feat: add search and likes commands

- Add 'twitter search' command with --type (Top/Latest/Photos/Videos), --max, --json, --filter
- Add 'twitter likes' command to view tweets liked by a user
- Add SearchTimeline and Likes GraphQL operations with fallback queryIds
- Update README with new command examples (EN + CN)

twitter_cli/cli.py

@@ -244,5 +244,81 @@ def user_posts(screen_name, max_count, as_json):
     console.print()
 
 
+SEARCH_PRODUCTS = ["Top", "Latest", "Photos", "Videos"]
+
+
+@cli.command()
+@click.argument("query")
+@click.option(
+    "--type",
+    "-t",
+    "product",
+    type=click.Choice(SEARCH_PRODUCTS, case_sensitive=False),
+    default="Top",
+    help="Search tab: Top, Latest, Photos, or Videos.",
+)
+@click.option("--max", "-n", "max_count", type=int, default=20, help="Max number of tweets to fetch.")
+@click.option("--json", "as_json", is_flag=True, help="Output as JSON.")
+@click.option("--filter", "do_filter", is_flag=True, help="Enable score-based filtering.")
+def search(query, product, max_count, as_json, do_filter):
+    # type: (str, str, int, bool, bool) -> None
+    """Search tweets by QUERY string."""
+    config = load_config()
+    try:
+        fetch_count = _resolve_fetch_count(max_count, 20)
+        client = _get_client(config)
+        console.print("🔍 Searching '%s' (%s, %d tweets)...\n" % (query, product, fetch_count))
+        start = time.time()
+        tweets = client.fetch_search(query, fetch_count, product)
+        elapsed = time.time() - start
+        console.print("✅ Found %d tweets in %.1fs\n" % (len(tweets), elapsed))
+    except RuntimeError as exc:
+        console.print("[red]❌ %s[/red]" % exc)
+        sys.exit(1)
+
+    filtered = _apply_filter(tweets, do_filter, config)
+
+    if as_json:
+        click.echo(tweets_to_json(filtered))
+        return
+
+    print_tweet_table(filtered, console, title="🔍 '%s' — %d tweets" % (query, len(filtered)))
+    console.print()
+
+
+@cli.command()
+@click.argument("screen_name")
+@click.option("--max", "-n", "max_count", type=int, default=20, help="Max number of tweets to fetch.")
+@click.option("--json", "as_json", is_flag=True, help="Output as JSON.")
+@click.option("--filter", "do_filter", is_flag=True, help="Enable score-based filtering.")
+def likes(screen_name, max_count, as_json, do_filter):
+    # type: (str, int, bool, bool) -> None
+    """Show tweets liked by a user. SCREEN_NAME is the @handle (without @)."""
+    screen_name = screen_name.lstrip("@")
+    config = load_config()
+    try:
+        fetch_count = _resolve_fetch_count(max_count, 20)
+        client = _get_client(config)
+        console.print("👤 Fetching @%s's profile..." % screen_name)
+        profile = client.fetch_user(screen_name)
+        console.print("❤️  Fetching likes (%d)...\n" % fetch_count)
+        start = time.time()
+        tweets = client.fetch_user_likes(profile.id, fetch_count)
+        elapsed = time.time() - start
+        console.print("✅ Fetched %d liked tweets in %.1fs\n" % (len(tweets), elapsed))
+    except RuntimeError as exc:
+        console.print("[red]❌ %s[/red]" % exc)
+        sys.exit(1)
+
+    filtered = _apply_filter(tweets, do_filter, config)
+
+    if as_json:
+        click.echo(tweets_to_json(filtered))
+        return
+
+    print_tweet_table(filtered, console, title="❤️ @%s likes — %d tweets" % (screen_name, len(filtered)))
+    console.print()
+
+
 if __name__ == "__main__":
     cli()

twitter_cli/client.py

@@ -28,6 +28,8 @@ FALLBACK_QUERY_IDS = {
     "Bookmarks": "VFdMm9iVZxlU6hD86gfW_A",
     "UserByScreenName": "1VOOyvKkiI3FMmkeDNxM9A",
     "UserTweets": "E3opETHurmVJflFsUBVuUQ",
+    "SearchTimeline": "VhUd6vHVmLBcw0uX-6jMLA",
+    "Likes": "lIDpu_NWL7_VhimGGt0o6A",
 }
 
 TWITTER_OPENAPI_URL = (
@@ -312,6 +314,44 @@ class TwitterClient:
             },
         )
 
+    def fetch_user_likes(self, user_id, count=20):
+        # type: (str, int) -> List[Tweet]
+        """Fetch tweets liked by a user."""
+        return self._fetch_timeline(
+            "Likes",
+            count,
+            lambda data: _deep_get(data, "data", "user", "result", "timeline_v2", "timeline", "instructions"),
+            extra_variables={
+                "userId": user_id,
+                "includePromotedContent": False,
+                "withClientEventToken": False,
+                "withBirdwatchNotes": False,
+                "withVoice": True,
+            },
+        )
+
+    def fetch_search(self, query, count=20, product="Top"):
+        # type: (str, int, str) -> List[Tweet]
+        """Search tweets by query.
+
+        Args:
+            query: Search query string.
+            count: Max number of tweets to return.
+            product: Search tab — "Top", "Latest", "People", "Photos", "Videos".
+        """
+        return self._fetch_timeline(
+            "SearchTimeline",
+            count,
+            lambda data: _deep_get(
+                data, "data", "search_by_raw_query", "search_timeline", "timeline", "instructions",
+            ),
+            extra_variables={
+                "rawQuery": query,
+                "querySource": "typed_query",
+                "product": product,
+            },
+        )
+
     def _fetch_timeline(self, operation_name, count, get_instructions, extra_variables=None):
         # type: (str, int, Callable[[Any], Any], Optional[Dict[str, Any]]) -> List[Tweet]
         """Generic timeline fetcher with pagination and deduplication."""