Add --quick and --deep flags for research depth

- quick: 8-12 sources each, faster response
- default: 20-30 sources each (unchanged behavior)
- deep: 50-70 Reddit, 40-60 X for comprehensive research

Adjusts API timeouts based on depth. Cache keys include depth
so different depths are cached separately.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

SKILL.md

@@ -84,7 +84,12 @@ echo "Please edit it to add your API keys, then run the skill again."
 
 ## Research Execution
 
-Run the research orchestrator with the TOPIC:
+Run the research orchestrator with the TOPIC.
+
+**Depth options** (passed through from user's command):
+- `--quick` → Faster, fewer sources (8-12 each)
+- (default) → Balanced (20-30 each)
+- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)
 
 ```bash
 python3 ~/.claude/skills/last30days/scripts/last30days.py "$ARGUMENTS" --emit=compact 2>&1

scripts/last30days.py

@@ -10,6 +10,8 @@ Options:
     --mock              Use fixtures instead of real API calls
     --emit=MODE         Output mode: compact|json|md|context|path (default: compact)
     --sources=MODE      Source selection: auto|reddit|x|both (default: auto)
+    --quick             Faster research with fewer sources (8-12 each)
+    --deep              Comprehensive research with more sources (50-70 Reddit, 40-60 X)
 """
 
 import argparse
@@ -54,6 +56,7 @@ def run_research(
     selected_models: dict,
     from_date: str,
     to_date: str,
+    depth: str = "default",
     mock: bool = False,
 ) -> tuple:
     """Run the research pipeline.
@@ -76,6 +79,7 @@ def run_research(
                 config["OPENAI_API_KEY"],
                 selected_models["openai"],
                 topic,
+                depth=depth,
             )
 
         # Parse response
@@ -102,6 +106,7 @@ def run_research(
                 topic,
                 from_date,
                 to_date,
+                depth=depth,
             )
 
         # Parse response
@@ -129,9 +134,30 @@ def main():
         default="auto",
         help="Source selection",
     )
+    parser.add_argument(
+        "--quick",
+        action="store_true",
+        help="Faster research with fewer sources (8-12 each)",
+    )
+    parser.add_argument(
+        "--deep",
+        action="store_true",
+        help="Comprehensive research with more sources (50-70 Reddit, 40-60 X)",
+    )
 
     args = parser.parse_args()
 
+    # Determine depth
+    if args.quick and args.deep:
+        print("Error: Cannot use both --quick and --deep", file=sys.stderr)
+        sys.exit(1)
+    elif args.quick:
+        depth = "quick"
+    elif args.deep:
+        depth = "deep"
+    else:
+        depth = "default"
+
     if not args.topic:
         print("Error: Please provide a topic to research.", file=sys.stderr)
         print("Usage: python3 last30days.py <topic> [options]", file=sys.stderr)
@@ -166,7 +192,7 @@ def main():
     from_date, to_date = dates.get_date_range(30)
 
     # Check cache (unless refresh or mock)
-    cache_key = cache.get_cache_key(args.topic, from_date, to_date, sources)
+    cache_key = cache.get_cache_key(args.topic, from_date, to_date, f"{sources}:{depth}")
     if not args.refresh and not args.mock:
         cached = cache.load_cache(cache_key)
         if cached:
@@ -208,6 +234,7 @@ def main():
         selected_models,
         from_date,
         to_date,
+        depth,
         args.mock,
     )
 

scripts/lib/openai_reddit.py

@@ -8,9 +8,16 @@ from . import http
 
 OPENAI_RESPONSES_URL = "https://api.openai.com/v1/responses"
 
+# Depth configurations: (min, max) threads to request
+DEPTH_CONFIG = {
+    "quick": (8, 12),
+    "default": (20, 30),
+    "deep": (50, 70),
+}
+
 REDDIT_SEARCH_PROMPT = """Search Reddit for discussions about: {topic}
 
-Focus on threads from the last 30 days. Find 15-30 high-quality, relevant threads.
+Focus on threads from the last 30 days. Find {min_items}-{max_items} high-quality, relevant threads.
 
 IMPORTANT: Return ONLY valid JSON in this exact format, no other text:
 {{
@@ -38,6 +45,7 @@ def search_reddit(
     api_key: str,
     model: str,
     topic: str,
+    depth: str = "default",
     mock_response: Optional[Dict] = None,
 ) -> Dict[str, Any]:
     """Search Reddit for relevant threads using OpenAI Responses API.
@@ -46,6 +54,7 @@ def search_reddit(
         api_key: OpenAI API key
         model: Model to use
         topic: Search topic
+        depth: Research depth - "quick", "default", or "deep"
         mock_response: Mock response for testing
 
     Returns:
@@ -54,11 +63,16 @@ def search_reddit(
     if mock_response is not None:
         return mock_response
 
+    min_items, max_items = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+
     headers = {
         "Authorization": f"Bearer {api_key}",
         "Content-Type": "application/json",
     }
 
+    # Adjust timeout based on depth
+    timeout = 60 if depth == "quick" else 90 if depth == "default" else 120
+
     payload = {
         "model": model,
         "tools": [
@@ -70,10 +84,10 @@ def search_reddit(
             }
         ],
         "include": ["web_search_call.action.sources"],
-        "input": REDDIT_SEARCH_PROMPT.format(topic=topic),
+        "input": REDDIT_SEARCH_PROMPT.format(topic=topic, min_items=min_items, max_items=max_items),
     }
 
-    return http.post(OPENAI_RESPONSES_URL, payload, headers=headers, timeout=60)
+    return http.post(OPENAI_RESPONSES_URL, payload, headers=headers, timeout=timeout)
 
 
 def parse_reddit_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:

scripts/lib/xai_x.py

@@ -9,9 +9,16 @@ from . import http
 # xAI uses chat completions endpoint
 XAI_CHAT_URL = "https://api.x.ai/v1/chat/completions"
 
+# Depth configurations: (min, max) posts to request
+DEPTH_CONFIG = {
+    "quick": (8, 12),
+    "default": (20, 30),
+    "deep": (40, 60),
+}
+
 X_SEARCH_PROMPT = """You have access to real-time X (Twitter) data. Search for posts about: {topic}
 
-Focus on posts from {from_date} to {to_date}. Find 15-30 high-quality, relevant posts.
+Focus on posts from {from_date} to {to_date}. Find {min_items}-{max_items} high-quality, relevant posts.
 
 IMPORTANT: Return ONLY valid JSON in this exact format, no other text:
 {{
@@ -47,6 +54,7 @@ def search_x(
     topic: str,
     from_date: str,
     to_date: str,
+    depth: str = "default",
     mock_response: Optional[Dict] = None,
 ) -> Dict[str, Any]:
     """Search X for relevant posts using xAI API with live search.
@@ -57,6 +65,7 @@ def search_x(
         topic: Search topic
         from_date: Start date (YYYY-MM-DD)
         to_date: End date (YYYY-MM-DD)
+        depth: Research depth - "quick", "default", or "deep"
         mock_response: Mock response for testing
 
     Returns:
@@ -65,11 +74,16 @@ def search_x(
     if mock_response is not None:
         return mock_response
 
+    min_items, max_items = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+
     headers = {
         "Authorization": f"Bearer {api_key}",
         "Content-Type": "application/json",
     }
 
+    # Adjust timeout based on depth
+    timeout = 60 if depth == "quick" else 90 if depth == "default" else 120
+
     # Use chat completions format with search enabled
     payload = {
         "model": model,
@@ -84,6 +98,8 @@ def search_x(
                     topic=topic,
                     from_date=from_date,
                     to_date=to_date,
+                    min_items=min_items,
+                    max_items=max_items,
                 ),
             }
         ],
@@ -95,7 +111,7 @@ def search_x(
         },
     }
 
-    return http.post(XAI_CHAT_URL, payload, headers=headers, timeout=90)
+    return http.post(XAI_CHAT_URL, payload, headers=headers, timeout=timeout)
 
 
 def parse_x_response(response: Dict[str, Any]) -> List[Dict[str, Any]]: