fix: Enforce strict 30-day date filtering

Previously Reddit was returning ~60% old content (some from 2022).
This commit adds multiple layers of date enforcement:

- Reddit prompt: Explicit from_date/to_date with "fewer results > older results"
- Hard filter: filter_by_date_range() in normalize.py excludes old content
- WebSearch Date Detective: Extracts dates from URLs (/2026/01/24/) and
  snippets ("January 24, 2026", "3 days ago")
- WebSearch scoring: +10 bonus for verified dates, -20 penalty for unknown

The skill now guarantees only content from the last 30 days.

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

plans/fix-strict-date-filtering.md

@@ -0,0 +1,328 @@
+# fix: Enforce Strict 30-Day Date Filtering
+
+## Overview
+
+The `/last30days` skill is returning content older than 30 days, violating its core promise. Analysis shows:
+- **Reddit**: Only 40% of results within 30 days (9/15 were older, some from 2022!)
+- **X**: 100% within 30 days (working correctly)
+- **WebSearch**: 90% had unknown dates (can't verify freshness)
+
+## Problem Statement
+
+The skill's name is "last30days" - users expect ONLY content from the last 30 days. Currently:
+
+1. **Reddit search prompt** says "prefer recent threads, but include older relevant ones if recent ones are scarce" - this is too permissive
+2. **X search prompt** explicitly includes `from_date` and `to_date` - this is why it works
+3. **WebSearch** returns pages without publication dates - we can't verify they're recent
+4. **Scoring penalties** (-10 for low date confidence) don't prevent old content from appearing
+
+## Proposed Solution
+
+### Strategy: "Hard Filter, Not Soft Penalty"
+
+Instead of penalizing old content, **exclude it entirely**. If it's not from the last 30 days, it shouldn't appear.
+
+| Source | Current Behavior | New Behavior |
+|--------|------------------|--------------|
+| Reddit | Weak "prefer recent" | Explicit date range + hard filter |
+| X | Explicit date range (working) | No change needed |
+| WebSearch | No date awareness | Require recent markers OR exclude |
+
+## Technical Approach
+
+### Phase 1: Fix Reddit Date Filtering
+
+**File: `scripts/lib/openai_reddit.py`**
+
+Current prompt (line 33):
+```
+Find {min_items}-{max_items} relevant Reddit discussion threads.
+Prefer recent threads, but include older relevant ones if recent ones are scarce.
+```
+
+New prompt:
+```
+Find {min_items}-{max_items} relevant Reddit discussion threads from {from_date} to {to_date}.
+
+CRITICAL: Only include threads posted within the last 30 days (after {from_date}).
+Do NOT include threads older than {from_date}, even if they seem relevant.
+If you cannot find enough recent threads, return fewer results rather than older ones.
+```
+
+**Changes needed:**
+1. Add `from_date` and `to_date` parameters to `search_reddit()` function
+2. Inject dates into `REDDIT_SEARCH_PROMPT` like X does
+3. Update caller in `last30days.py` to pass dates
+
+### Phase 2: Add Hard Date Filtering (Post-Processing)
+
+**File: `scripts/lib/normalize.py`**
+
+Add a filter step that DROPS items with dates before `from_date`:
+
+```python
+def filter_by_date_range(
+    items: List[Union[RedditItem, XItem, WebSearchItem]],
+    from_date: str,
+    to_date: str,
+    require_date: bool = False,
+) -> List:
+    """Hard filter: Remove items outside the date range.
+
+    Args:
+        items: List of items to filter
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        require_date: If True, also remove items with no date
+
+    Returns:
+        Filtered list with only items in range
+    """
+    result = []
+    for item in items:
+        if item.date is None:
+            if not require_date:
+                result.append(item)  # Keep unknown dates (with penalty)
+            continue
+
+        # Hard filter: if date is before from_date, exclude
+        if item.date < from_date:
+            continue  # DROP - too old
+
+        if item.date > to_date:
+            continue  # DROP - future date (likely parsing error)
+
+        result.append(item)
+
+    return result
+```
+
+### Phase 3: WebSearch Date Intelligence
+
+WebSearch CAN find recent content - Medium posts have dates, GitHub has commit timestamps, news sites have publication dates. We should **extract and prioritize** these signals.
+
+**Strategy: "Date Detective"**
+
+1. **Extract dates from URLs**: Many sites embed dates in URLs
+   - Medium: `medium.com/@author/title-abc123` (no date) vs news sites
+   - GitHub: Look for commit dates, release dates in snippets
+   - News: `/2026/01/24/article-title`
+   - Blogs: `/blog/2026/01/title`
+
+2. **Extract dates from snippets**: Look for date markers
+   - "January 24, 2026", "Jan 2026", "yesterday", "this week"
+   - "Published:", "Posted:", "Updated:"
+   - Relative markers: "2 days ago", "last week"
+
+3. **Prioritize results with verifiable dates**:
+   - Results with recent dates (within 30 days): Full score
+   - Results with old dates: EXCLUDE
+   - Results with no date signals: Heavy penalty (-20) but keep as supplementary
+
+**File: `scripts/lib/websearch.py`**
+
+Add date extraction functions:
+
+```python
+import re
+from datetime import datetime, timedelta
+
+# Patterns for date extraction
+URL_DATE_PATTERNS = [
+    r'/(\d{4})/(\d{2})/(\d{2})/',  # /2026/01/24/
+    r'/(\d{4})-(\d{2})-(\d{2})/',  # /2026-01-24/
+    r'/(\d{4})(\d{2})(\d{2})/',    # /20260124/
+]
+
+SNIPPET_DATE_PATTERNS = [
+    r'(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]* (\d{1,2}),? (\d{4})',
+    r'(\d{1,2}) (Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]* (\d{4})',
+    r'(\d{4})-(\d{2})-(\d{2})',
+    r'Published:?\s*(\d{4}-\d{2}-\d{2})',
+    r'(\d{1,2}) (days?|hours?|minutes?) ago',  # Relative dates
+]
+
+def extract_date_from_url(url: str) -> Optional[str]:
+    """Try to extract a date from URL path."""
+    for pattern in URL_DATE_PATTERNS:
+        match = re.search(pattern, url)
+        if match:
+            # Parse and return YYYY-MM-DD format
+            ...
+    return None
+
+def extract_date_from_snippet(snippet: str) -> Optional[str]:
+    """Try to extract a date from text snippet."""
+    for pattern in SNIPPET_DATE_PATTERNS:
+        match = re.search(pattern, snippet, re.IGNORECASE)
+        if match:
+            # Parse and return YYYY-MM-DD format
+            ...
+    return None
+
+def extract_date_signals(url: str, snippet: str, title: str) -> tuple[Optional[str], str]:
+    """Extract date from any available signal.
+
+    Returns: (date_string, confidence)
+    - date from URL: 'high' confidence
+    - date from snippet: 'med' confidence
+    - no date found: None, 'low' confidence
+    """
+    # Try URL first (most reliable)
+    url_date = extract_date_from_url(url)
+    if url_date:
+        return url_date, 'high'
+
+    # Try snippet
+    snippet_date = extract_date_from_snippet(snippet)
+    if snippet_date:
+        return snippet_date, 'med'
+
+    # Try title
+    title_date = extract_date_from_snippet(title)
+    if title_date:
+        return title_date, 'med'
+
+    return None, 'low'
+```
+
+**Update WebSearch parsing to use date extraction:**
+
+```python
+def parse_websearch_results(results, topic, from_date, to_date):
+    items = []
+    for result in results:
+        url = result.get('url', '')
+        snippet = result.get('snippet', '')
+        title = result.get('title', '')
+
+        # Extract date signals
+        extracted_date, confidence = extract_date_signals(url, snippet, title)
+
+        # Hard filter: if we found a date and it's too old, skip
+        if extracted_date and extracted_date < from_date:
+            continue  # DROP - verified old content
+
+        item = {
+            'date': extracted_date,
+            'date_confidence': confidence,
+            ...
+        }
+        items.append(item)
+
+    return items
+```
+
+**File: `scripts/lib/score.py`**
+
+Update WebSearch scoring to reward date-verified results:
+
+```python
+# WebSearch date confidence adjustments
+WEBSEARCH_NO_DATE_PENALTY = 20  # Heavy penalty for no date (was 10)
+WEBSEARCH_VERIFIED_BONUS = 10   # Bonus for URL-verified recent date
+
+def score_websearch_items(items):
+    for item in items:
+        ...
+        # Date confidence adjustments
+        if item.date_confidence == 'high':
+            overall += WEBSEARCH_VERIFIED_BONUS  # Reward verified dates
+        elif item.date_confidence == 'low':
+            overall -= WEBSEARCH_NO_DATE_PENALTY  # Heavy penalty for unknown
+        ...
+```
+
+**Result**: WebSearch results with verifiable recent dates rank well. Results with no dates are heavily penalized but still appear as supplementary context. Old verified content is excluded entirely.
+
+### Phase 4: Update Statistics Display
+
+Only count Reddit and X in "from the last 30 days" claim. WebSearch should be clearly labeled as supplementary.
+
+## Acceptance Criteria
+
+### Functional Requirements
+
+- [x] Reddit search prompt includes explicit `from_date` and `to_date`
+- [x] Items with dates before `from_date` are EXCLUDED, not just penalized
+- [x] X search continues working (no regression)
+- [x] WebSearch extracts dates from URLs (e.g., `/2026/01/24/`)
+- [x] WebSearch extracts dates from snippets (e.g., "January 24, 2026")
+- [x] WebSearch with verified recent dates gets +10 bonus
+- [x] WebSearch with no date signals gets -20 penalty (but still appears)
+- [x] WebSearch with verified OLD dates is EXCLUDED
+
+### Non-Functional Requirements
+
+- [ ] No increase in API latency
+- [ ] Graceful handling when few recent results exist (return fewer, not older)
+- [ ] Clear user messaging when results are limited due to strict filtering
+
+### Quality Gates
+
+- [ ] Test: Reddit search returns 0% results older than 30 days
+- [ ] Test: X search continues to return 100% recent results
+- [ ] Test: WebSearch is clearly differentiated in output
+- [ ] Test: Edge case - topic with no recent content shows helpful message
+
+## Implementation Order
+
+1. **Phase 1**: Fix Reddit prompt (highest impact, simple change)
+2. **Phase 2**: Add hard date filter in normalize.py (safety net)
+3. **Phase 3**: Add WebSearch date extraction (URL + snippet parsing)
+4. **Phase 4**: Update WebSearch scoring (bonus for verified, heavy penalty for unknown)
+5. **Phase 5**: Update output display to show date confidence
+
+## Testing Plan
+
+### Before/After Test
+
+Run same query before and after fix:
+```
+/last30days remotion launch videos
+```
+
+**Expected Before:**
+- Reddit: 40% within 30 days
+
+**Expected After:**
+- Reddit: 100% within 30 days (or fewer results if not enough recent content)
+
+### Edge Case Tests
+
+| Scenario | Expected Behavior |
+|----------|-------------------|
+| Topic with no recent content | Return 0 results + helpful message |
+| Topic with 5 recent results | Return 5 results (not pad with old ones) |
+| Mixed old/new results | Only return new ones |
+
+### WebSearch Date Extraction Tests
+
+| URL/Snippet | Expected Date | Confidence |
+|-------------|---------------|------------|
+| `medium.com/blog/2026/01/15/title` | 2026-01-15 | high |
+| `github.com/repo` + "Released Jan 20, 2026" | 2026-01-20 | med |
+| `docs.example.com/guide` (no date signals) | None | low |
+| `news.site.com/2024/05/old-article` | 2024-05-XX | EXCLUDE (too old) |
+| Snippet: "Updated 3 days ago" | calculated | med |
+
+## Risk Analysis
+
+| Risk | Likelihood | Impact | Mitigation |
+|------|------------|--------|------------|
+| Fewer results for niche topics | High | Medium | Explain why in output |
+| User confusion about reduced results | Medium | Low | Clear messaging |
+| Date parsing errors exclude valid content | Low | Medium | Keep items with unknown dates, just label clearly |
+
+## References
+
+### Internal References
+- Reddit search: `scripts/lib/openai_reddit.py:25-63`
+- X search (working example): `scripts/lib/xai_x.py:26-55`
+- Date confidence: `scripts/lib/dates.py:62-90`
+- Scoring penalties: `scripts/lib/score.py:149-153`
+- Normalization: `scripts/lib/normalize.py:49,99`
+
+### External References
+- OpenAI Responses API lacks native date filtering
+- Must rely on prompt engineering + post-processing

scripts/last30days.py

@@ -95,6 +95,8 @@ def run_research(
                     config["OPENAI_API_KEY"],
                     selected_models["openai"],
                     topic,
+                    from_date,
+                    to_date,
                     depth=depth,
                 )
             except http.HTTPError as e:
@@ -319,9 +321,14 @@ def main():
     normalized_reddit = normalize.normalize_reddit_items(reddit_items, from_date, to_date)
     normalized_x = normalize.normalize_x_items(x_items, from_date, to_date)
 
+    # Hard date filter: exclude items with verified dates outside the range
+    # This is the safety net - even if prompts let old content through, this filters it
+    filtered_reddit = normalize.filter_by_date_range(normalized_reddit, from_date, to_date)
+    filtered_x = normalize.filter_by_date_range(normalized_x, from_date, to_date)
+
     # Score items
-    scored_reddit = score.score_reddit_items(normalized_reddit)
+    scored_reddit = score.score_reddit_items(filtered_reddit)
-    scored_x = score.score_x_items(normalized_x)
+    scored_x = score.score_x_items(filtered_x)
 
     # Sort items
     sorted_reddit = score.sort_items(scored_reddit)

scripts/lib/normalize.py

@@ -1,9 +1,51 @@
 """Normalization of raw API data to canonical schema."""
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, TypeVar, Union
 
 from . import dates, schema
 
+T = TypeVar("T", schema.RedditItem, schema.XItem, schema.WebSearchItem)
+
+
+def filter_by_date_range(
+    items: List[T],
+    from_date: str,
+    to_date: str,
+    require_date: bool = False,
+) -> List[T]:
+    """Hard filter: Remove items outside the date range.
+
+    This is the safety net - even if the prompt lets old content through,
+    this filter will exclude it.
+
+    Args:
+        items: List of items to filter
+        from_date: Start date (YYYY-MM-DD) - exclude items before this
+        to_date: End date (YYYY-MM-DD) - exclude items after this
+        require_date: If True, also remove items with no date
+
+    Returns:
+        Filtered list with only items in range (or unknown dates if not required)
+    """
+    result = []
+    for item in items:
+        if item.date is None:
+            if not require_date:
+                result.append(item)  # Keep unknown dates (with scoring penalty)
+            continue
+
+        # Hard filter: if date is before from_date, exclude
+        if item.date < from_date:
+            continue  # DROP - too old
+
+        # Hard filter: if date is after to_date, exclude (likely parsing error)
+        if item.date > to_date:
+            continue  # DROP - future date
+
+        result.append(item)
+
+    return result
+
 
 def normalize_reddit_items(
     items: List[Dict[str, Any]],

scripts/lib/openai_reddit.py

@@ -24,21 +24,27 @@ DEPTH_CONFIG = {
 
 REDDIT_SEARCH_PROMPT = """Search Reddit for DISCUSSION THREADS about: {topic}
 
+DATE RANGE: Only include threads from {from_date} to {to_date} (last 30 days).
+
 SEARCH GUIDANCE:
 - Search for "site:reddit.com/r/ {topic}" to find subreddit discussions
-- Look in subreddits like r/design, r/UI_Design, r/iOSProgramming, r/SwiftUI, r/Figma, r/webdev, r/userexperience, r/graphic_design
+- Look in subreddits like r/design, r/UI_Design, r/iOSProgramming, r/SwiftUI, r/Figma, r/webdev, r/userexperience, r/graphic_design, r/ClaudeAI, r/ClaudeCode
 - ONLY include URLs containing "/r/" and "/comments/" (actual discussion threads)
 - IGNORE: developers.reddit.com, business.reddit.com, reddit.com/user/
 
-Find {min_items}-{max_items} relevant Reddit discussion threads. Prefer recent threads, but include older relevant ones if recent ones are scarce.
+CRITICAL DATE REQUIREMENT:
+- ONLY include threads posted AFTER {from_date}
+- Do NOT include threads older than {from_date}, even if they seem relevant
+- If you cannot find enough recent threads, return FEWER results rather than older ones
+- It is better to return 3 recent threads than 15 old ones
 
-CRITICAL: Return ALL discussion threads you find as JSON. Do NOT return errors or empty results.
+Find {min_items}-{max_items} relevant Reddit discussion threads from the last 30 days.
 
 For EACH Reddit thread URL (containing /r/subreddit/comments/), extract:
 - Thread title
 - Full Reddit URL
 - Subreddit name
-- Date (if visible, otherwise null)
+- Date (MUST be after {from_date}, otherwise do not include)
 - Why it's relevant
 
 Return ONLY valid JSON:
@@ -57,16 +63,18 @@ Return ONLY valid JSON:
 
 Rules:
 - ONLY URLs matching: reddit.com/r/*/comments/*
-- MUST return threads found - NEVER return empty items or errors
+- ONLY threads from {from_date} to {to_date}
-- If threads are older than 30 days, still include them with accurate dates
 - relevance: 0.0-1.0
-- Diverse subreddits preferred"""
+- Diverse subreddits preferred
+- Fewer recent results is better than many old results"""
 
 
 def search_reddit(
     api_key: str,
     model: str,
     topic: str,
+    from_date: str,
+    to_date: str,
     depth: str = "default",
     mock_response: Optional[Dict] = None,
 ) -> Dict[str, Any]:
@@ -76,6 +84,8 @@ def search_reddit(
         api_key: OpenAI API key
         model: Model to use
         topic: Search topic
+        from_date: Start date (YYYY-MM-DD) - only include threads after this
+        to_date: End date (YYYY-MM-DD) - only include threads before this
         depth: Research depth - "quick", "default", or "deep"
         mock_response: Mock response for testing
 
@@ -106,7 +116,13 @@ def search_reddit(
             }
         ],
         "include": ["web_search_call.action.sources"],
-        "input": REDDIT_SEARCH_PROMPT.format(topic=topic, min_items=min_items, max_items=max_items),
+        "input": REDDIT_SEARCH_PROMPT.format(
+            topic=topic,
+            from_date=from_date,
+            to_date=to_date,
+            min_items=min_items,
+            max_items=max_items,
+        ),
     }
 
     return http.post(OPENAI_RESPONSES_URL, payload, headers=headers, timeout=timeout)

scripts/lib/score.py

@@ -15,6 +15,10 @@ WEBSEARCH_WEIGHT_RELEVANCE = 0.55
 WEBSEARCH_WEIGHT_RECENCY = 0.45
 WEBSEARCH_SOURCE_PENALTY = 15  # Points deducted for lacking engagement
 
+# WebSearch date confidence adjustments
+WEBSEARCH_VERIFIED_BONUS = 10   # Bonus for URL-verified recent date (high confidence)
+WEBSEARCH_NO_DATE_PENALTY = 20  # Heavy penalty for no date signals (low confidence)
+
 # Default engagement score for unknown
 DEFAULT_ENGAGEMENT = 35
 UNKNOWN_ENGAGEMENT_PENALTY = 10
@@ -223,6 +227,11 @@ def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebS
     Uses reweighted formula: 55% relevance + 45% recency - 15pt source penalty.
     This ensures WebSearch items rank below comparable Reddit/X items.
 
+    Date confidence adjustments:
+    - High confidence (URL-verified date): +10 bonus
+    - Med confidence (snippet-extracted date): no change
+    - Low confidence (no date signals): -20 penalty
+
     Args:
         items: List of WebSearch items
 
@@ -255,11 +264,14 @@ def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebS
         # Apply source penalty (WebSearch < Reddit/X for same relevance/recency)
         overall -= WEBSEARCH_SOURCE_PENALTY
 
-        # Apply penalty for low date confidence
+        # Apply date confidence adjustments
-        if item.date_confidence == "low":
+        # High confidence (URL-verified): reward with bonus
-            overall -= 10
+        # Med confidence (snippet-extracted): neutral
-        elif item.date_confidence == "med":
+        # Low confidence (no date signals): heavy penalty
-            overall -= 5
+        if item.date_confidence == "high":
+            overall += WEBSEARCH_VERIFIED_BONUS  # Reward verified recent dates
+        elif item.date_confidence == "low":
+            overall -= WEBSEARCH_NO_DATE_PENALTY  # Heavy penalty for unknown
 
         item.score = max(0, min(100, int(overall)))
 

scripts/lib/websearch.py

@@ -11,12 +11,196 @@ The typical flow is:
 """
 
 import re
-from typing import Any, Dict, List, Optional
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional, Tuple
 from urllib.parse import urlparse
 
 from . import schema
 
 
+# Month name mappings for date parsing
+MONTH_MAP = {
+    "jan": 1, "january": 1,
+    "feb": 2, "february": 2,
+    "mar": 3, "march": 3,
+    "apr": 4, "april": 4,
+    "may": 5,
+    "jun": 6, "june": 6,
+    "jul": 7, "july": 7,
+    "aug": 8, "august": 8,
+    "sep": 9, "sept": 9, "september": 9,
+    "oct": 10, "october": 10,
+    "nov": 11, "november": 11,
+    "dec": 12, "december": 12,
+}
+
+
+def extract_date_from_url(url: str) -> Optional[str]:
+    """Try to extract a date from URL path.
+
+    Many sites embed dates in URLs like:
+    - /2026/01/24/article-title
+    - /2026-01-24/article
+    - /blog/20260124/title
+
+    Args:
+        url: URL to parse
+
+    Returns:
+        Date string in YYYY-MM-DD format, or None
+    """
+    # Pattern 1: /YYYY/MM/DD/ (most common)
+    match = re.search(r'/(\d{4})/(\d{2})/(\d{2})/', url)
+    if match:
+        year, month, day = match.groups()
+        if 2020 <= int(year) <= 2030 and 1 <= int(month) <= 12 and 1 <= int(day) <= 31:
+            return f"{year}-{month}-{day}"
+
+    # Pattern 2: /YYYY-MM-DD/ or /YYYY-MM-DD-
+    match = re.search(r'/(\d{4})-(\d{2})-(\d{2})[-/]', url)
+    if match:
+        year, month, day = match.groups()
+        if 2020 <= int(year) <= 2030 and 1 <= int(month) <= 12 and 1 <= int(day) <= 31:
+            return f"{year}-{month}-{day}"
+
+    # Pattern 3: /YYYYMMDD/ (compact)
+    match = re.search(r'/(\d{4})(\d{2})(\d{2})/', url)
+    if match:
+        year, month, day = match.groups()
+        if 2020 <= int(year) <= 2030 and 1 <= int(month) <= 12 and 1 <= int(day) <= 31:
+            return f"{year}-{month}-{day}"
+
+    return None
+
+
+def extract_date_from_snippet(text: str) -> Optional[str]:
+    """Try to extract a date from text snippet or title.
+
+    Looks for patterns like:
+    - January 24, 2026 or Jan 24, 2026
+    - 24 January 2026
+    - 2026-01-24
+    - "3 days ago", "yesterday", "last week"
+
+    Args:
+        text: Text to parse
+
+    Returns:
+        Date string in YYYY-MM-DD format, or None
+    """
+    if not text:
+        return None
+
+    text_lower = text.lower()
+
+    # Pattern 1: Month DD, YYYY (e.g., "January 24, 2026")
+    match = re.search(
+        r'\b(jan(?:uary)?|feb(?:ruary)?|mar(?:ch)?|apr(?:il)?|may|jun(?:e)?|'
+        r'jul(?:y)?|aug(?:ust)?|sep(?:t(?:ember)?)?|oct(?:ober)?|nov(?:ember)?|dec(?:ember)?)'
+        r'\s+(\d{1,2})(?:st|nd|rd|th)?,?\s*(\d{4})\b',
+        text_lower
+    )
+    if match:
+        month_str, day, year = match.groups()
+        month = MONTH_MAP.get(month_str[:3])
+        if month and 2020 <= int(year) <= 2030 and 1 <= int(day) <= 31:
+            return f"{year}-{month:02d}-{int(day):02d}"
+
+    # Pattern 2: DD Month YYYY (e.g., "24 January 2026")
+    match = re.search(
+        r'\b(\d{1,2})(?:st|nd|rd|th)?\s+'
+        r'(jan(?:uary)?|feb(?:ruary)?|mar(?:ch)?|apr(?:il)?|may|jun(?:e)?|'
+        r'jul(?:y)?|aug(?:ust)?|sep(?:t(?:ember)?)?|oct(?:ober)?|nov(?:ember)?|dec(?:ember)?)'
+        r'\s+(\d{4})\b',
+        text_lower
+    )
+    if match:
+        day, month_str, year = match.groups()
+        month = MONTH_MAP.get(month_str[:3])
+        if month and 2020 <= int(year) <= 2030 and 1 <= int(day) <= 31:
+            return f"{year}-{month:02d}-{int(day):02d}"
+
+    # Pattern 3: YYYY-MM-DD (ISO format)
+    match = re.search(r'\b(\d{4})-(\d{2})-(\d{2})\b', text)
+    if match:
+        year, month, day = match.groups()
+        if 2020 <= int(year) <= 2030 and 1 <= int(month) <= 12 and 1 <= int(day) <= 31:
+            return f"{year}-{month}-{day}"
+
+    # Pattern 4: Relative dates ("3 days ago", "yesterday", etc.)
+    today = datetime.now()
+
+    if "yesterday" in text_lower:
+        date = today - timedelta(days=1)
+        return date.strftime("%Y-%m-%d")
+
+    if "today" in text_lower:
+        return today.strftime("%Y-%m-%d")
+
+    # "N days ago"
+    match = re.search(r'\b(\d+)\s*days?\s*ago\b', text_lower)
+    if match:
+        days = int(match.group(1))
+        if days <= 60:  # Reasonable range
+            date = today - timedelta(days=days)
+            return date.strftime("%Y-%m-%d")
+
+    # "N hours ago" -> today
+    match = re.search(r'\b(\d+)\s*hours?\s*ago\b', text_lower)
+    if match:
+        return today.strftime("%Y-%m-%d")
+
+    # "last week" -> ~7 days ago
+    if "last week" in text_lower:
+        date = today - timedelta(days=7)
+        return date.strftime("%Y-%m-%d")
+
+    # "this week" -> ~3 days ago (middle of week)
+    if "this week" in text_lower:
+        date = today - timedelta(days=3)
+        return date.strftime("%Y-%m-%d")
+
+    return None
+
+
+def extract_date_signals(
+    url: str,
+    snippet: str,
+    title: str,
+) -> Tuple[Optional[str], str]:
+    """Extract date from any available signal.
+
+    Tries URL first (most reliable), then snippet, then title.
+
+    Args:
+        url: Page URL
+        snippet: Page snippet/description
+        title: Page title
+
+    Returns:
+        Tuple of (date_string, confidence)
+        - date from URL: 'high' confidence
+        - date from snippet/title: 'med' confidence
+        - no date found: None, 'low' confidence
+    """
+    # Try URL first (most reliable)
+    url_date = extract_date_from_url(url)
+    if url_date:
+        return url_date, "high"
+
+    # Try snippet
+    snippet_date = extract_date_from_snippet(snippet)
+    if snippet_date:
+        return snippet_date, "med"
+
+    # Try title
+    title_date = extract_date_from_snippet(title)
+    if title_date:
+        return title_date, "med"
+
+    return None, "low"
+
+
 # Domains to exclude (Reddit and X are handled separately)
 EXCLUDED_DOMAINS = {
     "reddit.com",
@@ -70,15 +254,25 @@ def is_excluded_domain(url: str) -> bool:
 def parse_websearch_results(
     results: List[Dict[str, Any]],
     topic: str,
+    from_date: str = "",
+    to_date: str = "",
 ) -> List[Dict[str, Any]]:
     """Parse WebSearch results into normalized format.
 
     This function expects results from Claude's WebSearch tool.
     Each result should have: title, url, snippet, and optionally date/relevance.
 
+    Uses "Date Detective" approach:
+    1. Extract dates from URLs (high confidence)
+    2. Extract dates from snippets/titles (med confidence)
+    3. Hard filter: exclude items with verified old dates
+    4. Keep items with no date signals (with low confidence penalty)
+
     Args:
         results: List of WebSearch result dicts
         topic: Original search topic (for context)
+        from_date: Start date for filtering (YYYY-MM-DD)
+        to_date: End date for filtering (YYYY-MM-DD)
 
     Returns:
         List of normalized item dicts ready for WebSearchItem creation
@@ -103,15 +297,27 @@ def parse_websearch_results(
         if not title and not snippet:
             continue
 
-        # Parse date if provided
+        # Use Date Detective to extract date signals
-        date = result.get("date")
+        date = result.get("date")  # Use provided date if available
         date_confidence = "low"
-        if date:
+
-            # Validate date format
+        if date and re.match(r'^\d{4}-\d{2}-\d{2}$', str(date)):
-            if re.match(r'^\d{4}-\d{2}-\d{2}$', str(date)):
+            # Provided date is valid
-                date_confidence = "med"  # WebSearch dates are often approximate
+            date_confidence = "med"
-            else:
+        else:
-                date = None
+            # Try to extract date from URL/snippet/title
+            extracted_date, confidence = extract_date_signals(url, snippet, title)
+            if extracted_date:
+                date = extracted_date
+                date_confidence = confidence
+
+        # Hard filter: if we found a date and it's too old, skip
+        if date and from_date and date < from_date:
+            continue  # DROP - verified old content
+
+        # Hard filter: if date is in the future, skip (parsing error)
+        if date and to_date and date > to_date:
+            continue  # DROP - future date
 
         # Get relevance if provided, default to 0.5
         relevance = result.get("relevance", 0.5)