[Feature] Improve LLM prompt and add sender frequency tracking

* Update default prompt to reduce false positives on legitimate emails
  - Explicitly recognize verification emails as legitimate
  - Require MULTIPLE red flags for phishing classification
  - Add guidance on known/frequent senders
* Add sender frequency detection in context
  - Classify senders as: new, occasional, known, frequent
  - Based on sender_counts from user context
  - Passed to LLM via context snippet
* Prompt instructs LLM to reduce phishing score for known senders
* Helps avoid false positives on transactional/verification emails

lualib/llm_context.lua

@@ -336,12 +336,28 @@ local function join_list(arr)
   return table.concat(arr, ', ')
 end
 
-local function format_context_prompt(ctx)
+local function format_context_prompt(ctx, task)
   local bullets = to_bullets_recent(ctx.recent_messages or {}, 5)
   local top_senders = join_list(ctx.top_senders or {})
   local flagged = join_list(ctx.flagged_phrases or {})
   local spam_types = join_list(ctx.last_spam_labels or {})
 
+  -- Check if current sender is known
+  local sender_frequency = 'new'
+  if task then
+    local from = ((task:get_from('smtp') or EMPTY)[1] or EMPTY)['addr']
+    if from and ctx.sender_counts and ctx.sender_counts[from] then
+      local count = ctx.sender_counts[from]
+      if count >= 10 then
+        sender_frequency = 'frequent'
+      elseif count >= 3 then
+        sender_frequency = 'known'
+      else
+        sender_frequency = 'occasional'
+      end
+    end
+  end
+
   local parts = {}
   table.insert(parts, 'User recent correspondence summary:')
   if bullets ~= '' then
@@ -356,6 +372,7 @@ local function format_context_prompt(ctx)
   if spam_types ~= '' then
     table.insert(parts, string.format('Last detected spam types: %s', spam_types))
   end
+  table.insert(parts, string.format('Current sender: %s', sender_frequency))
 
   return table.concat(parts, '\n')
 end
@@ -409,7 +426,7 @@ function M.fetch(task, redis_params, opts, callback, debug_module)
 
     lua_util.debugm(N, task, 'context warm-up OK: %s messages, generating snippet',
       tostring(msg_count))
-    local prompt_snippet = format_context_prompt(ctx)
+    local prompt_snippet = format_context_prompt(ctx, task)
     callback(nil, ctx, prompt_snippet)
   end
 

src/plugins/lua/gpt.lua

@@ -1239,19 +1239,43 @@ if opts then
 
   if not settings.prompt then
     if settings.extra_symbols then
-      settings.prompt = "Analyze this email strictly as a spam detector given the email message, subject, " ..
-          "FROM and url domains. Evaluate spam probability (0-1). " ..
+      settings.prompt = "Analyze this email as a spam detector. Evaluate spam probability (0-1).\n\n" ..
+          "LEGITIMATE patterns to recognize:\n" ..
+          "- Verification emails with time-limited codes are NORMAL and legitimate\n" ..
+          "- Transactional emails (receipts, confirmations, password resets) from services\n" ..
+          "- 'Verify email' or 'confirmation code' is NOT automatically phishing\n" ..
+          "- Emails from frequent/known senders (see context) are more trustworthy\n\n" ..
+          "Flag as SPAM/PHISHING only with MULTIPLE red flags:\n" ..
+          "- Urgent threats or fear tactics (account closure, legal action)\n" ..
+          "- Domain impersonation or suspicious lookalikes\n" ..
+          "- Requests for passwords, SSN, credit card numbers\n" ..
+          "- Mismatched URLs pointing to different domains than sender\n" ..
+          "- Poor grammar/spelling in supposedly professional emails\n\n" ..
+          "IMPORTANT: If sender is 'frequent' or 'known', reduce phishing probability " ..
+          "unless there are strong contradictory signals.\n\n" ..
           "Output ONLY 3 lines:\n" ..
           "1. Numeric score (0.00-1.00)\n" ..
-          "2. One-sentence reason citing whether it is spam, the strongest red flag, or why it is ham\n" ..
-          "3. Empty line or mention ONLY the primary concern category if found from the list: " ..
+          "2. One-sentence reason citing the strongest indicator\n" ..
+          "3. Primary category if applicable: " ..
           table.concat(lua_util.keys(categories_map), ', ')
     else
-      settings.prompt = "Analyze this email strictly as a spam detector given the email message, subject, " ..
-          "FROM and url domains. Evaluate spam probability (0-1). " ..
+      settings.prompt = "Analyze this email as a spam detector. Evaluate spam probability (0-1).\n\n" ..
+          "LEGITIMATE patterns to recognize:\n" ..
+          "- Verification emails with time-limited codes are NORMAL and legitimate\n" ..
+          "- Transactional emails (receipts, confirmations, password resets) from services\n" ..
+          "- 'Verify email' or 'confirmation code' is NOT automatically phishing\n" ..
+          "- Emails from frequent/known senders (see context) are more trustworthy\n\n" ..
+          "Flag as SPAM/PHISHING only with MULTIPLE red flags:\n" ..
+          "- Urgent threats or fear tactics (account closure, legal action)\n" ..
+          "- Domain impersonation or suspicious lookalikes\n" ..
+          "- Requests for passwords, SSN, credit card numbers\n" ..
+          "- Mismatched URLs pointing to different domains than sender\n" ..
+          "- Poor grammar/spelling in supposedly professional emails\n\n" ..
+          "IMPORTANT: If sender is 'frequent' or 'known', reduce phishing probability " ..
+          "unless there are strong contradictory signals.\n\n" ..
           "Output ONLY 2 lines:\n" ..
           "1. Numeric score (0.00-1.00)\n" ..
-          "2. One-sentence reason citing whether it is spam, the strongest red flag, or why it is ham\n"
+          "2. One-sentence reason citing the strongest indicator"
     end
   end