Problem:**
+- Aggregation is a simple pass that either injects full file content or a basic skeleton
+- No intelligence applied to determine what level of detail is needed
+- Same files get re-summarized on every discussion start even if unchanged
+
+**Goal:**
+- Use a sub-agent during aggregation pass for high-tier agents to generate succinct summaries
+- Cache summaries based on file hash - only re-summarize if file changed
+- Smart outline generation for code files, summary for text files
+
+## 2. Current State Audit
+
+### Existing Aggregation Behavior
+- `aggregate.py` handles context aggregation
+- `file_cache.py` provides AST parsing and skeleton generation
+- Per-file flags: `Auto-Aggregate` (summarize), `Force Full` (inject raw)
+- No caching of summarization results
+
+### Provider API Considerations
+- Different providers have different prompt/caching mechanisms
+- Need to verify how each provider handles system context and caching
+- May need provider-specific aggregation strategies
+
+## 3. Functional Requirements
+
+### 3.1 Hash-Based Summary Cache
+- Generate SHA256 hash of file content
+- Store summaries in a cache (file-based or in project state)
+- Before summarizing, check if file hash matches cached summary
+- Cache invalidation when file content changes
+
+### 3.2 Sub-Agent Summarization Pass
+- During aggregation, optionally invoke sub-agent for summarization
+- Sub-agent generates concise summary of file purpose and key points
+- Different strategies for:
+  - Code files: AST-based outline + key function signatures
+  - Text files: Paragraph-level summary
+  - Config files: Key-value extraction
+
+### 3.3 Tiered Aggregation Strategy
+- Tier 3/4 workers: Get skeleton outlines (fast, cheap)
+- Tier 2 (Tech Lead): Get summaries with key details
+- Tier 1 (Orchestrator): May get full content or enhanced summaries
+- Configurable per-agent via Persona
+
+### 3.4 Cache Persistence
+- Summaries persist across sessions
+- Stored in project directory or centralized cache location
+- Manual cache clear option in UI
+
+## 4. Data Model
+
+### 4.1 Summary Cache Entry
+```python
+{
+    "file_path": str,
+    "file_hash": str,  # SHA256 of content
+    "summary": str,
+    "outline": str,  # For code files
+    "generated_at": str,  # ISO timestamp
+    "generator_tier": str,  # Which tier generated it
+}
+```
+
+### 4.2 Aggregation Config
+```toml
+[aggregation]
+default_mode = "summarize"  # "full", "summarize", "outline"
+cache_enabled = true
+cache_dir = ".slop_cache"
+```
+
+## 5. UI Changes
+
+- Add "Clear Summary Cache" button in Files & Media or Context Composition
+- Show cached status indicator on files (similar to AST cache indicator)
+- Configuration in AI Settings or Project Settings
+
+## 6. Acceptance Criteria
+
+- [ ] File hash computed before summarization
+- [ ] Summary cache persists across app restarts
+- [ ] Sub-agent generates better summaries than basic skeleton
+- [ ] Aggregation respects tier-level configuration
+- [ ] Cache can be manually cleared
+- [ ] Provider APIs handle aggregated context correctly
+
+## 7. Out of Scope
+- Changes to provider API internals
+- Vector store / embeddings for RAG (separate track)
+- Changes to Session Hub / Discussion Hub layout
+
+## 8. Dependencies
+- `aggregate.py` - main aggregation logic
+- `file_cache.py` - AST parsing and caching
+- `ai_client.py` - sub-agent invocation
+- `models.py` - may need new config structures